Equipment monitoring real-time data acquisition method and system

Abstract

The invention is suitable for the technical field of data processing, and provides an equipment monitoring real-time data acquisition method and system, demand data is acquired, the demand data comprises energy efficiency demands, and corresponding sensor equipment is selected according to the demand data; the method comprises the following steps: acquiring information data of sensor equipment, preprocessing the information data, comparing the preprocessed data with a threshold value, and obtaining a comparison result which comprises comparison qualification and comparison disqualification; obtaining disqualification according to disqualification data generation reasons, obtaining processing schemes, comparing the processing schemes according to the generation reasons, performing scoring according to processing results, and judging whether the processing schemes are replaced or not according to scoring results; the method comprises the following steps: processing data, performing visualization processing according to the processing data, predicting an effect according to a visualization processing result, and judging whether subsequent data meets planning or not according to the prediction effect, thereby solving the problems that a processing scheme cannot be obtained in time after existing data acquisition, and whether the processing scheme is suitable for solving the current data or not cannot be judged at the same time.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to a method and system for collecting real-time data for equipment monitoring. Background Art

[0002] In the management of modern industry, buildings and infrastructure, energy management has become a key factor in improving operational efficiency, reducing energy consumption and reducing environmental impact. With the continuous development of information technology and sensor technology, intelligent and digital energy management systems have emerged, which can monitor and optimize energy consumption in real time. However, accurate energy management and optimization rely on real-time monitoring of equipment status, energy consumption and operating efficiency.

[0003] At present, the application of energy management is mainly focused on collecting environmental and equipment data such as power, temperature, humidity, pressure, etc. through sensors, and analyzing data through centralized monitoring systems, so as to achieve real-time monitoring, early warning, alarm and optimization adjustment. For energy management of industrial and commercial buildings, especially in power, air conditioning, HVAC, lighting and other equipment, real-time data collection and analysis has become an effective means to improve energy efficiency and reduce energy waste.

[0004] However, the existing technology has the problem that after data collection, the processing scheme for unqualified data cannot be scored, and the feasibility of the processing scheme cannot be judged based on the scoring results. Summary of the invention

[0005] The purpose of the embodiment of the present invention is to provide a real-time data collection method for equipment monitoring, aiming to solve the problem raised in the third part of the background technology.

[0006] The embodiment of the present invention is implemented as follows: a method for collecting real-time data for equipment monitoring, the method comprising: Demand data is acquired, wherein the demand data includes energy efficiency demand, and corresponding sensor equipment is selected according to the demand data.

[0007] Collecting sensor equipment information data, preprocessing the information data, comparing the preprocessed data with a threshold value, and obtaining a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison; Obtain the cause of the unqualified data and the treatment plan, compare the treatment plan with the cause, score the treatment results, and determine whether to change the treatment plan based on the scoring results; Perform visualization based on the processed data, make predictions based on the visualization results, and determine whether subsequent data meets the plan based on the prediction results.

[0008] Preferably, the steps of collecting sensor device information data, preprocessing the information data, comparing the preprocessed data with a threshold value, and obtaining a comparison result, wherein the comparison result includes a qualified comparison and a unqualified comparison, specifically include: Collect sensor equipment information data, including power, current, voltage, temperature, humidity, vibration, pressure and flow rate parameters, and pre-process the information data; Obtaining preprocessed data, wherein the preprocessing includes data cleaning, data interpolation, and obtaining an information data threshold, wherein the data threshold is a safety range of the data; The preprocessed data is compared with the threshold value to obtain a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison.

[0009] Preferably, the steps of obtaining the cause of the unqualified data, obtaining a treatment plan, comparing the treatment plan according to the cause, scoring according to the treatment result, and determining whether to change the treatment plan according to the scoring result specifically include: If the comparison is determined to be unqualified, the unqualified comparison means that the preprocessed data exceeds the data threshold, and the unqualified is obtained based on the cause of the unqualified data, which includes sensor problems, equipment failures and data collection errors; Obtaining a treatment plan, wherein the treatment plan is used to solve the cause of the problem, comparing the treatment plan with the cause of the problem, and obtaining a treatment result; Scoring is performed according to the processing results, and the scoring results are obtained. The scoring results are compared with preset grades, and the preset grades are used to judge the quality of the processing. It is determined whether to change the processing plan according to the scoring results.

[0010] Preferably, the steps of performing visualization processing on the processed data, predicting the effect based on the visualization processing result, and judging whether the subsequent data meets the planning according to the prediction effect specifically include: Acquire qualified data and processed unqualified data to obtain processed data, and perform visualization processing according to the processed data, wherein the visualization processing is processing the data into a line graph; Predicting effects based on the visualization results, including energy consumption, operating efficiency and management solutions, and sending the visualization results to the terminal; Obtain a plan, which is expected data for the future, and determine whether subsequent data meets the plan based on the predicted results.

[0011] Preferably, the sensor equipment includes an electric power meter, a temperature and humidity sensor, a flow meter, a pressure sensor and a vibration sensor.

[0012] Another object of an embodiment of the present invention is to provide a real-time data collection system for equipment monitoring, the system comprising: The sensor module obtains demand data, wherein the demand data includes energy efficiency demand, and selects corresponding sensor equipment according to the demand data.

[0013] A preprocessing module collects sensor device information data, preprocesses the information data, compares the preprocessed data with a threshold value, and obtains a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison; The processing scheme module obtains the cause of the unqualified data and obtains the processing scheme, compares the processing scheme according to the cause, scores the processing results, and determines whether to change the processing scheme according to the scoring results; The prediction module performs visualization based on the processed data, predicts the effect based on the visualization results, and determines whether the subsequent data meets the plan based on the prediction results.

[0014] Preferably, the preprocessing module comprises: An information data unit collects information data of sensor equipment, including multiple parameters such as power, current, voltage, temperature, humidity, vibration, pressure and flow, and pre-processes the information data; A preprocessing unit obtains preprocessed data, wherein the preprocessing includes data cleaning, data interpolation, and obtaining an information data threshold, wherein the data threshold is a safety range of the data; The comparison unit compares the preprocessed data with the threshold value to obtain a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison.

[0015] Preferably, the processing scheme module includes: The reason unit, if it is determined that the comparison is unqualified, the unqualified comparison means that the preprocessed data exceeds the data threshold, obtains the reason for the unqualified data, and the reason includes sensor problems, equipment failures and data collection errors; A processing solution unit, which obtains a processing solution, wherein the processing solution is used to solve the cause of the problem, compares the processing solution with the cause of the problem, and obtains a processing result; The scoring unit scores according to the processing result, obtains the scoring result, compares the scoring result with the preset classification, and the preset classification is used to judge the quality of the processing, and determines whether to change the processing plan according to the scoring result.

[0016] Preferably, the prediction module comprises: A visualization unit, which obtains qualified data and processed unqualified data to obtain processed data, and performs visualization processing according to the processed data, wherein the visualization processing is processing the data into a line graph; A prediction unit, which predicts effects according to the visualization processing results, wherein the prediction effects include energy consumption, operation efficiency and management solutions, and sends the visualization results to the terminal; The planning unit obtains a plan, which is expected data for the future, and determines whether subsequent data meets the plan based on the prediction effect.

[0017] Preferably, the sensor equipment includes an electric power meter, a temperature and humidity sensor, a flow meter, a pressure sensor and a vibration sensor.

[0018] The embodiment of the present invention provides a real-time data collection method for equipment monitoring, which obtains demand data, including energy efficiency demand, selects corresponding sensor equipment according to the demand data, collects information data of the sensor equipment, pre-processes the information data to obtain pre-processed data, obtains an information data threshold, the data threshold is a safety range of the data, compares the pre-processed data with the threshold, obtains a comparison result, and if it is determined that the comparison is unqualified, obtains the cause of the unqualified data, which includes sensor problems, equipment failures and data collection errors, obtains a processing plan, compares the processing plan according to the cause, obtains the processing result, scores according to the processing result, obtains the scoring result, compares the scoring result with a preset classification, determines whether to change the processing plan according to the scoring result, obtains qualified data and processed unqualified data, obtains processed data, performs visualization processing according to the processed data, predicts the effect according to the visualization processing result, sends the visualization result to the terminal, obtains a plan, and determines whether subsequent data meets the plan according to the prediction effect, thereby solving the problem that the processing plan cannot be obtained in time after the existing data is collected, and it cannot be determined whether the processing plan is suitable for solving the current data. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A flow chart of a method for collecting real-time data for equipment monitoring provided by an embodiment of the present invention; Figure 2 A flowchart of the steps of collecting sensor device information data, preprocessing the information data, and comparing the preprocessed data with a threshold value provided by an embodiment of the present invention; Figure 3 A flowchart of the steps of obtaining the cause of unqualified data according to the embodiment of the present invention, comparing the treatment plan according to the cause, and determining whether to change the treatment plan according to the scoring result; Figure 4 A flowchart of the steps of predicting the effect according to the visualization processing result and judging whether the subsequent data meets the planning according to the prediction effect provided by the embodiment of the present invention; Figure 5 An architecture diagram of a device monitoring real-time data acquisition system provided by an embodiment of the present invention; Figure 6 An architectural diagram of a preprocessing module provided in an embodiment of the present invention; Figure 7An architectural diagram of a processing solution module provided by an embodiment of the present invention; Figure 8 This is an architecture diagram of a prediction module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0020] 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.

[0021] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0022] like Figure 1 As shown, a device monitoring real-time data collection method provided by an embodiment of the present invention includes: S100, acquiring demand data, where the demand data includes energy efficiency demand, and selecting corresponding sensor equipment according to the demand data.

[0023] In this step, demand data is obtained. Demand data is the basis for determining sensor selection and configuration. Demand data includes energy efficiency requirements, which include target energy efficiency levels and energy use optimization targets. According to the demand data, corresponding sensor devices are selected to determine the energy efficiency indicators required for the target devices. According to the parameters mentioned in the demand data, select sensors that can meet the system requirements, including power metering instruments, temperature and humidity sensors, flow meters, pressure sensors and vibration sensors. By systematically analyzing the demand data and selecting appropriate sensor equipment, it can be ensured that the equipment monitoring data is accurate and meets the expected goals during the energy management and optimization process. This sensor selection based on demand analysis can ensure the accuracy and reliability of data collection, thereby providing support for energy efficiency optimization.

[0024] S200, collecting sensor device information data, preprocessing the information data, comparing the preprocessed data with a threshold, and obtaining a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison.

[0025] In this step, sensor equipment information data is collected, and energy consumption, power, energy efficiency and other data are collected in real time through sensor equipment (such as power meters, current and voltage sensors). These devices will continuously collect data into the system. The data collected by the sensor is generally affected by noise, errors, lost data, etc., so some preprocessing is required; Compare preprocessed data with thresholds. After data preprocessing, the data needs to be compared with the set thresholds. The thresholds are usually determined by the equipment's operating standards, industry specifications, or historical data. The comparison results will reflect whether the data is within the normal range. Common results include data that meets the threshold range and exceeds the normal range. The comparison results include qualified and unqualified.

[0026] S300, obtaining the cause of the unqualified data, obtaining the treatment plan, comparing the treatment plan with the cause, scoring according to the treatment result, and determining whether to change the treatment plan according to the scoring result.

[0027] In this step, the reasons for the generation of unqualified data are obtained. Unqualified data refers to data that fails to meet the expected requirements when the pre-processed data is compared with the set threshold. These data usually trigger an alarm mechanism to indicate potential problems of the equipment and obtain treatment solutions. Common treatment solutions include sensor calibration, sensor maintenance and equipment inspection; Compare the treatment plan with the cause. After determining the treatment plan, compare it with the specific cause to ensure the selected plan is targeted and effective. By matching it with the cause, ensure that the measures taken can effectively solve the problem; Scoring is done based on the treatment results. After the treatment plan is implemented, the treatment effect needs to be evaluated. A set of scoring criteria can be set to score the treatment results, and the decision on whether to change the treatment plan can be made based on the scoring results.

[0028] S400, performing visualization processing according to the processed data, predicting the effect according to the visualization processing result, and judging whether the subsequent data meets the plan according to the prediction effect.

[0029] In this step, visualization is performed based on the processed data. The purpose of visualization is to convert the processed data into easy-to-understand graphics to help users intuitively understand the changing trend and current status of the data. The line graph is used to show the trend of data changes over time, such as power, voltage, and temperature. According to the results of visualization, the prediction effect can be carried out. After data visualization, a certain prediction algorithm can be used to predict the operation status of the equipment in the future and evaluate whether the equipment will enter an unqualified state. Determine whether subsequent data meets the plan based on the prediction results. Determine whether future data meets the set planning requirements based on the prediction results, combined with the performance requirements and safe operation parameters of the equipment.

[0030] like Figure 2 As shown, as a preferred embodiment of the present invention, the steps of collecting sensor device information data, preprocessing the information data, comparing the preprocessed data with a threshold, and obtaining a comparison result, wherein the comparison result includes a qualified comparison and a unqualified comparison, specifically include: S201, collecting sensor equipment information data, the information data including power, current, voltage, temperature, humidity, vibration, pressure and flow rate parameters, and pre-processing the information data.

[0031] In this step, sensor equipment information data is collected, and the information data includes multiple parameters such as power, current, voltage, temperature, humidity, vibration, pressure and flow. Power reflects the energy consumption or workload of the equipment, current reflects the current consumption of the equipment, voltage is used to determine the voltage state of the equipment operation, temperature reflects the thermal state of the equipment, too high or too low temperature may indicate equipment abnormality, humidity is for electrical equipment or environmental monitoring, too high humidity may cause failure, vibration is monitored through vibration, abnormal vibration usually indicates mechanical structure problems of the equipment, pressure is used for liquid or gas equipment, pressure fluctuations may indicate leakage or failure, flow is suitable for liquid and gas flow monitoring to ensure that the flow is maintained within the appropriate range.

[0032] S202, obtaining preprocessed data, wherein the preprocessing includes data cleaning, data interpolation, and obtaining an information data threshold, wherein the data threshold is a safety range of the data.

[0033] In this step, preprocessed data is obtained. Preprocessing includes data cleaning, data interpolation, and obtaining information data thresholds. The goal of data cleaning is to remove invalid, inaccurate, or incomplete raw data to ensure the accuracy of analysis and subsequent processing, including removing outliers, taking out duplicate data, and unifying the format. Outliers are identified and removed according to preset rules. During the data collection process, duplicate records may appear. Duplicate data is removed to ensure that all data formats are consistent. Sometimes due to equipment failure, communication loss or other reasons, data may be missing. Data interpolation is used to fill these missing values, using the average of the previous and next data points to estimate the missing data; Get information data thresholds. The thresholds represent the safe range of equipment operation. Based on a large amount of historical data, the safety threshold is set by calculating the mean of the data. For example, a normal range can be set as the mean ± 2 times the standard deviation.

[0034] S203, comparing the preprocessed data with the threshold value to obtain a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison.

[0035] In this step, the preprocessed data is compared with the threshold. If the upper threshold exceeds this value, it means that the device may be overloaded, malfunction or have reduced efficiency. If the lower threshold is lower than this value, it means that the device cannot work properly and may have performance degradation. Check whether each data point exceeds the set safety threshold range; Get the comparison results. If the data is within the safe range, it is considered qualified, indicating that the equipment is operating normally. At this time, you can continue to monitor and record the data for subsequent analysis. If the data exceeds the safe range, it is considered unqualified, indicating that the equipment may be abnormal and needs further investigation, indicating that the equipment may be faulty or abnormal. At this time, it is necessary to trigger an alarm and conduct further fault analysis, maintenance or shutdown inspection.

[0036] like Figure 3 As shown, as a preferred embodiment of the present invention, the steps of obtaining the cause of the unqualified data, obtaining the treatment plan, comparing the treatment plan according to the cause, scoring according to the treatment result, and determining whether to change the treatment plan according to the scoring result specifically include: S301, if it is determined that the comparison is unqualified, the unqualified comparison means that the preprocessed data exceeds the data threshold, and the unqualified data is obtained based on the cause of the unqualified data, which includes sensor problems, equipment failures and data collection errors.

[0037] In this step, if the comparison is judged as unqualified, unqualified data refers to data that fails to meet the expected requirements when the pre-processed data is compared with the set threshold. Such data usually triggers an alarm mechanism to indicate potential problems with the device; Obtaining unqualified data based on the reasons for the unqualified data, which include sensor problems, equipment failures, and data collection errors. Sensor failures include sensor damage or reduced accuracy, resulting in data distortion. Common reasons include hardware failures, connection problems, improper sensor calibration, etc. Equipment failure includes aging, damage or unstable operation of the equipment itself, which may cause performance fluctuations beyond the normal working range; data collection errors include those during the data transmission process, such as network problems, signal loss, etc.

[0038] S302, obtaining a treatment plan, wherein the treatment plan is used to solve the cause of the problem, and the treatment plan is compared with the cause of the problem to obtain a treatment result.

[0039] In this step, a treatment plan is obtained. The treatment plan should be designed for different causes in order to effectively solve the problem. The type of treatment plan mainly depends on the nature of the equipment failure or external environmental factors, such as equipment failure repair, parameter adjustment and maintenance or replacement of equipment; Compare the treatment plan with the cause. Once the specific cause of the problem is identified, through data analysis and equipment inspection, it is necessary to compare these causes with the corresponding treatment plan. The purpose of the comparison process is to ensure that the selected treatment plan can accurately solve the root problem that produces unqualified data; For example, the comparison results show that the current value of the device exceeds the set safety threshold. Through analysis, it is found that the problem may be caused by the following reasons: Current sensor failure: The sensor failure causes the reading to be high; The equipment is overloaded: The equipment itself may cause the current to exceed the standard when it is overloaded; Electrical system connection problem: There may be problems such as poor contact or short circuit of the wires; Design corresponding treatment plans respectively: Sensor failure: Replace or repair the faulty sensor. Equipment overload: Adjust the load to reduce equipment stress. Electrical system problem: Check the electrical connections and repair any faulty parts.

[0040] After executing the treatment plan, it is necessary to monitor the treatment process and check its results. This is usually verified by real-time data collection and comparison to threshold values.

[0041] S303, scoring is performed according to the processing results, the scoring results are obtained, and the scoring results are compared with the preset classifications, wherein the preset classifications are used to judge the quality of the processing, and whether to change the processing scheme is determined according to the scoring results.

[0042] In this step, the processing results are scored and the scoring criteria are determined. The scoring criteria include data recovery degree, processing time, resource consumption, stability and economy. Each criterion is scored using a quantitative scoring method, set to 1-5 points, 1 for the worst and 5 for the best. After each score is scored, the average or weighted calculation is taken to obtain the total score; Get the scoring results and compare them with the preset classification, for example, excellent: 80 points and above, the solution is very effective and the device is quickly restored to normal; good: 60-79 points, the solution is effective, but there is room for improvement, such as a longer recovery time or higher resource consumption; medium: 40-59 points, the solution is effective, but there are certain problems, such as incomplete data recovery, and some devices may still have problems; poor: below 40 points, the solution fails to effectively solve the problem, and the device is still in an abnormal state; Determine whether to change the treatment plan based on the scoring results. If the score is high, it means that the treatment plan is effective and can be standardized and applied to other devices or scenarios. If the score is medium, the defects of the treatment plan can be analyzed, such as long processing time, high resource consumption, etc., and optimized and improved. If the score is below 40 points, the treatment plan fails to effectively solve the problem and the equipment is still in an abnormal state.

[0043] like Figure 4 As shown, as a preferred embodiment of the present invention, the steps of performing visualization processing according to the processed data, predicting the effect according to the visualization processing result, and judging whether the subsequent data meets the planning according to the prediction effect specifically include: S401, obtaining qualified data and processed unqualified data to obtain processed data, and performing visualization processing on the processed data, wherein the visualization processing is processing the data into a line graph.

[0044] In this step, qualified data and processed unqualified data are obtained. Qualified data refers to data that meets the set threshold requirements after preprocessing, which usually means that the device is in normal operation. Processed unqualified data refers to data that still does not meet the threshold after processing, which may indicate that the device has potential failures or unstable operation; The purpose of visualization is to convert raw data into easy-to-understand charts or graphs, so that decision makers and maintenance personnel can observe the status of equipment more intuitively. Line charts are used to display data such as power, current, and voltage that change over time, making it easier to observe trends and fluctuations. The visualized data is not only used to display the current status, but can also be used for predictions.

[0045] S402, predicting effects according to the visualization processing results, wherein the prediction effects include energy consumption, operation efficiency and management solutions, and sending the visualization results to the terminal.

[0046] In this step, the prediction effect is made based on the visualization processing results. The prediction effect includes energy consumption, operation efficiency and management plan. Energy consumption prediction is based on historical data trends and equipment load conditions to predict future energy consumption; operation efficiency prediction is to predict the health status and operation efficiency of the equipment by analyzing equipment efficiency data. Low efficiency may indicate equipment failure, aging or other problems; management plan prediction is based on data trends and prediction results to formulate future management plans; Send visualization results to the terminal and display them to the terminal equipment or personnel to ensure timely action. The staff can quickly determine which equipment needs attention and which energy efficiency optimization solutions can be implemented, and take timely action.

[0047] S403, obtaining a plan, where the plan is expected data for the future, and judging whether subsequent data meets the plan based on the prediction effect.

[0048] In this step, the plan is obtained. The plan is a description of the future state based on business goals or equipment performance requirements, including energy consumption expectations, operating efficiency expectations, and production capacity expectations. The subsequent data is judged based on the prediction results to determine whether it meets the plan. By comparing the prediction results with the planning goals, it can be judged whether the equipment operation meets the expected goals. If the prediction results exceed the planning range, measures need to be taken to make adjustments.

[0049] like Figure 5 As shown, a real-time data acquisition system for equipment monitoring provided by an embodiment of the present invention includes: The sensor module 100 is used to obtain demand data, wherein the demand data includes energy efficiency demand, and select corresponding sensor equipment according to the demand data.

[0050] In this system, the sensor module 100 obtains demand data, which is the basis for determining sensor selection and configuration. The demand data includes energy efficiency requirements, which include target energy efficiency levels and energy use optimization targets. The corresponding sensor device is selected according to the demand data, and the energy efficiency index required by the target device is determined. According to the parameters mentioned in the demand data, select sensors that can meet the system requirements, including power metering instruments, temperature and humidity sensors, flow meters, pressure sensors and vibration sensors. By systematically analyzing the demand data and selecting appropriate sensor equipment, it can be ensured that the equipment monitoring data is accurate and meets the expected goals during the energy management and optimization process. This sensor selection based on demand analysis can ensure the accuracy and reliability of data collection, thereby providing support for energy efficiency optimization.

[0051] The preprocessing module 200 is used to collect information data of the sensor device, preprocess the information data, compare the preprocessed data with a threshold value, and obtain a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison.

[0052] In this system, the preprocessing module 200 collects sensor equipment information data, and collects energy consumption, power, energy efficiency and other data in real time through sensor equipment (such as power meters, current and voltage sensors). These devices will continuously collect data into the system. The data collected by the sensor is generally affected by noise, errors, lost data, etc., so some preprocessing is required; Compare preprocessed data with thresholds. After data preprocessing, the data needs to be compared with the set thresholds. The thresholds are usually determined by the equipment's operating standards, industry specifications, or historical data. The comparison results will reflect whether the data is within the normal range. Common results include data that meets the threshold range and exceeds the normal range. The comparison results include qualified and unqualified.

[0053] The processing solution module 300 is used to obtain the cause of the unqualified data, obtain the processing solution, compare the processing solution according to the cause, score according to the processing result, and determine whether to change the processing solution according to the scoring result.

[0054] In this system, the processing solution module 300 obtains the reasons for the generation of unqualified data. Unqualified data refers to data that fails to meet the expected requirements when the pre-processed data is compared with the set threshold. These data usually trigger an alarm mechanism to indicate potential problems of the equipment and obtain processing solutions. Common processing solutions include sensor calibration, sensor maintenance and equipment inspection; Compare the treatment plan with the cause. After determining the treatment plan, compare it with the specific cause to ensure the selected plan is targeted and effective. By matching it with the cause, ensure that the measures taken can effectively solve the problem; Scoring is done based on the treatment results. After the treatment plan is implemented, the treatment effect needs to be evaluated. A set of scoring criteria can be set to score the treatment results, and the decision on whether to change the treatment plan can be made based on the scoring results.

[0055] The prediction module 400 is used to perform visualization processing according to the processed data, predict the effect according to the visualization processing result, and judge whether the subsequent data meets the plan according to the prediction effect.

[0056] In this system, the prediction module 400 performs visualization processing based on the processed data. The purpose of the visualization processing is to convert the processed data into easy-to-understand graphics to help users intuitively understand the change trend and current status of the data. The line graph is used to show the trend of data changes over time, such as power, voltage and temperature; According to the results of visualization, the prediction effect can be carried out. After data visualization, a certain prediction algorithm can be used to predict the operation status of the equipment in the future and evaluate whether the equipment will enter an unqualified state. Determine whether subsequent data meets the plan based on the prediction results. Determine whether future data meets the set planning requirements based on the prediction results, combined with the performance requirements and safe operation parameters of the equipment.

[0057] like Figure 6 As shown, as a preferred embodiment of the present invention, the preprocessing module 200 includes: The information data unit 201 is used to collect information data of sensor equipment, wherein the information data includes multiple parameters such as power, current, voltage, temperature, humidity, vibration, pressure and flow, and pre-process the information data.

[0058] In this module, the information data unit 201 collects information data of sensor equipment, and the information data includes multiple parameters such as power, current, voltage, temperature, humidity, vibration, pressure and flow. Power reflects the energy consumption or workload of the equipment, current reflects the current consumption of the equipment, voltage is used to determine the voltage state of the equipment operation, temperature reflects the thermal state of the equipment, too high or too low temperature may indicate equipment abnormality, humidity is for electrical equipment or environmental monitoring, too high humidity may cause failure, vibration is monitored through vibration, abnormal vibration usually indicates mechanical structure problems of the equipment, pressure is used for equipment such as liquid or gas, pressure fluctuations may indicate leakage or failure, flow is suitable for liquid and gas flow monitoring to ensure that the flow is maintained within the appropriate range.

[0059] The preprocessing unit 202 is used to obtain preprocessed data, wherein the preprocessing includes data cleaning, data interpolation, and obtaining an information data threshold, wherein the data threshold is a safety range of the data.

[0060] In this module, the preprocessing unit 202 obtains preprocessed data. Preprocessing includes data cleaning, data interpolation, and obtaining information data thresholds. The goal of data cleaning is to remove invalid, inaccurate or incomplete original data to ensure the accuracy of analysis and subsequent processing, including removing outliers, taking out duplicate data and unifying the format. Outliers are identified and removed according to preset rules. In the process of data collection, duplicate records may appear. Duplicate data is removed to ensure that all data formats are consistent. Sometimes due to equipment failure, communication loss or other reasons, data may be missing. Data interpolation is used to fill these missing values, using the average of the previous and next data points to estimate the missing data; Get information data thresholds. The thresholds represent the safe range of equipment operation. Based on a large amount of historical data, the safety threshold is set by calculating the mean of the data. For example, a normal range can be set as the mean ± 2 times the standard deviation.

[0061] The comparison unit 203 is used to compare the preprocessed data with the threshold value to obtain a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison.

[0062] In this module, the comparison unit 203 compares the preprocessed data with the threshold value. The upper threshold value exceeds this value, indicating that the device may be overloaded, malfunctioning, or have reduced efficiency. The lower threshold value indicates that the device cannot work properly and may have performance degradation when it is below this value. It checks whether each data point exceeds the set safety threshold range; Get the comparison results. If the data is within the safe range, it is considered qualified, indicating that the equipment is operating normally. At this time, you can continue to monitor and record the data for subsequent analysis. If the data exceeds the safe range, it is considered unqualified, indicating that the equipment may be abnormal and needs further investigation, indicating that the equipment may be faulty or abnormal. At this time, it is necessary to trigger an alarm and conduct further fault analysis, maintenance or shutdown inspection.

[0063] like Figure 7 As shown, as a preferred embodiment of the present invention, the processing scheme module 300 includes: The reason unit 301 is used to obtain the cause of the unqualified data if it is determined that the comparison is unqualified, and the unqualified comparison means that the preprocessed data exceeds the data threshold. The cause includes sensor problems, equipment failures and data collection errors.

[0064] In this module, if the reason unit 301 determines that the comparison is unqualified, the unqualified data refers to the data that fails to meet the expected requirements when the pre-processed data is compared with the set threshold. Such data usually triggers an alarm mechanism to indicate potential problems of the device; Obtaining unqualified data based on the reasons for the unqualified data, which include sensor problems, equipment failures, and data collection errors. Sensor failures include sensor damage or reduced accuracy, resulting in data distortion. Common reasons include hardware failures, connection problems, improper sensor calibration, etc. Equipment failure includes aging, damage or unstable operation of the equipment itself, which may cause performance fluctuations and exceed the normal working range; data collection errors include those during the data transmission process, such as network problems, signal loss, etc.

[0065] The processing solution unit 302 is used to obtain a processing solution, wherein the processing solution is used to solve the cause of the problem, and compare the processing solution with the cause of the problem to obtain a processing result.

[0066] In this module, the processing plan unit 302 obtains the processing plan, which should be designed for different causes in order to effectively solve the problem. The type of processing plan mainly depends on the nature of the equipment failure or external environmental factors, such as equipment failure repair, parameter adjustment and maintenance or replacement of equipment; Compare the treatment plan with the cause. Once the specific cause of the problem is identified, through data analysis and equipment inspection, it is necessary to compare these causes with the corresponding treatment plan. The purpose of the comparison process is to ensure that the selected treatment plan can accurately solve the root problem that produces unqualified data; For example, the comparison results show that the current value of the device exceeds the set safety threshold. Through analysis, it is found that the problem may be caused by the following reasons: Current sensor failure: The sensor failure causes the reading to be high; The equipment is overloaded: The equipment itself may cause the current to exceed the standard when it is overloaded; Electrical system connection problem: There may be problems such as poor contact or short circuit of the wires; Design corresponding treatment plans respectively: Sensor failure: Replace or repair the faulty sensor. Equipment overload: Adjust the load to reduce equipment stress. Electrical system problem: Check the electrical connections and repair any faulty parts.

[0067] After executing the treatment plan, it is necessary to monitor the treatment process and check its results. This is usually verified by real-time data collection and comparison to threshold values.

[0068] The scoring unit 303 is used to score according to the processing results, obtain the scoring results, compare the scoring results with the preset classification, the preset classification is used to judge the quality of the processing, and determine whether to change the processing plan according to the scoring results.

[0069] In this module, the scoring unit 303 scores according to the processing results and determines the scoring criteria, which include data recovery degree, processing time, resource consumption, stability and economy. Each criterion is scored using a quantitative scoring method, which is set to 1-5 points, with 1 being the worst and 5 being the best. After each scoring, the average or weighted calculation is performed to obtain the total score; Get the scoring results and compare them with the preset classification, for example, excellent: 80 points and above, the solution is very effective and the device is quickly restored to normal; good: 60-79 points, the solution is effective, but there is room for improvement, such as a longer recovery time or higher resource consumption; medium: 40-59 points, the solution is effective, but there are certain problems, such as incomplete data recovery, and some devices may still have problems; poor: below 40 points, the solution fails to effectively solve the problem, and the device is still in an abnormal state; Determine whether to change the treatment plan based on the scoring results. If the score is high, it means that the treatment plan is effective and can be standardized and applied to other devices or scenarios. If the score is medium, the defects of the treatment plan can be analyzed, such as long processing time, high resource consumption, etc., and optimized and improved. If the score is below 40 points, the treatment plan fails to effectively solve the problem and the equipment is still in an abnormal state.

[0070] like Figure 8 As shown, as a preferred embodiment of the present invention, the prediction module 400 includes: The visualization unit 401 is used to obtain qualified data and processed unqualified data, obtain processed data, and perform visualization processing based on the processed data. The visualization processing is to process the data into a line graph.

[0071] In this module, the visualization unit 401 obtains qualified data and processed unqualified data. The qualified data refers to data that meets the set threshold requirements after preprocessing, which usually means that the device is in normal operation. The processed unqualified data refers to data that still does not meet the threshold after processing, which may indicate that the device has potential failures or unstable operation. The purpose of visualization is to convert raw data into easy-to-understand charts or graphs, so that decision makers and maintenance personnel can observe the status of equipment more intuitively. Line charts are used to display data such as power, current, and voltage that change over time, making it easier to observe trends and fluctuations. The visualized data is not only used to display the current status, but can also be used for predictions.

[0072] The prediction unit 402 is used to predict the effect according to the visualization processing result, and the prediction effect includes energy consumption, operation efficiency and management plan, and send the visualization result to the terminal.

[0073] In this module, the prediction unit 402 predicts the effect based on the visualization processing results. The prediction effects include energy consumption, operating efficiency and management solutions. Energy consumption prediction is based on historical data trends and equipment load conditions to predict future energy consumption; operating efficiency prediction is to predict the health status and operating efficiency of the equipment by analyzing equipment efficiency data. Low efficiency may indicate equipment failure, aging or other problems; management solution prediction is based on data trends and prediction results to formulate future management solutions; Send visualization results to the terminal and display them to the terminal equipment or personnel to ensure timely action. The staff can quickly determine which equipment needs attention and which energy efficiency optimization solutions can be implemented, and take timely action.

[0074] The planning unit 403 is used to obtain a plan, where the plan is expected data for the future, and to determine whether subsequent data meets the plan based on the prediction effect.

[0075] In this module, the planning unit 403 obtains the plan, which is a description of the future state based on business goals or equipment performance requirements, including energy consumption expectations, operating efficiency expectations, and production capacity expectations. The subsequent data is judged whether it meets the plan based on the prediction effect. By comparing the prediction effect with the planning target, it can be judged whether the equipment operation meets the expected target. If the prediction effect exceeds the planning range, measures need to be taken to make adjustments.

[0076] In one embodiment, a computer device is provided, the computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented: Demand data is acquired, wherein the demand data includes energy efficiency demand, and corresponding sensor equipment is selected according to the demand data.

[0077] Collecting sensor equipment information data, preprocessing the information data, comparing the preprocessed data with a threshold value, and obtaining a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison; Obtain the cause of the unqualified data and the treatment plan, compare the treatment plan with the cause, score the treatment results, and determine whether to change the treatment plan based on the scoring results; Perform visualization based on the processed data, make predictions based on the visualization results, and determine whether subsequent data meets the plan based on the prediction results.

[0078] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the processor performs the following steps: Demand data is acquired, wherein the demand data includes energy efficiency demand, and corresponding sensor equipment is selected according to the demand data.

[0079] Collecting sensor equipment information data, preprocessing the information data, comparing the preprocessed data with a threshold value, and obtaining a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison; Obtain the cause of the unqualified data and the treatment plan, compare the treatment plan with the cause, score the treatment results, and determine whether to change the treatment plan based on the scoring results; Perform visualization based on the processed data, make predictions based on the visualization results, and determine whether subsequent data meets the plan based on the prediction results.

[0080] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps. Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0081] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0082] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0083] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for collecting real-time data for equipment monitoring, characterized in that: The method comprises: Acquire demand data, wherein the demand data includes energy efficiency demand, and select corresponding sensor equipment according to the demand data; Collecting sensor equipment information data, preprocessing the information data, comparing the preprocessed data with a threshold value, and obtaining a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison; Obtain the cause of the unqualified data and the treatment plan, compare the treatment plan with the cause, score the treatment results, and determine whether to change the treatment plan based on the scoring results; Perform visualization based on the processed data, make predictions based on the visualization results, and determine whether subsequent data meets the plan based on the prediction results.

2. A method for real-time data collection for equipment monitoring according to claim 1, characterized in that: The steps of collecting sensor device information data, preprocessing the information data, comparing the preprocessed data with a threshold, and obtaining a comparison result, wherein the comparison result includes a qualified comparison and a unqualified comparison, specifically include: Collect sensor equipment information data, including power, current, voltage, temperature, humidity, vibration, pressure and flow rate parameters, and pre-process the information data; Obtaining preprocessed data, wherein the preprocessing includes data cleaning, data interpolation, and obtaining an information data threshold, wherein the data threshold is a safety range of the data; The preprocessed data is compared with the threshold value to obtain a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison.

3. The method for real-time data collection for equipment monitoring according to claim 1, characterized in that: The steps of obtaining the cause of the unqualified data, obtaining a treatment plan, comparing the treatment plan according to the cause, scoring according to the treatment result, and determining whether to change the treatment plan according to the scoring result specifically include: If the comparison is determined to be unqualified, the unqualified comparison means that the preprocessed data exceeds the data threshold, and the unqualified is obtained based on the cause of the unqualified data, which includes sensor problems, equipment failures and data collection errors; Obtaining a treatment plan, wherein the treatment plan is used to solve the cause of the problem, comparing the treatment plan with the cause of the problem, and obtaining a treatment result; Scoring is performed according to the processing results, and the scoring results are obtained. The scoring results are compared with preset grades, and the preset grades are used to judge the quality of the processing. It is determined whether to change the processing plan according to the scoring results.

4. The method for real-time data collection for equipment monitoring according to claim 1, characterized in that: The steps of performing visualization processing according to the processed data, predicting the effect according to the visualization processing result, and judging whether the subsequent data meets the planning according to the prediction effect specifically include: Acquire qualified data and processed unqualified data to obtain processed data, and perform visualization processing according to the processed data, wherein the visualization processing is processing the data into a line graph; Predicting effects based on the visualization results, including energy consumption, operating efficiency and management solutions, and sending the visualization results to the terminal; Obtain a plan, which is expected data for the future, and determine whether subsequent data meets the plan based on the predicted results.

5. The method for real-time data collection for equipment monitoring according to claim 1, characterized in that: The sensor equipment includes an electric power meter, a temperature and humidity sensor, a flow meter, a pressure sensor and a vibration sensor.

6. A real-time data acquisition system for equipment monitoring, characterized in that: The system comprises: The sensor module obtains demand data, wherein the demand data includes energy efficiency demand, and selects corresponding sensor equipment according to the demand data; A preprocessing module collects sensor device information data, preprocesses the information data, compares the preprocessed data with a threshold value, and obtains a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison; The processing scheme module obtains the causes of unqualified data and obtains the processing scheme, compares the processing scheme according to the causes, scores the processing results, and determines whether to change the processing scheme according to the scoring results; The prediction module performs visualization based on the processed data, predicts the effect based on the visualization results, and determines whether the subsequent data meets the plan based on the prediction results.

7. The real-time data acquisition system for equipment monitoring according to claim 6, characterized in that: The preprocessing module comprises: An information data unit collects information data of sensor equipment, including multiple parameters such as power, current, voltage, temperature, humidity, vibration, pressure and flow, and pre-processes the information data; A preprocessing unit obtains preprocessed data, wherein the preprocessing includes data cleaning, data interpolation, and obtaining an information data threshold, wherein the data threshold is a safety range of the data; The comparison unit compares the preprocessed data with the threshold value to obtain a comparison result, wherein the comparison result includes a qualified comparison and a failed comparison.

8. The real-time data acquisition system for equipment monitoring according to claim 7, characterized in that: The processing scheme module includes: The reason unit, if it is determined that the comparison is unqualified, the unqualified comparison means that the preprocessed data exceeds the data threshold, obtains the cause of the unqualified data, and the cause includes sensor problems, equipment failures and data collection errors; A processing solution unit, which obtains a processing solution, wherein the processing solution is used to solve the cause of the problem, compares the processing solution with the cause of the problem, and obtains a processing result; The scoring unit scores according to the processing results, obtains the scoring results, compares the scoring results with the preset classification, and the preset classification is used to judge the quality of the processing, and determines whether to change the processing plan according to the scoring results.

9. The real-time data acquisition system for equipment monitoring according to claim 8, characterized in that: The prediction module comprises: A visualization unit, which obtains qualified data and processed unqualified data to obtain processed data, and performs visualization processing according to the processed data, wherein the visualization processing is processing the data into a line graph; A prediction unit, which predicts effects according to the visualization processing results, wherein the prediction effects include energy consumption, operation efficiency and management solutions, and sends the visualization results to the terminal; The planning unit obtains a plan, which is expected data for the future, and determines whether subsequent data meets the plan based on the prediction effect.

10. The real-time data acquisition system for equipment monitoring according to claim 9, characterized in that: The sensor equipment includes an electric power meter, a temperature and humidity sensor, a flow meter, a pressure sensor and a vibration sensor.

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