Operation safety detection system for smart pump room based on Internet of Things

Abstract

The invention relates to the technical field of remote monitoring, in particular to an operation safety detection system for an intelligent pump room based on the Internet of Things, and the system comprises an energy consumption management module which is used for calculating energy consumption of a pump and a valve based on pump room operation data, generating real-time energy consumption data, and adjusting monitoring parameters of a water level sensor and a flowmeter according to the real-time energy consumption data. According to the method, real-time energy consumption monitoring allows an operator to optimize an operation strategy according to energy consumption data, energy efficiency optimization of the pump and the valve is achieved, and energy waste is reduced. And the monitoring parameters are adjusted, so that the response speed and precision of the equipment are improved, and the adaptability to environment change is enhanced. In addition, the safety and reliability of the pump room are improved through an automatically-adjusted monitoring strategy, various safety risks can be responded in time, and accidents are effectively prevented. According to the integrated and automatic management method, the economical efficiency and the operation efficiency of the pump house are improved, the requirement for manual intervention is lowered, and therefore the operation and maintenance cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of remote monitoring technology, and in particular to an operation safety detection system for a smart pump room based on the Internet of Things. Background Art

[0002] Remote monitoring technology is a method of real-time data collection, processing and analysis using technical means. It is widely used in multiple industries, especially in industrial automation and facility management. It enables operators to monitor and control equipment or systems such as pumping stations, power stations and production lines from remote locations.

[0003] However, existing technologies have difficulty in effectively identifying abnormal data patterns, and may overlook potential problems with equipment, increasing the risk of system failure. In addition, existing technologies lack real-time monitoring and optimization capabilities in energy consumption management, resulting in unsatisfactory energy utilization efficiency and increased economic burden. For example, pump speed and valve settings are not adjusted in time to match actual needs, often resulting in unnecessary consumption of energy. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose an operation safety detection system for a smart pump room based on the Internet of Things.

[0005] In order to achieve the above purpose, the present invention adopts the following technical solution: an operation safety detection system for a smart pump room based on the Internet of Things comprises: The energy consumption management module calculates the energy consumption of the pump and valve based on the pump room operation data, generates real-time energy consumption data, adjusts the monitoring parameters of the water level sensor and the flow meter according to the real-time energy consumption data, and generates the results of adjusting the monitoring parameters; The equipment control module receives the result of adjusting the monitoring parameters, adjusts the working mode of the pump and the valve according to the grid load and electricity price information, generates the adjusted working mode data, optimizes the operating parameters of the pump and the valve according to the adjusted working mode data and environmental indicators, and obtains the parameter optimization result; The edge analysis module collects the operation data of the pump and the pipeline, analyzes the data anomalies, generates the diagnosis results, analyzes the diagnosis results, determines the safety risks, and generates the safety risk assessment results; The remote monitoring and dispatching module receives the safety risk assessment result, adjusts the operation strategy of the pump room in combination with the remote control command of the main control center, automatically adjusts the monitoring strategy, and generates the monitoring strategy adjustment result.

[0006] Preferably, the steps of acquiring the real-time energy consumption data are: Obtain pump room operation data, extract the real-time operation parameters of pumps and valves in the pump room operation data, including operation time, opening frequency and current load, and generate real-time operation parameter records of the pump room; Based on the real-time operating parameter records of the pump room, the energy consumption of each pump and each valve is calculated using the following formula: in, For equipment Energy consumption, is the device power, is the running time, is the equipment efficiency, For maximum efficiency, Adjust the index for energy consumption; The calculated energy consumption of each device is summarized to obtain the total energy consumption of the pump room and generate real-time energy consumption data of the pump room.

[0007] Preferably, the steps of obtaining the result of adjusting the monitoring parameters are: Analyze real-time energy consumption data to determine the current energy consumption level, and analyze the correlation between the current energy consumption level and water level and flow rate to generate analysis results; According to the analysis results, calculate and adjust the sensitivity of the water level sensor , the calculation formula is: in, are the original sensitivity and calibration values, is the current energy consumption, is the target energy consumption, is the adjustment sensitivity coefficient; The sensitivity is applied, the setting of the water level sensor is updated to match the current energy consumption, and the result of adjusting the monitoring parameter is generated.

[0008] Preferably, the step of obtaining the adjusted working mode data is: Receive the results of adjusting monitoring parameters, synchronously collect current grid load and electricity price information, and generate data preparation results; Based on the data preparation results, the grid load and electricity price information are analyzed, and the working mode index is calculated according to the current monitoring parameters of the pump and valve. The calculation formula is: in, Indicates the working mode adjustment value, Current grid load, is the load target, is the adjustment factor, is the current electricity price; The working mode adjustment value is used to update the settings of the pump and the valve, optimize energy efficiency and cost, and obtain the adjusted working mode data.

[0009] Preferably, the steps for obtaining the parameter optimization results are: Receiving the adjusted working mode data and current environmental indicators, the current environmental indicators including temperature, humidity and pressure, integrating the adjusted working mode data and the current environmental indicators to obtain a data integration result; Based on the data integration results, the operating parameters of the pumps and valves are analyzed and adjusted to match the environmental changes and the working mode requirements, the parameters that need to be optimized are determined, and an operating parameter adjustment plan is obtained; According to the operating parameter adjustment scheme, the flow rate, switching frequency and pressure setting of the pump and valve are set to obtain parameter optimization results.

[0010] Preferably, the steps of obtaining the diagnosis result are: Collecting real-time operation data of the pump room and pipeline, the real-time operation data including flow rate, pressure and temperature, to obtain an operation data set; Based on the operation data set, an abnormality index is calculated to identify abnormal patterns in the operation data. The calculation formula is: in, represents the cumulative anomaly index, represents a single data point, is the data average, is the standard deviation, is the total number of data points; Based on the abnormal index, the performance status of the pump and the pipeline is analyzed, potential operating failures or efficiency problems are identified, and diagnostic results are generated.

[0011] Preferably, the steps for obtaining the security risk assessment results are: Based on the diagnosis results, the safety risk score of each fault point is calculated using the following formula: in, Score the overall security risk, is the risk level of the i-th fault point, is the probability weight of the ith fault point, is the total number of fault points; Based on the total safety risk score, the safety status of the equipment is analyzed to determine whether there is a safety risk and form a safety risk assessment result.

[0012] Preferably, the steps for obtaining the monitoring strategy adjustment result are: Prepare to adjust the pump room operation strategy and generate adjustment data based on the safety risk assessment results and remote control instructions from the main control center; The adjustment data is used to calculate the pump room operation strategy parameters, and the algorithm is applied to adjust the pump room working mode. The algorithm formula is: in, Indicates the pump room operation strategy parameters, is the current strategy parameter of the ith pump, is the adjustment factor, is the total number of pumps; Based on the pump room operation strategy parameters, the monitoring strategy is automatically adjusted to optimize the efficiency and safety of the pump room and generate monitoring strategy adjustment results.

[0013] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, real-time energy consumption monitoring allows the operator to optimize the operation strategy according to the energy consumption data, realize the energy efficiency optimization of pumps and valves, and reduce energy waste. Adjusting the monitoring parameters improves the response speed and accuracy of the equipment and enhances its adaptability to environmental changes. In addition, the automatically adjusted monitoring strategy improves the safety and reliability of the pump room, can respond to various safety risks in a timely manner, and effectively prevent accidents. This integrated and automated management method not only improves the economy and operating efficiency of the pump room, but also reduces the need for manual intervention, thereby reducing operation and maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0016] See also Figure 1 The present invention provides a technical solution: an operation safety detection system for a smart pump room based on the Internet of Things comprises: The energy consumption management module calculates the energy consumption of pumps and valves based on the pump room operation data, generates real-time energy consumption data, adjusts the monitoring parameters of the water level sensor and flow meter according to the real-time energy consumption data, and generates the results of adjusting the monitoring parameters; The equipment control module receives the results of adjusting and monitoring parameters, adjusts the working modes of pumps and valves according to the grid load and electricity price information, generates adjusted working mode data, optimizes the operating parameters of pumps and valves according to the adjusted working mode data and environmental indicators, and obtains parameter optimization results; The edge analysis module collects the operating data of pumps and pipelines, analyzes data anomalies, generates diagnostic results, analyzes the diagnostic results, determines safety risks, and generates safety risk assessment results; The remote monitoring and dispatching module receives the safety risk assessment results, combines the remote control instructions from the main control center, adjusts the operation strategy of the pump room, automatically adjusts the monitoring strategy, and generates the monitoring strategy adjustment results.

[0017] Specifically, real-time energy consumption monitoring allows operators to optimize operating strategies based on energy consumption data, optimize the energy efficiency of pumps and valves, and reduce energy waste. Adjusting monitoring parameters improves the response speed and accuracy of the equipment and enhances its adaptability to environmental changes. In addition, the automatically adjusted monitoring strategy improves the safety and reliability of the pump room, responds to various safety risks in a timely manner, and effectively prevents accidents. This integrated and automated management method not only improves the economy and operating efficiency of the pump room, but also reduces the need for manual intervention, thereby reducing operation and maintenance costs.

[0018] The steps to obtain real-time energy consumption data are as follows: Obtain pump room operation data, extract the real-time operation parameters of pumps and valves in the pump room operation data, including operation time, opening frequency and current load, and generate real-time operation parameter records of the pump room; Based on the real-time operating parameter records of the pump room, the energy consumption of each pump and each valve is calculated using the following formula: in, For equipment Energy consumption, is the device power, is the running time, is the equipment efficiency, For maximum efficiency, Adjust the index for energy consumption; The calculated energy consumption of each device is summarized to obtain the total energy consumption of the pump room and generate real-time energy consumption data of the pump room.

[0019] Specifically, the process of extracting operating parameters involves collecting information from multiple data sources, including but not limited to operating hours, opening frequency, and current load recorded by sensors. This data is automatically recorded by sensors installed on each pump and valve. The sensors scan the equipment status once a minute to ensure that the collected data is highly real-time and accurate. The collected data is first stored in the local control unit and then uploaded to the central database regularly for further analysis. These operating parameters are the basis for evaluating equipment performance and planning maintenance activities to ensure that the equipment can operate in the best condition. In this way, not only can a comprehensive record of real-time operating parameters of the pump room be obtained, but also the efficiency of the equipment can be monitored in real time, thereby providing data support for energy consumption management and optimization. formula The benefit is that by introducing equipment efficiency With maximum efficiency The proportion and energy adjustment index , which can evaluate the energy efficiency performance of each device in more detail. This setting ensures that the energy consumption calculation not only reflects the actual operating status of the device, but also takes into account the impact of efficiency changes on energy consumption; The acquisition steps are to read directly from the device specification table. The sensors record the equipment operation time. Obtained from equipment maintenance records and data provided by the manufacturer, is the industry standard value. It is the adjustment factor determined through historical data analysis; The acquisition steps are as follows: record the actual power value consumed at each pump and valve, detect the current and voltage at intervals of one second or one minute through the online power acquisition device, multiply them to obtain the instantaneous power, and then average all instantaneous power points to obtain And recorded as 30kW.

[0020] The acquisition steps are as follows: record the start and stop time of the equipment throughout the day, accumulate the time point difference to obtain the running time in the current cycle, and record it as 5h.

[0021] The acquisition steps are as follows: based on the comparison between the actual output flow and input power of the known device, the actual output power of the device is obtained from multiple online measurements and divided by the input power to obtain the device efficiency value, which is recorded as 0.85.

[0022] The steps for obtaining are to determine the highest efficiency that can be achieved by comparing the comprehensive efficiencies of equipment of the same category and specification. The highest efficiency range can be analyzed by combining previous operating data, and the efficiency peak value of this equipment can be obtained and recorded as 0.95.

[0023] The steps to obtain are as follows: by continuously observing the sensitivity of energy consumption to load changes in the water pump engineering application scenario, an energy consumption coefficient adjustment sequence is established, mathematical regression processing is performed on the sequence, and the fitted stability index is taken, which is recorded as 1.02.

[0024] Calculation process: The first step is to multiply the power by the running time: The second step is to introduce the equipment efficiency ratio: The third step is to multiply the above results: The fourth step is to evaluate the results Exponential operation: The result shows that the energy consumption value for the current device state and operation cycle is approximately 136.39 kWh, which corresponds to the energy consumption data calculated in this step. When the result is greater than 100 kWh, it means that the energy consumption of the device is high in the given operation cycle, and when the result is less than 100 kWh, it means that the energy consumption is low; When aggregating the energy consumption data of each device in the pump room to calculate the total energy consumption, a method that comprehensively considers the operating status and efficiency of each device is adopted. First, data is collected from the energy consumption monitoring system of each device, which includes the power, operating time and efficiency of the device. The energy consumption of each device is first calculated according to its power and operating time, and then adjusted according to the efficiency of the device. For those inefficient devices, their impact will be reduced by reducing their weight in the total energy consumption calculation. After the energy consumption data of all devices are aggregated, the total energy consumption of the pump room is obtained by weighting each device according to its contribution to the total operating load through the weighted average method. This process not only optimizes the accuracy of energy consumption calculations, but also provides a basis for decision-making on equipment maintenance and energy-saving measures, ensuring that the pump room system can operate at the highest efficiency.

[0025] The steps to obtain the results of adjusting monitoring parameters are as follows: Analyze real-time energy consumption data to determine the current energy consumption level, and analyze the correlation between the current energy consumption level and water level and flow rate to generate analysis results; According to the analysis results, calculate and adjust the sensitivity of the water level sensor , the calculation formula is: in, are the original sensitivity and calibration values, is the current energy consumption, is the target energy consumption, is the adjustment sensitivity coefficient; Apply sensitivity and update the water level sensor settings to match the current energy consumption, generating results for adjusting monitoring parameters.

[0026] Specifically, collect current energy consumption data from the energy consumption monitoring system and evaluate the operating efficiency of the pump room based on this data. Identify the equipment with the highest energy consumption and analyze the correlation between its energy consumption and the water level and flow monitoring parameters. Use historical data comparison methods to analyze the changes in energy consumption levels and energy consumption during the same period in the past. Through this comparison, determine the trend of increasing or decreasing energy consumption. On this basis, use the Pearson or Spearman correlation coefficient to determine the specific impact of energy consumption changes on water level and flow parameters. This analysis result will be used to guide subsequent parameter adjustment decisions to ensure that the adjustment measures can accurately reflect the actual impact of energy consumption changes on the monitoring system.

[0027] In the formula, The steps for obtaining the sensitivity of the newly installed water level sensor are as follows: when calibrating and testing the sensor before it goes online, select a standard water level scale reference point and place the sensor at that point, read the initial sensitivity value of the sensor, combine multiple re-tests within 30 days and perform average processing, and record this result as 3.5.

[0028] The acquisition steps are as follows: in the pump room energy consumption collection, the actual energy consumption values ​​of the past 24 hours are continuously accumulated, the hourly energy consumption is read one by one, and all hourly records are added together to obtain the total energy consumption for the day. The total is measured to be 400kWh on that day, so 400 is used as the current energy consumption value.

[0029] The steps for obtaining it are to refer to the average energy consumption performance during normal operation obtained above, extract the median value from the 30-day statistical period and combine it with the design load factor of the pump room equipment to determine the target energy consumption range to be around 300kWh, which is currently taken as 300.

[0030] The acquisition steps are as follows: compare the responses of the water level sensor in different calibration processes through the previous multiple sensitivity calibration results and the equipment operation history records, establish a calibration coefficient sequence from these records, perform weighted regression processing on the sequence, and record the steady-state convergence result as 1.25.

[0031] Calculation process: Calculate the energy consumption difference: Divide the energy consumption difference by the adjustment factor : Subtract the above result from the initial sensitivity value: The results show that This value in the step of the formula means that when the current energy consumption and the target energy consumption differ greatly, the sensitivity is updated in a lower direction. When the result is close to 0 or above, it means that the adjustment range is small. If it exceeds the negative range too much, it means that the energy consumption differs significantly from the target energy consumption, and other control measures need to be combined for further analysis.

[0032] Reconfigure the monitoring settings of the water level sensors according to the new sensitivity to ensure the accuracy and responsiveness of the monitoring system. First, manually enter the new parameters into the control interface and ensure that these parameters are applied to the relevant sensors and metering equipment according to the predetermined standards. Next, perform functional tests, such as response time tests and range tests, to ensure that each adjustment meets the expected technical specifications. Finally, by continuously monitoring and recording the operating data of the equipment, verify whether the adjusted parameter settings have improved the monitoring accuracy and the overall effectiveness of the system, thereby ensuring that the pump room can maintain optimal performance under the new operating conditions.

[0033] The steps to adjust the working mode data are as follows: Receive the results of adjusting monitoring parameters, synchronously collect current grid load and electricity price information, and generate data preparation results; Based on the data preparation results, the grid load and electricity price information are analyzed, and the working mode index is calculated according to the current monitoring parameters of the pump and valve. The calculation formula is: in, Indicates the working mode adjustment value, Current grid load, is the load target, is the adjustment factor, is the current electricity price; The working mode adjustment values ​​are used to update the settings of pumps and valves, optimize energy efficiency and cost, and obtain the adjusted working mode data.

[0034] Specifically, the results of the adjusted monitoring parameters are received, and real-time data collection of the current grid load and electricity price is synchronously performed. After collecting this data, a detailed analysis is performed, which includes determining the real-time load of the grid and the current electricity price, and identifying the differences with the set load and electricity price thresholds. Analyzing this data can help identify the cost efficiency of grid operation in a specific time period, and further determine the optimal point for energy consumption. Based on this data analysis, the working mode of pumps and valves is adjusted to ensure that pumps and valves operate at the lowest efficiency during the period with the highest electricity cost, and improve efficiency during the period with lower electricity cost. This strategy aims to reduce operating costs and increase the energy efficiency of the system by adjusting the operating mode to adapt to changes in grid demand.

[0035] In the formula, Adjust the value for the working mode, is the current grid load, is the optimal load target, is the load adjustment factor, is the current electricity price; The formula is beneficial in that it allows for dynamic adjustment of pump and valve operating modes to achieve energy efficiency optimization by taking into account real-time data on electricity prices and grid load; The acquisition steps are to read the real-time load value in the current period from the public power grid monitoring system, collect multiple times in the same period and average the collected results. After summarizing this step, 3500kW is obtained.

[0036] The acquisition steps are to conduct large-scale statistics on equipment operation and power demand during the previous load forecast period, and determine the ideal load range under the current process requirements based on operating parameter screening and empirical data analysis, which is finally recorded as 2500kW.

[0037] The steps for obtaining the formula are as follows: when comparing the load response speed of pumps and valves under various working conditions, a corresponding table between different load differences and required adjustment time is established. After regression analysis of the corresponding table, a sequence of adjustment coefficients that can dynamically float with the load difference is obtained, and the optimal value is selected as 0.70.

[0038] The acquisition steps are as follows: read the electricity price for the current period according to the pricing information on the grid side on that day, exclude the instantaneous abnormal price caused by line maintenance, and sample and compare the continuous records, and finally obtain the electricity price for this period as 0.90 yuan / kWh.

[0039] Calculation process: The first step is to calculate the load difference : The second step is to compare the above difference with Multiplication: Step 3: Divide the result by the electricity price : This result shows that the current working mode adjustment value About 777.78, when When it is greater than 500, it means that the running speed or start-stop rhythm of the pump and valve needs to be significantly adjusted to avoid higher electricity costs. When it is less than 300, it means that the matching degree between current load and electricity price is in a relatively stable state.

[0040] Based on the data derived from the operating mode index, the operating mode settings of pumps and valves are updated, including adjusting the operating speed of the pumps and the degree of opening and closing of the valves. These adjustments are made to match the newly calculated operating mode index to cope with real-time changes in electricity prices and grid loads. During this process, the adjustment required for each device is calculated, such as the percentage of pump speed reduction or increase, and the valve opening is adjusted to a specific angle to ensure optimal synchronization with the grid. This adjustment optimizes energy consumption and reduces operating costs by precisely controlling the device's response to changes in grid conditions, thereby improving the responsiveness and efficiency of the entire system.

[0041] The steps to obtain parameter optimization results are: Receiving the adjusted working mode data and the current environmental indicators, the current environmental indicators including temperature, humidity and pressure, integrating the adjusted working mode data and the current environmental indicators to obtain a data integration result; Based on the data integration results, analyze and adjust the operating parameters of pumps and valves to match environmental changes and working mode requirements, determine the parameters that need to be optimized, and obtain the operating parameter adjustment plan; According to the operating parameter adjustment plan, the flow rate, switching frequency and pressure settings of the pump and valve are set to obtain the parameter optimization results.

[0042] Specifically, it receives monitoring parameter results from the sensor network, including flow rate, pressure and opening time data of pumps and valves; at the same time, it collects real-time temperature, humidity and pressure data provided by environmental monitoring stations, which are sent to the control center in real time via wireless signals; at the control center, the data format is first standardized, and then error detection and correction are performed on the collected data, and outliers are removed to ensure data accuracy and availability; Conduct a detailed analysis of the integrated monitoring and environmental data, mainly including the exploration of the correlation between the operating parameters of the pumps and valves and environmental factors; use linear regression analysis to determine the degree of influence of each operating parameter on the equipment performance, find out the environmental factors with the greatest impact, and determine the priority of adjustment accordingly; next, formulate a specific parameter adjustment plan based on the analysis results, which details the operating parameters that need to be adjusted, such as the specific value of flow rate increase or decrease, the optimization range of switching frequency, and the adjustment range of pressure setting; the execution of this step ensures that the operating parameters of the pumps and valves can adapt to the current working environment and enhance the operating efficiency and reliability of the equipment; According to the adjustment plan, the operating parameters of the pumps and valves are set. During the adjustment process, new flow rates, switching frequencies and pressure settings are applied to each device individually. Operators monitor the implementation of these adjustments to ensure that each adjustment is accurately executed. At the same time, the adjustment effects are monitored to ensure that the equipment operation achieves the expected results. During the adjustment process, all key operations and equipment responses are recorded for subsequent performance evaluation and necessary further adjustments. Through these precise and targeted adjustments, the optimization of equipment operating parameters is achieved, ensuring that the equipment can achieve optimal performance under new working conditions, thereby obtaining the final parameter optimization results.

[0043] The steps to obtain the diagnosis results are: Collect real-time operation data of the pump room and pipelines, including flow rate, pressure and temperature, to obtain operation data sets; Based on the running data set, the anomaly index is calculated to identify abnormal patterns in the running data. The calculation formula is: in, represents the cumulative anomaly index, represents a single data point, is the data average, is the standard deviation, is the total number of data points; Based on the abnormal index, the performance status of pumps and pipelines is analyzed, potential operating failures or efficiency issues are identified, and diagnostic results are generated.

[0044] Specifically, in the daily operation of pump rooms and pipelines, in order to ensure the integrity and accuracy of the data, it is necessary to deploy various types of sensors, such as flow meters, pressure gauges, and temperature sensors, which are placed at key nodes. Through these sensors, flow rate, pressure, and temperature data can be collected every minute. Once the data is collected, it is transmitted to the data processing center. In the data processing center, the first operation is data cleaning to remove abnormal data points caused by sensor failure or external interference. The cleaning algorithms mainly include sliding window method and outlier removal method, which can effectively smooth short-term data fluctuations and improve data quality. Afterwards, these pre-processed data will be temporarily stored in the server's cache, awaiting further analysis and use.

[0045] formula The benefit is that by processing the deviations of each data point, it is possible to identify abnormal patterns in the data and help diagnose possible problems; The acquisition steps are as follows: select the single measurement values ​​of flow rate, pressure and temperature from the previously obtained operating data set, arrange each measurement value in chronological order to form a sequence, for example, if the data is collected once every minute within 24 hours, there will be a total of 1440 points, read the records from these points one by one, and use each single measurement value obtained as And number them sequentially.

[0046] The steps to obtain are: The average value is obtained by summing up and dividing by the total number of data points. For example, 1440 measured values ​​are summed up and divided by 1440 to obtain a value, which is recorded as 12.5 (for example only).

[0047] The steps to obtain are: and The difference is accumulated by square and divided by the total number of data points and then squared. For example, for the above sequence, Subtract 12.5, square the result, add it, divide it by 1440, and take the square root to get the standard deviation, which is 1.8 (for example only).

[0048] The acquisition step is to count the total number of data points actually involved in the calculation in the sequence, which is 1440 in the above acquisition scheme.

[0049] Calculation process: The first step is to minus and with Compare: In the second step, the above results of all data points are accumulated: In the third step, the cumulative result is , for example, the cumulative result is approximately 932.4.

[0050] The results show that the cumulative abnormal index of the current 24-hour monitoring sequence is about 932.4. When it is greater than 800, the overall deviation is obvious and further comparison is required in combination with each individual data point. When it is less than 500, it means that the overall data deviation is small.

[0051] Based on the collected operating data, data anomaly analysis is a key step to ensure the normal operation of the pump and pipeline system. This process involves a variety of statistical methods, including standard deviation analysis and historical data comparison. First, the real-time data is compared with the historical performance data, and the data points that deviate from the normal range are quickly identified by calculating the standard deviation and average of the current data. For example, if the pressure reading at a certain point in time is suddenly higher than two standard deviations of the average, the data is marked as abnormal. This method can effectively screen out possible signs of operating failures or performance degradation, thereby triggering alarms and prompting the maintenance team to inspect and repair. Next, the analysis results will be used to generate a diagnostic report, which lists all identified problems and corresponding recommended measures in detail.

[0052] The steps to obtain the security risk assessment results are as follows: Based on the diagnosis results, the safety risk score of each fault point is calculated using the following formula: in, Score the overall security risk, is the risk level of the i-th fault point, is the probability weight of the ith fault point, is the total number of fault points.

[0053] Based on the total safety risk score, analyze the safety status of the equipment, determine whether there is a safety risk, and form a safety risk assessment result.

[0054] Specifically, the system receives a diagnostic result data set, which contains the equipment performance and potential fault information obtained from the operating data of the pump and pipeline, and performs data cleaning and preprocessing, including outlier detection and missing data interpolation. It uses statistical methods to calculate basic data statistics, such as mean, standard deviation, etc., which are used as input parameters for subsequent risk assessment models. On this basis, it builds a fault database based on the historical fault records and maintenance logs of the equipment to support the accuracy and reliability of the fault diagnosis algorithm, and finally obtains a diagnostic result data set for risk assessment.

[0055] formula The benefit of this approach is that it provides a method for quantitatively assessing the safety risk of the entire system by combining the risk level and probability of occurrence of the failure point.

[0056] The steps for obtaining the risk level are as follows: for each identified fault point, record the possible impact on the pump and pipeline, refer to the detection data of downtime and maintenance difficulty when multiple faults occur, compile a fault intensity sequence, normalize the maximum and minimum values ​​​​among them to obtain the risk level range, and then repeatedly compare the intervention measures and loss range when the fault occurs during actual operation, and take the comprehensive score as 5 (for example only).

[0057] The steps of obtaining are as follows: according to the statistics of the occurrence probability of each fault point under the actual operating conditions, the equipment operation records of multiple days, weeks or even months under the same working conditions are retrieved, and whether the fault point occurs is marked in each record and the proportion of the occurrence time is recorded. For example, if this fault point occurs 3 times in 300 hours of continuous monitoring of equipment operation, the calculated probability weight is 3 / 300=0.01. A more accurate weight value can be obtained based on more data sources, which is recorded as 0.01 in this example.

[0058] The acquisition steps are to merge all known fault points to remove duplicates, classify the same fault sources, and finally count all items confirmed as independent fault types, and use their values ​​as ,In this example, m=5.

[0059] Calculation process: If the number of fault points m=5, obtain the risk levels of the five fault points respectively to , and their respective probability weights to ; Example , , , , ; Multiply and add the terms in the formula: Perform numerical operations: The results show that the current total safety risk score is about 0.33. When the value is greater than 0.50, it means that the safety risk may be high and high-risk fault points need to be checked. When the value is lower than 0.20, it indicates that the overall fault risk is at a low level.

[0060] Based on the intermediate results of the safety risk assessment, the design parameters, operating conditions and environmental factors of the equipment are comprehensively considered. By simulating different operating scenarios and evaluating the response of the equipment under extreme conditions, the failure mode effect analysis (FMEA) method is used to predict the key factors that may cause equipment failure. This includes continuous monitoring of physical parameters such as temperature and pressure, analyzing their changing trends, performing linear regression analysis on the trends, identifying key risk points that may affect the safe operation of the equipment, and ultimately generating a comprehensive assessment result of the safety status of the entire equipment, indicating whether the current operating status of the equipment is stable and whether there are safety issues that need to be resolved immediately.

[0061] The steps to obtain the monitoring strategy adjustment results are as follows: According to the safety risk assessment results and the remote control instructions from the main control center, prepare to adjust the pump room operation strategy and generate adjustment data.

[0062] Using the adjustment data, the pump room operation strategy parameters are calculated, and the algorithm is applied to adjust the pump room working mode. The algorithm formula is: in, Indicates the pump room operation strategy parameters, is the current strategy parameter of the ith pump, is the adjustment factor, is the total number of pumps.

[0063] Based on the pump room operation strategy parameters, the monitoring strategy is automatically adjusted to optimize the efficiency and safety of the pump room and generate monitoring strategy adjustment results.

[0064] Specifically, data such as pressure, flow, and power consumption are recorded in real time through installed sensors. Data encryption and integrity verification mechanisms are used to ensure data security and accuracy. This data is compared with historical operating data to analyze deviations and anomalies. This analysis relies on machine learning technology to identify subtle trend changes and potential risk factors. In addition, remote control instructions from the main control center are integrated, and these instructions are adjusted based on the latest safety risk assessment results to optimize the operation strategy of the pump room. During this integration process, the operating status and environmental factors of each pump station are evaluated to ensure that adjustment measures can improve efficiency without sacrificing safety. Finally, an adjusted operation strategy is generated, and actionable suggestions and warnings are provided to operators, thereby achieving effective monitoring and automatic adjustment of the overall operation strategy of the pump room.

[0065] formula The benefit is that it provides a quantitative method for adjusting the pump room operation strategy, which can automatically calculate the new operation strategy based on the current strategy of each pump and the set adjustment coefficient, which is crucial for achieving automated control and optimizing pump room operation. The acquisition steps are as follows: calculate according to the existing working mode data of each pump and its start-stop frequency records under different load requirements. For example, first record the operating time, operating frequency and flow output of each pump in the past 24 hours, and then summarize these indicators again after data cleaning and compare them with the strategy benchmark values ​​of the previous stage, and finally obtain the current strategy parameters of each pump. In this example, the Note it as 1.2.

[0066] The acquisition steps are as follows: referring to the regulation rules of the previous pump control system and the peak-valley characteristics of the current power grid, a correction table with time and load fluctuations is established to obtain the regulation coefficient sequence corresponding to each pump, and then the most suitable coefficient in the sequence is taken out according to the actual operating environment in the pump room (such as water level changes and energy consumption restrictions, etc.), and recorded as the coefficient of the pump. In this example, the Note it as 1.05.

[0067] The acquisition step is to count the number of all pumps in the pump room that can execute the strategy and record it as the total number. For example, at this stage, there are 4 pumps that meet the execution conditions of the strategy, which are recorded as .

[0068] Calculation process: Determine the total number of pumps And obtain one by one and ; The second step is to give an example ; ; ; ; First, check the To do the multiplication: Add the total and divide by 4: The results show that the new pump room operation strategy parameter is approximately 1.215. When the value is greater than 1.2, it means that the scheduling tends to increase the overall operating speed or flow. When the value is lower than 1.0, it means that the scheduling tends to be conservative and the pump operation frequency needs to be reduced accordingly.

[0069] After the new pump room operation strategy is generated, the next key task is to automatically adjust the monitoring strategy. This adjustment is based on the comprehensive analysis of the operation data and risk assessment results. First, the current working status and historical performance data of each pump are analyzed, and then combined with the operation strategy parameters to determine how each pump should be adjusted to adapt to the new strategy. This includes adjusting the pump speed, flow setting, and possible pressure thresholds. In addition, considering energy efficiency and cost-effectiveness, the optimal operating parameters are calculated to reduce energy consumption and maintenance requirements. Then, pre-operation tests are carried out through simulation software to ensure that the adjusted parameters will not cause instability or exceed safety standards. On this basis, the adjusted monitoring strategy is continuously optimized through a real-time feedback mechanism, which can receive feedback signals from each pump in real time and automatically adjust the control parameters to maintain the system in the best state. Finally, these adjustment results are formally recorded and fed back to the central control room, where operators can monitor these changes in real time and intervene manually when necessary to ensure efficient and safe operation of the pump room.

Claims

1. An operation safety detection system for a smart pump room based on the Internet of Things, characterized in that: The system comprises: The energy consumption management module calculates the energy consumption of the pump and valve based on the pump room operation data, generates real-time energy consumption data, adjusts the monitoring parameters of the water level sensor and the flow meter according to the real-time energy consumption data, and generates the results of adjusting the monitoring parameters; The equipment control module receives the result of adjusting the monitoring parameters, adjusts the working mode of the pump and the valve according to the grid load and the electricity price information, generates the adjusted working mode data, optimizes the operating parameters of the pump and the valve according to the adjusted working mode data and the environmental indicators, and obtains the parameter optimization result; The edge analysis module collects the operation data of the pump and the pipeline, analyzes the data anomalies, generates the diagnosis results, analyzes the diagnosis results, determines the safety risks, and generates the safety risk assessment results; The remote monitoring and dispatching module receives the safety risk assessment result, adjusts the operation strategy of the pump room in combination with the remote control command of the main control center, automatically adjusts the monitoring strategy, and generates the monitoring strategy adjustment result.

2. The operation safety detection system for a smart pump room based on the Internet of Things according to claim 1 is characterized in that: The steps for acquiring the real-time energy consumption data are as follows: Obtain pump room operation data, extract the real-time operation parameters of pumps and valves in the pump room operation data, including operation time, opening frequency and current load, and generate real-time operation parameter records of the pump room; Based on the real-time operating parameter records of the pump room, the energy consumption of each pump and each valve is calculated using the following formula: in, For equipment Energy consumption, is the device power, is the running time, is the equipment efficiency, For maximum efficiency, Adjust the index for energy consumption; The calculated energy consumption of each device is summarized to obtain the total energy consumption of the pump room and generate real-time energy consumption data of the pump room.

3. The operation safety detection system for a smart pump room based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the result of adjusting the monitoring parameters are as follows: Analyze real-time energy consumption data to determine the current energy consumption level, and analyze the correlation between the current energy consumption level and water level and flow rate to generate analysis results; According to the analysis results, calculate and adjust the sensitivity of the water level sensor , the calculation formula is: in, is the original sensitivity, is the current energy consumption, is the target energy consumption, is the adjustment sensitivity coefficient; The sensitivity is applied, the setting of the water level sensor is updated to match the current energy consumption, and the result of adjusting the monitoring parameters is generated.

4. The operation safety detection system for a smart pump room based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the adjustment working mode data are as follows: Receive the results of adjusting monitoring parameters, synchronously collect current grid load and electricity price information, and generate data preparation results; Based on the data preparation results, the grid load and electricity price information are analyzed, and the working mode index is calculated according to the current monitoring parameters of the pump and valve. The calculation formula is: in, Indicates the working mode adjustment value, Current grid load, is the load target, is the adjustment factor, is the current electricity price; The working mode adjustment value is used to update the settings of the pump and the valve, optimize energy efficiency and cost, and obtain the adjusted working mode data.

5. The operation safety detection system for a smart pump room based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the parameter optimization results are: Receiving the adjusted working mode data and current environmental indicators, the current environmental indicators including temperature, humidity and pressure, integrating the adjusted working mode data and the current environmental indicators to obtain a data integration result; Based on the data integration results, the operating parameters of the pumps and valves are analyzed and adjusted to match the environmental changes and the working mode requirements, the parameters that need to be optimized are determined, and an operating parameter adjustment plan is obtained; According to the operating parameter adjustment scheme, the flow rate, switching frequency and pressure setting of the pump and valve are set to obtain parameter optimization results.

6. The operation safety detection system for a smart pump room based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the diagnosis result are: Collecting real-time operation data of the pump room and pipeline, the real-time operation data including flow rate, pressure and temperature, to obtain an operation data set; Based on the operation data set, an abnormal index is calculated to identify abnormal patterns in the operation data. The calculation formula is: in, represents the cumulative anomaly index, represents a single data point, is the data average, is the standard deviation, is the total number of data points; Based on the abnormal index, the performance status of the pump and the pipeline is analyzed, potential operating failures or efficiency problems are identified, and diagnostic results are generated.

7. The operation safety detection system for a smart pump room based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the security risk assessment results are: Based on the diagnosis results, the safety risk score of each fault point is calculated using the following formula: in, Score the overall security risk, is the risk level of the i-th fault point, is the probability weight of the ith fault point, is the total number of fault points; Based on the total safety risk score, the safety status of the equipment is analyzed to determine whether there is a safety risk and form a safety risk assessment result.

8. The operation safety detection system for a smart pump room based on the Internet of Things according to claim 1 is characterized in that: The steps for obtaining the monitoring strategy adjustment result are: Prepare to adjust the pump room operation strategy and generate adjustment data based on the safety risk assessment results and remote control instructions from the main control center; The adjustment data is used to calculate the pump room operation strategy parameters, and the algorithm is applied to adjust the pump room working mode. The algorithm formula is: in, Indicates the pump room operation strategy parameters, is the current strategy parameter of the ith pump, is the adjustment factor, is the total number of pumps; Based on the pump room operation strategy parameters, the monitoring strategy is automatically adjusted to optimize the efficiency and safety of the pump room and generate monitoring strategy adjustment results.

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