Method for monitoring operation efficiency of mechanical equipment along river in combination with Internet of Things

Abstract

The invention relates to the technical field of industrial Internet of Things and equipment energy efficiency monitoring, in particular to an Internet of Things-combined riverside mechanical equipment operation efficiency monitoring method, which comprises the following steps of: acquiring operation electrical parameters and environment parameters of equipment through an Internet of Things sensing node cluster, and calculating real-time operation power; constructing an environment coupling coefficient based on the operation water level and the inclination angle, and correcting the power to obtain equivalent net power; and further calculating a comprehensive energy efficiency ratio, judging an operation efficiency level in combination with a preset efficiency threshold interval, and outputting a corresponding early warning signal. According to the method, the water level and attitude interference in the working environment along the river is fully considered, power correction and efficiency grade intelligent judgment can be realized, and the method has relatively high environmental adaptability and operation and maintenance response capability and is suitable for energy efficiency evaluation and operation state monitoring of various engineering mechanical equipment.

Description

Technical Field

[0001] This invention relates to the field of industrial Internet of Things (IoT) and equipment energy efficiency monitoring technology, and in particular to a method for monitoring the operating efficiency of riverside machinery equipment by combining IoT. Background Technology

[0002] For construction machinery operating in complex environments such as along rivers, including excavators, grab buckets, and cranes, operational stability and energy efficiency directly impact operational safety, operating costs, and resource allocation efficiency. With the widespread adoption of the Internet of Things (IoT) and sensor technologies, the ability to collect data on equipment operating status has significantly improved, providing a technological foundation for data-driven operational efficiency monitoring and refined management.

[0003] In existing technologies, some solutions have attempted to estimate equipment power by collecting voltage and current data, and then conduct energy efficiency assessments. However, they generally suffer from three technical bottlenecks: First, they do not consider the impact of external environmental variables such as water level in the operating area and equipment tilt on power output, resulting in efficiency assessment results that deviate from reality. Second, the power correction methods lack a clear modeling mechanism and dynamic coupling calculation process, making it impossible to quantitatively eliminate environmental factors. Third, the assessment results rely heavily on manual interpretation, lacking grading standards and response mechanisms, and failing to form an automated, intelligent, and efficient operation and maintenance process. Summary of the Invention

[0004] This invention provides a method for monitoring the operating efficiency of riverside machinery and equipment by combining the Internet of Things (IoT). The method integrates multi-source sensing, environmental correction, and intelligent judgment to improve the accuracy of assessment, the timeliness of response, and the closed-loop control capability of the system.

[0005] A method for monitoring the operational efficiency of riverside machinery and equipment using the Internet of Things (IoT) includes the following steps: S1: Real-time collection of the target mechanical equipment's operating electrical parameters and its operating area's environmental parameters via an IoT sensor node cluster; the operating electrical parameters include operating voltage and operating current; the environmental parameters include the equipment's current operating water level and the equipment's tilt angle; S2: The real-time operating power of the device is calculated based on the operating voltage and operating current; S3: Based on the current operating water level and tilt angle of the equipment, the environmental coupling coefficient used to correct the theoretical work is calculated; S4: Correct the real-time operating power based on the environmental coupling coefficient to obtain the equivalent net power after removing environmental interference; S5: Based on the equivalent net power, calculate the comprehensive energy efficiency ratio to characterize the equipment's operating efficiency; S6: Compare the overall energy efficiency ratio with the preset efficiency threshold range, and output the current operating efficiency level and warning signal of the device.

[0006] Optionally, S1 includes: S11: Configure the working parameters of the IoT sensor node cluster deployed on the key nodes of the target mechanical equipment. The working parameters include the sampling frequency and communication protocol, which establish the foundation for data acquisition and transmission. S12: Using the electrical parameter sensing unit in the IoT sensor node cluster with configured operating parameters, the operating voltage and operating current of the target mechanical equipment are synchronously collected at the set sampling frequency to obtain the original operating electrical parameter data stream. S13: Using the environmental parameter sensing unit in the IoT sensor node cluster with configured working parameters, synchronously collect the current operating water level and equipment tilt angle to obtain the raw environmental parameter data stream; S14: The original operating electrical parameter data stream and the original environmental parameter data stream are encapsulated according to a unified timing reference to form a comprehensive monitoring data frame containing operating voltage, operating current, current operating water level of the equipment and tilt angle of the equipment, and transmitted to the data aggregation node through the Internet of Things network; S15: The data aggregation node parses and preprocesses the received integrated monitoring data frame, separates and outputs the operating voltage and operating current for use in S2, and the current operating water level and tilt angle of the equipment for use in S3.

[0007] Optionally, the preprocessing includes: removing outliers from the operating voltage and operating current data, and performing moving average filtering on the device tilt angle.

[0008] Optionally, S2 includes: S21: Receive the timing data of the operating voltage and the operating current from S15, and perform data preprocessing on the timing data of the operating voltage and the operating current respectively to eliminate abnormal fluctuations, and obtain the preprocessed operating voltage data and the preprocessed operating current data. S22: Multiply the values ​​of the preprocessed operating voltage data and the preprocessed operating current data at the same time to calculate the instantaneous power at that time and generate instantaneous power time series data. S23: Using a complete equipment working cycle as a time window, perform integral calculation on the instantaneous power time sequence data and calculate the average value to obtain the average power within the time window, and use this average power as the real-time operating power.

[0009] Optionally, S3 includes: S31: Receive the timing data of the current operating water level and tilt angle of the equipment from S15, and perform data calibration and synchronization processing on the timing data of the current operating water level and tilt angle of the equipment to obtain the calibrated current operating water level data and calibrated tilt angle data of the equipment. S32: Input the calibrated current operating water level data of the equipment into a preset water level-resistance mapping function to calculate the water level influence coefficient; at the same time, input the calibrated tilt angle data of the equipment into a preset tilt angle-efficiency mapping function to calculate the tilt angle influence coefficient; S33: Substitute the water level influence coefficient and the tilt angle influence coefficient into the environmental coupling coefficient synthesis formula for weighted fusion calculation, and output the final environmental coupling coefficient used to correct the theoretical work.

[0010] Optionally, the data calibration includes: correcting the time-series data of the current operating water level of the equipment using a temperature compensation algorithm, and performing zero-point drift compensation on the time-series data of the tilt angle of the equipment.

[0011] Optionally, S4 includes: S41: Receive the timing data of the real-time operating power and the timing data of the environmental coupling coefficient, and perform timestamp alignment and validity verification on the timing data of the real-time operating power and the timing data of the environmental coupling coefficient to ensure that the two are strictly synchronized in time and the data is valid, so as to obtain synchronized real-time operating power data and synchronized environmental coupling coefficient data. S42: Divide the synchronized real-time operating power data with the synchronized environmental coupling coefficient data at the same time to calculate the preliminary equivalent net power after environmental correction at that time, and generate preliminary equivalent net power time series data. S43: Perform smoothing filtering on the preliminary equivalent net power time series data to eliminate high-frequency noise introduced during the calculation process and output the final smooth and stable equivalent net power.

[0012] Optionally, S5 includes: S51: Receive the time-series data of the equivalent net power, and perform unit conversion and dimension unification processing on the time-series data of the equivalent net power to obtain standardized equivalent net power data. S52: Within a complete statistical period, the standardized equivalent net power data is integrated to calculate the effective power output of the device within that period, i.e., the actual equivalent net power completed. S53: Obtain the rated input energy of the equipment within the statistical period according to the equipment model, and calculate the ratio of the equivalent net power to the rated input energy to obtain the final comprehensive energy efficiency ratio used to characterize the operating efficiency of the equipment.

[0013] Optionally, S6 includes: S61: Receive the comprehensive energy efficiency ratio and query the preset database to obtain a preset efficiency threshold range that matches the current equipment model and operating conditions. The preset efficiency threshold range includes a high-efficiency range, a normal range, and a low-efficiency range. S62: The overall energy efficiency ratio is compared with the preset efficiency threshold range in real time, and the current operating efficiency level of the equipment is determined according to the comparison result. If the overall energy efficiency ratio is in the high efficiency range, the operating efficiency level is "high efficiency"; if it is in the normal range, the operating efficiency level is "normal"; if it is in the low efficiency range, the operating efficiency level is "low efficiency". S63: Generate a corresponding warning signal based on the determined operating efficiency level. If the operating efficiency level is "high efficiency", a green safety signal is generated; if it is "normal", a yellow warning signal is generated; if it is "inefficient", a red alarm signal is generated. The final operating efficiency level and warning signal are then output to the human-machine interface for display.

[0014] The beneficial effects of this invention are: This invention constructs a dual-channel acquisition system for "operating electrical parameters + environmental parameters" and introduces an environmental coupling coefficient modeling and real-time correction mechanism to effectively eliminate the interference of external environmental factors such as water level fluctuations and equipment attitude changes on power measurement. Filtering and time alignment processing are employed in the equivalent net power calculation to ensure that the final power value is closer to the actual output of the equipment, thereby significantly improving the accuracy and objectivity of the subsequent comprehensive energy efficiency ratio and providing a scientific basis for evaluating equipment operating efficiency.

[0015] This invention uses a fixed time window or standard operating cycle as the evaluation period and combines it with the Simpson integral method to perform high-precision statistical analysis of the energy efficiency output of equipment within the cycle, constructing a database of efficiency threshold intervals that dynamically matches equipment model and operating status. Compared with traditional methods that judge efficiency based on instantaneous parameters, this invention has stronger repeatability and comparability in evaluating unit energy consumption output, and is suitable for standardized and institutionalized energy efficiency management and hierarchical scheduling of large-scale equipment.

[0016] This invention establishes a closed-loop monitoring and control chain from data acquisition and analysis to response by enabling rapid local determination of efficiency levels at edge computing nodes, alerting on-site personnel to inefficient operating conditions via audible and visual alarms, and triggering automated work order creation on a remote platform using digital command signals. This mechanism significantly improves fault identification and maintenance response speed, reduces the frequency of human intervention, and possesses significant potential for engineering deployment feasibility and management efficiency improvement. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the S1 process in an embodiment of the present invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0020] like Figures 1-2 As shown, a method for monitoring the operating efficiency of riverside machinery and equipment using the Internet of Things (IoT) includes the following steps: S1: Through an IoT sensor node cluster, the operating electrical parameters of the target mechanical equipment and the environmental parameters of its working area are collected in real time; the operating electrical parameters include the operating voltage and operating current; the environmental parameters include the current water level of the equipment and the equipment tilt angle, specifically: S11: Configure the operating parameters of the IoT sensor node cluster deployed at key nodes of the target machinery. Specific operations include: issuing configuration commands to each IoT sensor node via a remote control platform, setting its sampling frequency to a fixed value between 0.1kHz and 10kHz, and specifying its communication protocol as either LoRaWAN or NB-IoT. The sampling frequency is selected based on the response characteristics and data change cycle of the target machinery to achieve efficient data coverage and reasonable power consumption control. The communication protocol selection is based on signal coverage and network bandwidth limitations in the operating environment to ensure that the IoT sensor node cluster can stably and continuously communicate with the data aggregation node.

[0021] S12: Utilizing the electrical parameter sensing units in the IoT sensor node cluster with pre-configured operating parameters, the operating voltage and current of the target mechanical equipment are synchronously acquired at a set sampling frequency. The electrical parameter sensing units employ Hall effect sensors, installed on the outside of the equipment's power supply cable, enabling non-contact current detection. Combined with a matching voltage sampling module, this forms a complete electrical parameter acquisition channel. All acquisition processes are coordinated and controlled by a local microcontroller, which continuously outputs raw operating electrical parameter data streams according to timestamps. This data stream is continuously generated at millisecond-level sampling intervals.

[0022] S13: Utilizing environmental parameter sensing units within a cluster of IoT sensor nodes configured with operating parameters, the system synchronously collects the current operating water level and equipment tilt angle. The environmental parameter sensing units include a radar level gauge for collecting the current operating water level and a MEMS tilt sensor for collecting the equipment tilt angle. The radar level gauge is installed on a support above the equipment's operating area, using high-frequency radar pulses to achieve non-contact ranging from the water surface to the sensor. The MEMS tilt sensor is installed on a rigid part of the equipment structure, providing dual-axis attitude angle data. The continuously collected measurement results form a raw environmental parameter data stream at a set frequency, which is then buffered and managed uniformly by the edge processing unit.

[0023] S14: The original operating electrical parameter data stream and the original environmental parameter data stream are paired and integrated according to a unified time base to form a structurally standardized comprehensive monitoring data frame. During integration, a timestamp alignment algorithm is first used to combine the sampled data from the two data streams with the closest timestamps into one frame, ensuring that all parameters in the data frame have the same sampling time. The comprehensive monitoring data frame includes operating voltage, operating current, current water level, and equipment tilt angle. To optimize network transmission efficiency and ensure data security, the comprehensive monitoring data frame is compressed using a lossless compression algorithm before encapsulation, and then the compressed data content is encrypted using the AES encryption algorithm to form the final transmission payload.

[0024] S15: The compressed and encrypted integrated monitoring data frame is sent to the designated data aggregation node via the configured IoT communication protocol. Upon receiving the data, the data aggregation node first performs AES decryption and lossless decompression to restore the original data frame structure. Then, it performs parsing and preprocessing operations on the data frame, specifically including: outlier removal for the operating voltage and current data, with the removal standard being outliers exceeding the historical statistical range by ±3 standard deviations; and applying moving average filtering to the equipment tilt angle data, using a data segment with a window length of 5 for weighted smoothing to remove measurement noise. Finally, the operating voltage and current for use by S2, and the current operating water level and equipment tilt angle for use by S3 are output respectively.

[0025] S2: Based on the operating voltage and operating current, the real-time operating power of the device is calculated as follows: S21: Receive the timing data of the operating voltage and operating current output from step S15, and perform data preprocessing operations on both to eliminate any transient abnormal fluctuations that may exist in the original sampling. The data preprocessing uses a median filtering algorithm, specifically: traverse the entire timing data of the operating voltage and operating current in a sliding window manner, with a window length set to 5 sampling points. Calculate the median value within each window and replace the data point at the center of the window with this median value, thereby eliminating sudden spike interference or pulse-like jumps, and outputting the preprocessed operating voltage and operating current data. Median filtering is completed by the filtering submodule of the data processing module, and timestamps are retained during processing to ensure consistency in subsequent operation timing.

[0026] S22: The preprocessed operating voltage and current data are multiplied point-by-point at the same time to obtain the instantaneous power of the target mechanical equipment at each sampling moment. "Same time" in this step refers to data pairs with completely identical timestamps in the two data sequences. The multiplication operation is performed by a high-speed digital multiplier in the digital processing unit, employing a pipelined structure to achieve real-time multiplication at the sampling level, ensuring no processing delay is introduced. The output of each multiplication operation is the instantaneous power at that sampling moment, which is then arranged in chronological order to form instantaneous power time-series data. This data is used for subsequent window integration analysis.

[0027] S23: Using a complete work cycle of the target machinery as a time window, perform integration and averaging calculations on the instantaneous power time-series data. A complete work cycle refers to the complete operation cycle of the equipment completing one digging, lifting, rotating, unloading, and returning to its original position. The start and end times of this time period are determined by the equipment operation status identification module through detecting changes in the equipment control signals. The integration calculation uses the trapezoidal rule numerical integration formula to perform a weighted summation of all instantaneous power values ​​within this time window. The specific calculation process is as follows: ; in, and The instantaneous power values ​​of two adjacent sampling points. Where is the time interval between adjacent sampling points, T is the duration of the entire equipment operating cycle, and N is the total number of sampling points. The final output integral average result is the average power within this operating cycle, which is defined as the real-time operating power and used in subsequent efficiency evaluation steps.

[0028] S3: Based on the current operating water level and the equipment tilt angle, the environmental coupling coefficient used to correct the theoretical work is calculated, specifically as follows: S31: Receive the timing data of the current operating water level and equipment tilt angle output in step S15, and perform data calibration and synchronization processing on this timing data to ensure data accuracy and timing consistency. Data calibration includes the following two operations: First, a temperature compensation algorithm is applied to the current operating water level data of the equipment. This algorithm obtains the real-time temperature parameters of the sensor's environment and calculates the error impact of temperature on the liquid level measurement based on the sensor's factory calibration curve. Then, it corrects the liquid level data at each moment to obtain the calibrated current operating water level data of the equipment. Secondly, a zero-point drift compensation operation is performed on the device tilt angle data. This compensation algorithm is based on the tilt angle reference value collected when the device is in its static initial state, and performs point-by-point offset correction on subsequent sampled data, thereby eliminating the drift error caused by the MEMS tilt sensor due to long-term operation, and obtaining the calibrated device tilt angle data.

[0029] To ensure synchronization, the two parameter data after calibration are rearranged using a timestamp alignment mechanism and output with a unified time base for subsequent modeling.

[0030] S32: Input the calibrated current operating water level data of the equipment into the preset water level-resistance mapping function to obtain the water level influence coefficient reflecting the impact of liquid level changes on equipment load. The water level-resistance mapping function is a quadratic polynomial function constructed based on fluid mechanics principles, and its expression is as follows: ; Where h represents the calibrated current operating water level data of the equipment. These are coefficients obtained through experimental calibration based on equipment type and operating environment. The calculated result is the water level influence coefficient, used to describe the coupling effect of water depth on equipment operating resistance.

[0031] Simultaneously, the calibrated equipment tilt angle data is input into a preset tilt angle-efficiency mapping function to obtain the tilt angle influence coefficient, which reflects the impact of tilt posture on operating efficiency. The tilt angle-efficiency mapping function is a piecewise linear function constructed based on the characteristics of the equipment's hydraulic system, and its expression is as follows: ; in, For the calibrated equipment tilt angle data, The slope and intercept of the segmented intervals are given. The tilt angle is the critical value, and all parameters were determined experimentally based on the hydraulic drive characteristics. The output calculation result is the tilt angle influence coefficient.

[0032] S33: Substitute the water level influence coefficient and the tilt angle influence coefficient into the environmental coupling coefficient synthesis formula, perform weighted fusion calculation, and obtain the final environmental coupling coefficient used to correct the theoretical work. The specific synthesis formula is as follows: Environmental coupling coefficient = (water level influence coefficient) Inclination influence coefficient ; in, and This is a pre-calibrated weighted index based on equipment model, operation type, and usage conditions, used to adjust the weighting of different environmental parameters on equipment energy consumption. The formula is executed in real-time by an embedded algorithm module on an edge computing node, and the final output environmental coupling coefficient is used in subsequent steps to correct real-time operating power, eliminating interference from environmental factors in energy efficiency assessment.

[0033] S4: Correct the real-time operating power based on the environmental coupling coefficient to obtain the equivalent net power after removing environmental interference, specifically: S41: Receive the time-series data of the real-time operating power and the environmental coupling coefficient calculated in the aforementioned steps, and perform timestamp alignment and validity verification on the two to obtain synchronous data input that is strictly corresponding in time scale and valid in numerical value.

[0034] During the timestamp alignment process, linear interpolation is used to uniformly map the time series data of the environmental coupling coefficient to the sampling time series that is completely consistent with the time series data of the real-time operating power, ensuring that the two form a one-to-one correspondence at every point in time.

[0035] During the validity verification process, the following judgment operation is performed on the data pair at each time point: if the real-time operating power value is less than or equal to zero, or the environmental coupling coefficient value exceeds the pre-set physical reasonable range. If the data at that moment is invalid, the data from the previous valid moment is used as the replacement value for the current moment to ensure data continuity and physical reliability. Finally, the synchronized real-time operating power data and synchronized environmental coupling coefficient data are output as input for the next division operation.

[0036] S42: Perform point-by-point division on the synchronized real-time operating power data and synchronized environmental coupling coefficient data obtained in the above steps at each same time to calculate the preliminary equivalent net power after environmental factor correction at that time.

[0037] The specific calculation process is completed by the digital divider in the digital signal processing module. The division operation is performed point by point in a real-time calculation method at the sampling level. That is, the synchronized real-time operating power data at each moment is used as the dividend, and the synchronized environmental coupling coefficient data at the corresponding moment is used as the divisor. The ratio result is output as the preliminary equivalent net power value at that moment.

[0038] The final preliminary equivalent net power time series data, arranged in chronological order, is used for the next step of smoothing.

[0039] S43: Perform smoothing filtering on the preliminary equivalent net power timing data generated above to eliminate high-frequency noise fluctuations that may be introduced during the division calculation, and obtain more stable and representative power output results.

[0040] In this invention, two typical algorithms are used for smoothing filtering: One approach is to use a first-order low-pass infinite impulse response (IIR) digital filter to perform real-time recursive filtering on the preliminary equivalent net power time series data, with the specific filter coefficients preset according to the sampling frequency and system response characteristics; Another approach is to use a moving average algorithm, setting the sliding window length between 5 and 15, and performing weighted smoothing on each sampling point. Specifically, the values ​​of several points before and after the sampling point are averaged with equal weights, and the result is used to replace the original value of the current sampling point.

[0041] Two filtering methods can be configured and selected based on the equipment installation environment and data fluctuation characteristics. The processed result is the final output equivalent net power, which directly reflects the actual operating energy consumption level of the equipment after eliminating the influence of the external environment.

[0042] S5: Based on the equivalent net power, the comprehensive energy efficiency ratio, used to characterize the equipment's operating efficiency, is calculated as follows: S51: Receives the time-series data of the equivalent net power output from S4, and performs unit conversion and dimension unification processing on this data to ensure that the subsequent integration calculation results have a unified physical meaning and statistical comparability. Specifically, the unit of the sampled equivalent net power is uniformly converted from "kilowatt (kW)" to "megawatt (MW)" using the following conversion formula: ; in, Represents the original value of the equivalent net power. This is the converted value. After processing, the standardized equivalent net power data is output in megawatts (MW) for integration processing in subsequent statistical periods.

[0043] S52: Using a complete statistical period as the time boundary, perform an integral operation on the standardized equivalent net power data mentioned above to calculate the total energy output by the equipment within that statistical period, which is taken as the actual equivalent net work performed by the equipment. There are two possible ways to define a complete statistical period: 1. If the equipment is to operate continuously, a fixed hourly time window (e.g., 60 minutes) can be set. 2. If the equipment operates with discrete work cycles, the time taken for the target mechanical equipment to complete a standard work cycle is used as the cycle boundary. The work cycle is defined as the entire process of the equipment completing one digging, lifting, rotating, unloading and returning to its original position.

[0044] The aforementioned statistical period is automatically marked with its start and end times by the scheduling control module, driving subsequent integral calculations. The integration operation employs Simpson's numerical integration method based on the following calculation formula: ; Where E represents the equivalent net work. The sampling time step is at equal intervals, and n is the number of sampling points within this statistical period (which is an even number). This represents the equivalent net power value at the 0th sampling point. The equivalent net power value at the nth sampling point This represents the equivalent net power value at odd-numbered sampling points. This represents the equivalent net power value at even-numbered sampling points. This represents the sum of an odd number of terms. This represents the sum of an even number of terms.

[0045] The integral result is the effective power output of the equipment within the statistical period (unit: megawatt-hour, MWh), which is used as the equivalent net power output.

[0046] S53: Obtain the rated input energy of the equipment within the above statistical period, and calculate the ratio between the input energy and the equivalent net power output in step S52 to obtain the final comprehensive energy efficiency ratio used to characterize the operating efficiency of the equipment.

[0047] Rated input energy can be obtained in two ways: If the equipment model has been pre-registered in the system, the rated input energy of the equipment model in the corresponding statistical period can be directly retrieved from the built-in equipment model-energy consumption database. If the equipment is connected to an energy consumption monitoring channel, the actual energy consumption data is obtained by monitoring the equipment's fuel consumption or the electrical energy input from the grid in real time, and the input energy is calculated by combining the standard calorific value or the power conversion factor. For fuel-powered devices, the formula for calculating input energy is: ; in, In order to consume fuel volume, This is the standard calorific value (unit: megawatt-hours per liter) for this type of fuel. For electrically driven equipment, the formula for calculating input energy is: ; in, The power input to the power grid. Finally, the formula for calculating the overall energy efficiency ratio is: ; The overall energy efficiency ratio is a dimensionless indicator. The higher the value, the more effective work the equipment can accomplish per unit of energy consumption, and the higher the operating efficiency.

[0048] S6: Compare the overall energy efficiency ratio with the preset efficiency threshold range, and output the current operating efficiency level and warning signal of the equipment, specifically: S61: Receive the comprehensive energy efficiency ratio output in step S5, and obtain the preset efficiency threshold range corresponding to the current equipment model and operating conditions from the cloud database.

[0049] The preset efficiency threshold range includes three sub-ranges: High-efficiency range: Indicates that the equipment is operating in excellent condition and has high energy utilization efficiency; Normal range: Indicates that the equipment is operating within an acceptable range; Inefficient range: This indicates that the equipment is operating in a low efficiency and there is a potential risk of abnormal energy consumption.

[0050] Database query operations are initiated by the control host through a configured IoT network, sending a data request to the remote cloud database. The request includes information such as the device's unique identifier, the current operating mode code, and a timestamp. Upon receiving the request, the cloud database uses its built-in rule engine to query the corresponding efficiency standards and returns the preset efficiency threshold range to the field control host in a structured data format. This efficiency threshold range supports an online dynamic update mechanism; the cloud database can automatically adjust and distribute threshold configurations based on the latest industry standards or equipment upgrades, achieving long-term adaptive updates.

[0051] S62: The received comprehensive energy efficiency ratio is compared with the preset efficiency threshold range in real time to determine the operating efficiency level of the target device.

[0052] The specific comparison logic is as follows: If the overall energy efficiency ratio is greater than or equal to the lower limit of the high efficiency range, the current operating efficiency level is determined to be "high efficiency". Otherwise, determine whether the overall energy efficiency ratio is greater than or equal to the lower limit of the normal range. If it is true, then it is judged as "normal". If neither of the first two conditions is met, the current operating status of the equipment is determined to be "inefficient".

[0053] The entire comparison process is executed in real time on the local edge computing terminal, ensuring that the current operating efficiency level is updated immediately after each statistical cycle is completed, and providing a level identifier that can be used to drive subsequent response logic.

[0054] S63: Generate and trigger corresponding early warning signals based on the determined operating efficiency level, and simultaneously output the operating results to the human-machine interface and remote management platform: If the operating efficiency level is "high efficiency", a green safety signal is generated, the human-machine interface displays a green icon and prompts "excellent operation", and no on-site intervention is required; If the operating efficiency level is "normal", a yellow warning signal will be generated, the interface will display a yellow icon and prompt "Efficiency is normal, please continue to observe"; If the operating efficiency level is "inefficient", a red alarm signal will be generated, and the following two operations will be performed: Audible and visual alarm handling: The control system triggers the local high-decibel buzzer and red warning light of the equipment to immediately provide on-site audible and visual alarm prompts, reminding maintenance personnel to pay attention to the equipment status; Remote early warning and linkage processing: A digital command signal is generated and automatically sent to the remote monitoring platform of the equipment management center via the Internet of Things network. After receiving the red alarm signal, the platform automatically triggers the built-in work order management module to generate a maintenance and inspection work order and assign it to the corresponding maintenance team, realizing a remote closed-loop response to anomalies.

[0055] In addition, the final operating efficiency level and warning signals will be displayed in real time on the local human-machine interface in a graphic and textual format, allowing on-site operators to intuitively grasp the equipment operating status. At the same time, they will be saved in the local log for later operation data analysis and decision support.

[0056] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0057] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for monitoring the operating efficiency of riverside mechanical equipment using the Internet of Things (IoT), characterized in that, Includes the following steps: S1: Real-time collection of the target mechanical equipment's operating electrical parameters and its operating area's environmental parameters via an IoT sensor node cluster; the operating electrical parameters include operating voltage and operating current; the environmental parameters include the equipment's current operating water level and the equipment's tilt angle; S2: The real-time operating power of the device is calculated based on the operating voltage and operating current; S3: Based on the current operating water level and tilt angle of the equipment, the environmental coupling coefficient used to correct the theoretical work is calculated; S4: Correct the real-time operating power based on the environmental coupling coefficient to obtain the equivalent net power after removing environmental interference; S5: Based on the equivalent net power, calculate the comprehensive energy efficiency ratio to characterize the equipment's operating efficiency; S6: Compare the overall energy efficiency ratio with the preset efficiency threshold range, and output the current operating efficiency level and warning signal of the device.

2. The method for monitoring the operating efficiency of riverside mechanical equipment in conjunction with the Internet of Things as described in claim 1, characterized in that, S1 includes: S11: Configure the working parameters of the IoT sensor node cluster deployed on the target mechanical equipment node. The working parameters include sampling frequency and communication protocol to establish a foundation for data acquisition and transmission. S12: Using the electrical parameter sensing unit in the IoT sensor node cluster with configured operating parameters, the operating voltage and operating current of the target mechanical equipment are synchronously collected at the set sampling frequency to obtain the original operating electrical parameter data stream. S13: Using the environmental parameter sensing unit in the IoT sensor node cluster with configured working parameters, synchronously collect the current operating water level and equipment tilt angle to obtain the raw environmental parameter data stream; S14: The original operating electrical parameter data stream and the original environmental parameter data stream are encapsulated according to a unified timing reference to form a comprehensive monitoring data frame containing operating voltage, operating current, current operating water level of the equipment and tilt angle of the equipment, and transmitted to the data aggregation node through the Internet of Things network; S15: The data aggregation node parses and preprocesses the received integrated monitoring data frame, separates and outputs the operating voltage and operating current for use in S2, and the current operating water level and tilt angle of the equipment for use in S3.

3. The method for monitoring the operating efficiency of riverside mechanical equipment in conjunction with the Internet of Things as described in claim 2, characterized in that, The preprocessing includes: removing outliers from the operating voltage and operating current data, and performing a moving average filter on the device tilt angle.

4. The method for monitoring the operating efficiency of riverside mechanical equipment in conjunction with the Internet of Things as described in claim 3, characterized in that, S2 includes: S21: Receive the timing data of the operating voltage and the operating current from S15, and perform data preprocessing on the timing data of the operating voltage and the operating current respectively to eliminate abnormal fluctuations, and obtain the preprocessed operating voltage data and the preprocessed operating current data. S22: Multiply the values ​​of the preprocessed operating voltage data and the preprocessed operating current data at the same time to calculate the instantaneous power at that time and generate instantaneous power time series data. S23: Using a complete equipment working cycle as a time window, perform integral calculation on the instantaneous power time sequence data and calculate the average value to obtain the average power within the time window, and use this average power as the real-time operating power.

5. The method for monitoring the operating efficiency of riverside mechanical equipment in conjunction with the Internet of Things as described in claim 4, characterized in that, S3 includes: S31: Receive the timing data of the current operating water level and tilt angle of the equipment from S15, and perform data calibration and synchronization processing on the timing data of the current operating water level and tilt angle of the equipment to obtain the calibrated current operating water level data and calibrated tilt angle data of the equipment. S32: Input the calibrated current operating water level data of the equipment into a preset water level-resistance mapping function to calculate the water level influence coefficient; at the same time, input the calibrated tilt angle data of the equipment into a preset tilt angle-efficiency mapping function to calculate the tilt angle influence coefficient; S33: Substitute the water level influence coefficient and the tilt angle influence coefficient into the environmental coupling coefficient synthesis formula for weighted fusion calculation, and output the final environmental coupling coefficient used to correct the theoretical work.

6. The method for monitoring the operating efficiency of riverside mechanical equipment in conjunction with the Internet of Things as described in claim 5, characterized in that, The data calibration includes: correcting the time-series data of the current operating water level of the equipment using a temperature compensation algorithm, and performing zero-point drift compensation on the time-series data of the equipment's tilt angle.

7. A method for monitoring the operating efficiency of riverside mechanical equipment in conjunction with the Internet of Things, as described in claim 6, is characterized in that, S4 includes: S41: Receive the timing data of the real-time operating power and the timing data of the environmental coupling coefficient, and perform timestamp alignment and validity verification on the timing data of the real-time operating power and the timing data of the environmental coupling coefficient to ensure that the two are strictly synchronized in time and the data is valid, so as to obtain synchronized real-time operating power data and synchronized environmental coupling coefficient data. S42: Divide the synchronized real-time operating power data with the synchronized environmental coupling coefficient data at the same time to calculate the preliminary equivalent net power after environmental correction at that time, and generate preliminary equivalent net power time series data. S43: Perform smoothing filtering on the preliminary equivalent net power time series data to eliminate high-frequency noise introduced during the calculation process and output the final smooth and stable equivalent net power.

8. A method for monitoring the operating efficiency of riverside mechanical equipment in conjunction with the Internet of Things, as described in claim 7, is characterized in that, S5 includes: S51: Receive the time-series data of the equivalent net power, and perform unit conversion and dimension unification processing on the time-series data of the equivalent net power to obtain standardized equivalent net power data. S52: Within a complete statistical period, the standardized equivalent net power data is integrated to calculate the effective power output of the device within that period, i.e., the actual equivalent net power completed. S53: Obtain the rated input energy of the equipment within the statistical period according to the equipment model, and calculate the ratio of the equivalent net power to the rated input energy to obtain the final comprehensive energy efficiency ratio used to characterize the operating efficiency of the equipment.

9. A method for monitoring the operating efficiency of riverside mechanical equipment in conjunction with the Internet of Things, as described in claim 8, is characterized in that, S6 includes: S61: Receive the comprehensive energy efficiency ratio and query the preset database to obtain a preset efficiency threshold range that matches the current equipment model and operating conditions. The preset efficiency threshold range includes a high-efficiency range, a normal range, and a low-efficiency range. S62: The overall energy efficiency ratio is compared with the preset efficiency threshold range in real time, and the current operating efficiency level of the equipment is determined according to the comparison result. If the overall energy efficiency ratio is in the high efficiency range, the operating efficiency level is "high efficiency"; if it is in the normal range, the operating efficiency level is "normal"; if it is in the low efficiency range, the operating efficiency level is "low efficiency". S63: Generate a corresponding warning signal based on the determined operating efficiency level. If the operating efficiency level is "high efficiency", a green safety signal is generated; if it is "normal", a yellow warning signal is generated; if it is "inefficient", a red alarm signal is generated. The final operating efficiency level and warning signal are then output to the human-machine interface for display.

Images