A current sensor data acquisition and analysis system, method and storage medium

Abstract

The present invention relates to a current sensor data acquisition and analysis system, method and storage medium. The present invention includes a current sensor unit, a data output unit, an intelligent analysis engine unit, an operating condition normative analysis unit and a remote monitoring service end. The present invention uses a current sensor unit to accurately capture current signals, and the data output unit sends the collected current data to the intelligent engine analysis unit. The intelligent analysis engine unit conducts in-depth mining of the collected current data and automatically identifies abnormal conditions and predicts potential faults, thereby providing strong technical support for the safe and stable operation of the power system. The operating condition normative analysis unit conducts real-time analysis of the operating conditions of the current sensor unit, thereby ensuring the safe and stable operation of the current sensor unit and maintaining the accuracy of current detection, thereby meeting the needs of intelligent management of power detection.

Description

Technical Field

[0001] The present invention relates to the technical field of current sensor monitoring, and in particular to a current sensor data acquisition and analysis system, method and storage medium. Background Art

[0002] The current sensor can sense the information of the measured current and transform the sensed information into an electrical signal or other required information output in accordance with certain rules. As a key component for monitoring the health status of the power system, the accuracy and real-time performance of the current sensor's data collection and analysis directly affect the reliability and safety of the system.

[0003] At present, when current sensors are used to monitor the current of power lines, it is often difficult to accurately judge the standardization of the operating conditions of the current sensors while realizing current collection, analysis and early warning. In addition, when judging the standardization of the operating conditions of the current sensors, it is impossible to accurately assess the urgency of the inspection and calibration of the current sensors, which is not conducive to ensuring the efficient and smooth operation of the current sensors and realizing the needs of intelligent management of power detection.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The object of the present invention is to provide a current sensor data acquisition and analysis system, method and storage medium to solve the technical defects proposed by the background technology.

[0006] To achieve the above-mentioned object, the present invention provides the following technical solutions: a current sensor data acquisition and analysis system, comprising a current sensor unit, a data output unit, an intelligent analysis engine unit, an operating condition normative analysis unit and a remote monitoring service end;

[0007] The current sensor unit uses a high-sensitivity, wide-measuring-range, and low-power current sensor designed based on the Hall effect or magnetoresistive effect principle to accurately capture the current signal; the data output unit is connected to the current sensor unit, responsible for collecting the current data output by the current sensor unit, and sending the collected current data to the intelligent engine analysis unit;

[0008] The intelligent analysis engine unit has built-in machine learning algorithms and data analysis models to conduct in-depth mining of the collected current data, automatically identify abnormal conditions, predict potential faults, and provide optimization suggestions, and send the analysis results to the remote monitoring server; the operating condition normative analysis unit performs real-time analysis of the operating conditions of the current sensor unit, generates a high-specification operating condition signal or a low-specification operating condition signal through analysis, and sends the high-specification operating condition signal or the low-specification operating condition signal to the remote monitoring server. When the remote monitoring server receives the low-specification operating condition signal, it will take corresponding regulatory measures.

[0009] Furthermore, the specific analysis process of the operating condition normative analysis unit includes:

[0010] A curve graph of the supply voltage of the current sensor unit within a unit time is obtained, the variance of all the supply voltage values ​​within a unit time is calculated to obtain a supply voltage fluctuation coefficient, and the duration of the supply voltage deviation from the corresponding specified range within a unit time is marked as the supply voltage deviation coefficient;

[0011] The power supply voltage fluctuation coefficient and the power supply voltage deviation coefficient are numerically compared with the preset power supply voltage fluctuation coefficient threshold and the preset power supply voltage deviation coefficient threshold respectively. If the power supply voltage fluctuation coefficient or the power supply voltage deviation coefficient exceeds the corresponding preset threshold, a low specification signal of the operating condition is generated.

[0012] Further, if the power supply voltage fluctuation coefficient and the power supply voltage deviation coefficient do not exceed the corresponding preset threshold values, the temperature data of the environment in which the current sensor unit is located is collected, the temperature data is calculated as a difference with the median of the preset suitable temperature data range, and the absolute value is taken to obtain the working temperature coefficient, and the magnetic field strength, humidity and dust concentration around the current sensor unit are collected and marked as the working magnetic field coefficient, working humidity coefficient and working pollution coefficient respectively, and the vibration impact amplitude to which the current sensor unit is subjected is collected and marked as the working impact coefficient;

[0013] Assigning corresponding preset weight coefficients to the working temperature coefficient, working magnetic field coefficient, working humidity coefficient, working pollution coefficient and working impact coefficient respectively, multiplying the working temperature coefficient, working magnetic field coefficient, working humidity coefficient, working pollution coefficient and working impact coefficient by the corresponding preset weight coefficients respectively, and marking the sum of the five groups of multiplication results as the normative real-time coefficient;

[0014] Compare the normative real-time coefficient with the preset normative real-time coefficient threshold, if the normative real-time coefficient exceeds the preset normative real-time coefficient threshold, mark the corresponding normative real-time coefficient as a normative abnormal value; obtain the number of normative abnormal coefficients per unit time and mark them as normative abnormal values, and mark the average value and maximum value of all normative real-time coefficients per unit time as normative performance value and normative amplitude value respectively;

[0015] The compliance negative coefficient is obtained by numerically calculating the normative anomaly value, the normative performance value and the normative table amplitude, and the compliance negative coefficient is numerically compared with the preset compliance negative coefficient threshold. If the compliance negative coefficient exceeds the preset compliance negative coefficient threshold, a low-normative signal for operating conditions is generated; if the compliance negative coefficient does not exceed the preset compliance negative coefficient threshold, a high-normative signal for operating conditions is generated.

[0016] Furthermore, the operating condition normative analysis unit is communicatively connected to the calibration prediction analysis unit, and the operating condition normative analysis unit sends the operating condition high specification signal to the calibration prediction analysis unit. When the calibration prediction analysis unit receives the operating condition high specification signal, it performs calibration prediction analysis, determines through analysis whether to generate a calibration alarm signal, and sends the calibration alarm signal to the remote monitoring server.

[0017] Furthermore, the specific analysis process of calibration prediction analysis is as follows:

[0018] Obtaining the acquisition abnormality probability coefficient, comparing the acquisition abnormality probability coefficient with a preset acquisition abnormality probability coefficient threshold, and generating a calibration alarm signal if the acquisition abnormality probability coefficient exceeds the preset acquisition abnormality probability coefficient threshold;

[0019] If the acquisition abnormality probability coefficient does not exceed the preset acquisition abnormality probability coefficient threshold, the time when the current sensor unit was calibrated last is obtained, the time difference between the current time and the time when the current sensor unit was calibrated last is calculated to obtain the calibration interval duration, the calibration interval duration is numerically compared with the preset calibration interval duration threshold, and if the calibration interval duration exceeds the preset calibration interval duration threshold, a calibration alarm signal is generated;

[0020] If the calibration interval duration does not exceed the preset calibration interval duration threshold, the number of times the operating condition low specification signal is generated within the calibration interval duration is obtained and marked as the operating low specification detection value, and the interval duration between the production date of the current sensor unit and the current date is obtained and marked as the production time value, and several groups of preset production time value ranges are set in advance, and each group of preset production time value ranges corresponds to a group of life factors, and the life factors of the preset production time value ranges containing the corresponding production time values ​​are marked as target factors;

[0021] The calibration prediction value is obtained by numerically calculating the target factor, the calibration interval duration and the running low specification detection value, and the calibration prediction value is numerically compared with the preset calibration prediction threshold. If the calibration prediction value exceeds the preset calibration prediction threshold, a calibration alarm signal is generated.

[0022] Furthermore, the calibration prediction and analysis unit is communicatively connected to the acquisition abnormality probability analysis unit. When the acquisition abnormality probability analysis unit receives the operation high specification signal, it analyzes the acquisition abnormality probability of the current sensor unit, obtains the acquisition abnormality probability coefficient through analysis, and sends the acquisition abnormality probability coefficient to the calibration prediction and analysis unit.

[0023] Furthermore, the analysis and acquisition method of the acquisition abnormal probability coefficient is as follows:

[0024] The moment when the current sensor unit captures the current signal is obtained and marked as the capture moment, the interval between two adjacent groups of capture moments is marked as the capture interval, the capture interval is numerically compared with a preset capture interval threshold, and if the capture interval exceeds the preset capture interval threshold, the corresponding capture interval is marked as an abnormal capture interval;

[0025] The number of capture interval durations per unit time is obtained and marked as an abnormal capture detection value, and the average of all capture interval durations per unit time is calculated to obtain a capture interval detection value; and the number of occurrences in which the current data collected per unit time is not within the preset reasonable current data range is obtained and marked as a capture high deviation value, and the acquisition abnormality probability coefficient is obtained by numerically calculating the abnormal capture detection value, the capture interval detection value and the capture high deviation value.

[0026] Further, the calibration prediction and analysis unit is communicatively connected to the calibration push management module, and the calibration prediction and analysis unit sends the calibration alarm signal to the calibration push management module. When the calibration push management module receives the calibration alarm signal, it performs calibration push analysis, obtains all managers on duty, collects the positions of corresponding managers, calculates the distance between the positions of corresponding managers and the positions of the current sensor units to obtain calibration values, sorts all managers in ascending order of the calibration values, and marks the managers in the first half as close-range personnel;

[0027] The number of times the corresponding close personnel calibrated the current sensor in the historical stage is collected and marked as the calibration frequency value, and the average value of all single calibration durations of the corresponding close personnel calibrating the current sensor in the historical stage is marked as the calibration effectiveness value, and the average value of all single occurrence frequencies of incorrect operations when the corresponding close personnel calibrated the current sensor in the historical stage is marked as the calibration error value;

[0028] The calibration prediction coefficient is obtained by numerically calculating the calibration distance value, calibration frequency value, calibration efficiency value and calibration error value. All close-range personnel are sorted in ascending order according to the values ​​of the calibration prediction coefficient. The close-range personnel ranked first is marked as the push personnel, and the push personnel are sent to the remote monitoring server. The remote monitoring server sends the calibration alarm signal to the smart terminal of the push personnel.

[0029] The present invention further proposes a current sensor data acquisition and analysis method, comprising the following steps:

[0030] Step 1: The current sensor unit accurately captures the current signal;

[0031] Step 2: The data output unit collects the current data output by the current sensor unit, and sends the collected current data to the intelligent engine analysis unit;

[0032] Step 3: The intelligent analysis engine unit conducts in-depth mining of the collected current data, automatically identifies abnormal conditions and predicts potential faults, provides optimization suggestions, and sends the analysis results to the remote monitoring server;

[0033] Step 4: the operating condition normativeness analysis unit performs real-time analysis on the operating condition of the current sensor unit, generates an operating condition high normative signal or an operating condition low normative signal through analysis, and sends the operating condition high normative signal or the operating condition low normative signal to the remote monitoring server;

[0034] Step 5: The remote monitoring server displays the analysis results of the intelligent analysis engine unit and takes corresponding control measures when receiving a low-standard operating condition signal.

[0035] The present invention further proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned current sensor data collection and analysis method is implemented.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. In the present invention, the current signal is accurately captured by the current sensor unit, and the data output unit sends the collected current data to the intelligent engine analysis unit. The intelligent analysis engine unit deeply mines the collected current data and automatically identifies abnormal conditions and predicts potential faults, providing strong technical support for the safe and stable operation of the power system. The operating condition normative analysis unit accurately determines whether the operating conditions of the current sensor meet the requirements and makes reasonable control measures in time, which is conducive to ensuring the safe and stable operation of the current sensor unit and maintaining the accuracy of current detection, and realizing the needs of intelligent management of power detection;

[0038] 2. In the present invention, the operating condition high specification signal is sent to the calibration prediction analysis unit through the operating condition normative analysis unit. When the calibration prediction analysis unit receives the operating condition high specification signal, it performs calibration prediction analysis to determine whether to generate a calibration alarm signal. When the calibration alarm signal is generated, the calibration push analysis is performed to automatically and reasonably determine the most suitable manager for the current inspection and calibration operation, which is conducive to the efficient and smooth completion of the inspection and calibration operation, significantly reduces the difficulty of supervision on the current sensor, and further ensures the safe, stable and high-precision operation of the current sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] To facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0040] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0041] Figure 2 It is a system block diagram of Embodiment 2 and Embodiment 3 of the present invention;

[0042] Figure 3 This is a flow chart of the method of Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Embodiment 1: Figure 1 As shown, a current sensor data acquisition and analysis system proposed by the present invention includes a current sensor unit, a data output unit, an intelligent analysis engine unit, an operating condition normative analysis unit and a remote monitoring service end;

[0045] The current sensor unit uses a high-sensitivity, wide-measuring-range, and low-power current sensor designed based on the Hall effect or magnetoresistive effect principle to accurately capture the current signal. The data output unit is connected to the current sensor unit and is responsible for collecting the current data output by the current sensor unit and sending the collected current data to the intelligent engine analysis unit.

[0046] The intelligent analysis engine unit has built-in machine learning algorithms and data analysis models (such as anomaly detection algorithms, trend prediction models, etc.), which conduct in-depth mining of the collected current data (for example, through statistical analysis, spectrum analysis and trend prediction of current data, it can realize real-time monitoring of circuit status and fault warning), automatically identify abnormal status and predict potential faults and provide optimization suggestions, and send the analysis results to the remote monitoring server. By integrating high-precision sensing technology, high-speed data acquisition, intelligent analysis and remote monitoring functions, it provides strong technical support for the safe and stable operation of the power system.

[0047] The operating condition normative analysis unit analyzes the operating conditions of the current sensor unit in real time, generates an operating condition high-norm signal or an operating condition low-norm signal through analysis, and sends the operating condition high-norm signal or the operating condition low-norm signal to the remote monitoring server. When the remote monitoring server receives the operating condition low-norm signal, it takes corresponding control measures, such as adjusting the temperature and humidity, to ensure the safe and stable operation of the current sensor unit and maintain the current detection accuracy. The specific analysis process of the operating condition normative analysis unit is as follows:

[0048] A curve graph of the supply voltage of the current sensor unit within a unit time is obtained, the variance of all the supply voltage values ​​within a unit time is calculated to obtain a supply voltage fluctuation coefficient, and the duration of the supply voltage deviation from the corresponding specified range within a unit time is marked as the supply voltage deviation coefficient;

[0049] Compare the power supply voltage fluctuation coefficient and the power supply voltage deviation coefficient with the preset power supply voltage fluctuation coefficient threshold and the preset power supply voltage deviation coefficient threshold respectively; if the power supply voltage fluctuation coefficient or the power supply voltage deviation coefficient exceeds the corresponding preset threshold, it indicates that the power supply voltage is unstable, which is not conducive to ensuring the accurate detection of the current sensor unit, and then generate an operating condition low specification signal;

[0050] If the power supply voltage fluctuation coefficient and the power supply voltage deviation coefficient do not exceed the corresponding preset threshold value, the temperature data of the environment in which the current sensor unit is located is collected (temperature is one of the key factors affecting the accuracy of the current sensor. Too high or too low temperature can easily cause the output signal of the current sensor to deviate, thereby affecting the measurement accuracy). The temperature data is calculated by difference with the median of the preset suitable temperature data range and the absolute value is taken to obtain the operating temperature coefficient;

[0051] The magnetic field strength around the current sensor unit (when the current sensor is in a strong magnetic field environment, its measurement error will increase significantly, or even fail to work properly), humidity (too high humidity can easily cause the electronic components inside the current sensor to get damp, thus affecting its performance) and dust concentration (dust and dirt can easily hinder the heat dissipation in the current sensor, causing the sensor to overheat and interfere with the measurement of the current sensor) are collected and marked as the working magnetic field coefficient, working humidity coefficient and working pollution coefficient respectively. The vibration impact amplitude to which the current sensor unit is subjected is collected (strong vibration and impact can easily cause the components inside the current sensor to loosen or be damaged, thus affecting its measurement accuracy) and marked as the working impact coefficient.

[0052] Assign corresponding preset weight coefficients with values ​​greater than zero to the working temperature coefficient, working magnetic field coefficient, working humidity coefficient, working pollution coefficient and working impact coefficient respectively. The greater the influence of the corresponding parameter on the analysis result, the greater the value of the preset weight coefficient matched therewith; multiply the working temperature coefficient, working magnetic field coefficient, working humidity coefficient, working pollution coefficient and working impact coefficient by the corresponding preset weight coefficient respectively, and mark the sum of the five groups of multiplication results as the normative real-time coefficient; it should be noted that the larger the value of the normative real-time coefficient is, the worse the real-time operating condition of the current sensor unit is, and the greater the degree of adverse impact on its measurement accuracy;

[0053] Compare the normative real-time coefficient with the preset normative real-time coefficient threshold. If the normative real-time coefficient exceeds the preset normative real-time coefficient threshold, indicating that the real-time operating condition of the current sensor unit is poor, the corresponding normative real-time coefficient is marked as a normative abnormal value. Obtain the number of normative abnormal coefficients per unit time and mark them as normative abnormal values, and mark the average value and maximum value of all normative real-time coefficients per unit time as normative performance value and normative table amplitude, respectively.

[0054] The compliance negative coefficient N is obtained by numerically calculating the normative abnormal value CY, the normative performance value WF and the normative table amplitude PM through the formula N=wq×CY+(hy×WF+kp×PM) / 2; wherein wq, hy, and kp are preset weight coefficients with values ​​greater than zero, and the larger the value of the compliance negative coefficient N is, the worse the overall operating condition of the current sensor unit in unit time is, and the less conducive it is to ensure the accuracy of its current detection result;

[0055] The compliance negative coefficient N is numerically compared with the preset compliance negative coefficient threshold. If the compliance negative coefficient N exceeds the preset compliance negative coefficient threshold, it indicates that the operating conditions of the current sensor unit per unit time are generally poor, which is not conducive to ensuring the accuracy of its current detection results, and a low-specification operating condition signal is generated; if the compliance negative coefficient N does not exceed the preset compliance negative coefficient threshold, it indicates that the operating conditions of the current sensor unit per unit time are generally good, which is conducive to ensuring the accuracy of its current detection results, and a high-specification operating condition signal is generated.

[0056] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the operating condition normative analysis unit is communicatively connected to the calibration prediction analysis unit, the operating condition normative analysis unit sends the operating condition high specification signal to the calibration prediction analysis unit, and the calibration prediction analysis unit performs calibration prediction analysis when receiving the operating condition high specification signal, and determines whether to generate a calibration alarm signal through analysis, and sends the calibration alarm signal to the remote monitoring service end;

[0057] When the remote monitoring server receives the calibration alarm signal, it issues a corresponding warning to remind the back-end personnel to arrange personnel to check and calibrate the current sensor unit in time, which significantly reduces the difficulty of supervising the current sensor and further ensures the safe, stable and high-precision operation of the current sensor. The specific analysis process of calibration prediction analysis is as follows:

[0058] Obtain the acquisition abnormality probability coefficient YP, compare the acquisition abnormality probability coefficient YP with the preset acquisition abnormality probability coefficient threshold, if the acquisition abnormality probability coefficient YP exceeds the preset acquisition abnormality probability coefficient threshold, it indicates that the acquisition operation of the current sensor is abnormal and needs to be checked or calibrated, then generate a calibration alarm signal;

[0059] If the acquisition abnormality probability coefficient YP does not exceed the preset acquisition abnormality probability coefficient threshold, the time of the last calibration for the current sensor unit is obtained, the time difference between the current time and the last calibration for the current sensor unit is calculated to obtain the calibration interval duration, the calibration interval duration is numerically compared with the preset calibration interval duration threshold, and if the calibration interval duration exceeds the preset calibration interval duration threshold, a calibration alarm signal is generated;

[0060] If the calibration interval does not exceed the preset calibration interval threshold, the number of times the operating condition low specification signal is generated within the calibration interval is obtained and marked as the operating low specification detection value, and the interval between the production date of the current sensor unit and the current date is obtained and marked as the production time value, and several groups of preset production time value ranges are set in advance, and each group of preset production time value ranges corresponds to a group of life factors, and the life factors of the preset production time value ranges containing the corresponding production time values ​​are marked as target factors β; it should be noted that the values ​​of the life factors are all positive numbers, and the larger the value of the corresponding preset production time value range is, the larger the value of the life factor that matches it is;

[0061] The target factor β, the calibration interval time TL and the running low specification detection value GP are numerically calculated by the formula QW=β×(rg×TL+uy×GP), wherein rg and uy are preset weight coefficients whose values ​​are greater than zero, and the larger the value of the calibration prediction value QW is, the more timely it is necessary to check and calibrate the current sensor unit; the calibration prediction value QW is numerically compared with the preset calibration prediction threshold value, and if the calibration prediction value QW exceeds the preset calibration prediction threshold value, it indicates that the current sensor unit needs to be checked and calibrated in time, and a calibration alarm signal is generated.

[0062] Furthermore, the calibration prediction analysis unit is communicatively connected to the acquisition abnormality probability analysis unit. When the acquisition abnormality probability analysis unit receives the high-specification operation signal, it analyzes the acquisition abnormality probability of the current sensor unit, obtains the acquisition abnormality probability coefficient through analysis, and sends the acquisition abnormality probability coefficient to the calibration prediction analysis unit, which can not only reasonably feedback the data acquisition abnormality probability status, but also provide data support for the analysis process of the calibration prediction analysis unit, ensuring the comprehensiveness of its analysis process and the accuracy of the calibration prediction results; the analysis and acquisition method of the acquisition abnormality probability coefficient is as follows:

[0063] The moment when the current sensor unit captures the current signal is obtained and marked as the capture moment, the interval between two adjacent groups of capture moments is marked as the capture interval, the capture interval is numerically compared with a preset capture interval threshold, and if the capture interval exceeds the preset capture interval threshold, the corresponding capture interval is marked as an abnormal capture interval;

[0064] The number of capture intervals per unit time is obtained and marked as an abnormal capture detection value, and the capture interval detection value is obtained by averaging all capture intervals per unit time; and the number of occurrences of the current data collected per unit time not being within the preset reasonable current data range (i.e., the collected current data obviously deviates from the normal range) is obtained and marked as a capture high deviation value;

[0065] The abnormal capture detection value TW, the capture interval detection value SW and the capture high deviation value HX are numerically calculated through the formula YP=up×TW+ts×SW+cq×HX to obtain the acquisition abnormality probability coefficient YP; wherein, up, ts, cq are preset weight coefficients whose values ​​are greater than zero, and the larger the value of the acquisition abnormality probability coefficient YP is, the more abnormal the acquisition operation of the current sensor unit per unit time is, and the more timely inspection and calibration are needed.

[0066] Embodiment 3: Figure 2 As shown, the difference between this embodiment and the first and second embodiments is that the calibration prediction and analysis unit is communicatively connected to the calibration push management module, the calibration prediction and analysis unit sends the calibration alarm signal to the calibration push management module, and the calibration push management module performs calibration push analysis upon receiving the calibration alarm signal, obtains all managers on duty, collects the positions of the corresponding managers, and calculates the distance between the positions of the corresponding managers and the positions of the current sensor units to obtain the calibration distance value;

[0067] Sort all managers in ascending order of calibration distance values, and mark the managers in the first half as close-range personnel;

[0068] The number of times the corresponding close personnel calibrated the current sensor in the historical stage is collected and marked as the calibration frequency value, and the average value of all single calibration durations of the corresponding close personnel calibrating the current sensor in the historical stage is marked as the calibration effectiveness value, and the average value of all single occurrence frequencies of incorrect operations when the corresponding close personnel calibrated the current sensor in the historical stage is marked as the calibration error value;

[0069] The calibration prediction coefficient XL is obtained by numerically calculating the calibration value SP, the calibration value HR, the calibration value QX and the calibration error value WS through the formula XL=(b×SP+e×QX+a×WS) / (t×HR+1); wherein b, t, e and a are preset proportional coefficients with values ​​greater than zero, and the smaller the value of the calibration prediction coefficient XL is, the more suitable it is for the corresponding close-range personnel to go and inspect and calibrate the current sensor;

[0070] All close-range personnel are sorted in ascending order according to the values ​​of the calibration prediction coefficients, and the close-range personnel ranked first are marked as push personnel, who are sent to the remote monitoring server. The remote monitoring server sends the calibration alarm signal to the push personnel's smart terminal, which can automatically and reasonably determine the most suitable management personnel for the current inspection and calibration operation, reduce the difficulty of arranging calibration personnel, and ensure the efficient and smooth completion of the calibration operation.

[0071] Embodiment 4: Figure 3As shown, the difference between this embodiment and the first, second and third embodiments is that the current sensor data acquisition and analysis method proposed in the present invention includes the following steps:

[0072] Step 1: The current sensor unit accurately captures the current signal;

[0073] Step 2: The data output unit collects the current data output by the current sensor unit, and sends the collected current data to the intelligent engine analysis unit;

[0074] Step 3: The intelligent analysis engine unit conducts in-depth mining of the collected current data, automatically identifies abnormal conditions and predicts potential faults, provides optimization suggestions, and sends the analysis results to the remote monitoring server;

[0075] Step 4: the operating condition normativeness analysis unit performs real-time analysis on the operating condition of the current sensor unit, generates an operating condition high normative signal or an operating condition low normative signal through analysis, and sends the operating condition high normative signal or the operating condition low normative signal to the remote monitoring server;

[0076] Step 5: The remote monitoring server displays the analysis results of the intelligent analysis engine unit and takes corresponding control measures when receiving a low-standard operating condition signal.

[0077] In addition, a computer-readable storage medium is also proposed, on which a computer program is stored. When the computer program is executed by the processor, the above-mentioned current sensor data acquisition and analysis method is implemented. Ordinary technicians in this field can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiments, and the aforementioned storage medium includes ROM, RAM, disk or CD-ROM and other media that can store program codes.

[0078] The working principle of the present invention is as follows: when in use, the current signal is accurately captured by the current sensor unit, and the data output unit sends the collected current data to the intelligent engine analysis unit. The intelligent analysis engine unit conducts in-depth mining of the collected current data and automatically identifies abnormal conditions and predicts potential faults. By integrating high-precision sensing technology, high-speed data acquisition, intelligent analysis and remote monitoring functions, it provides strong technical support for the safe and stable operation of the power system. The operating conditions of the current sensor unit are analyzed in real time by the operating condition normative analysis unit, and corresponding control measures are taken when a low-normative operating condition signal is generated to ensure the safe and stable operation of the current sensor unit, maintain the accuracy of current detection, and realize the needs of intelligent management of power detection.

[0079] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A current sensor data acquisition and analysis system, characterized in that: It includes a current sensor unit, a data output unit, an intelligent analysis engine unit, an operating condition normative analysis unit and a remote monitoring server; The current sensor unit accurately captures the current signal. The data output unit is connected to the current sensor unit and is responsible for collecting the current data output by the current sensor unit and sending the collected current data to the intelligent engine analysis unit. The intelligent analysis engine unit has built-in machine learning algorithms and data analysis models to conduct in-depth mining of the collected current data, automatically identify abnormal conditions and predict potential faults, and provide optimization suggestions, and send the analysis results to the remote monitoring server. The operating condition normative analysis unit analyzes the operating conditions of the current sensor unit in real time, and generates operating condition high-normative signals or operating condition low-normative signals through analysis. When the remote monitoring server receives the operating condition low-normative signal, it will take corresponding control measures. The specific analysis process of the operating condition normative analysis unit includes: A curve graph of the supply voltage of the current sensor unit within a unit time is obtained, the variance of all the supply voltage values ​​within a unit time is calculated to obtain a supply voltage fluctuation coefficient, and the duration of the supply voltage deviation from the corresponding specified range within a unit time is marked as the supply voltage deviation coefficient; if the supply voltage fluctuation coefficient or the supply voltage deviation coefficient exceeds the corresponding preset threshold, a low-specification operating condition signal is generated; If the power supply voltage fluctuation coefficient and the power supply voltage deviation coefficient do not exceed the corresponding preset threshold values, the temperature data of the environment in which the current sensor unit is located is collected, the temperature data is calculated as a difference with the median of the preset suitable temperature data range, and the absolute value is taken to obtain the working temperature coefficient, and the magnetic field strength, humidity and dust concentration around the current sensor unit are collected and marked as the working magnetic field coefficient, working humidity coefficient and working pollution coefficient respectively, and the vibration impact amplitude to which the current sensor unit is subjected is collected and marked as the working impact coefficient; Assigning corresponding preset weight coefficients to the working temperature coefficient, working magnetic field coefficient, working humidity coefficient, working pollution coefficient and working impact coefficient respectively, multiplying the working temperature coefficient, working magnetic field coefficient, working humidity coefficient, working pollution coefficient and working impact coefficient by the corresponding preset weight coefficients respectively, and marking the sum of the five groups of multiplication results as the normative real-time coefficient; Compare the normative real-time coefficient with the preset normative real-time coefficient threshold, if the normative real-time coefficient exceeds the preset normative real-time coefficient threshold, mark the corresponding normative real-time coefficient as a normative abnormal coefficient; obtain the number of normative abnormal coefficients per unit time and mark them as normative abnormal values, and mark the average value and maximum value of all normative real-time coefficients per unit time as normative performance value and normative amplitude value respectively; The compliance negative coefficient is obtained by numerically calculating the normative anomaly value, the normative performance value and the normative table amplitude, and the compliance negative coefficient is numerically compared with the preset compliance negative coefficient threshold. If the compliance negative coefficient exceeds the preset compliance negative coefficient threshold, a low-normative signal for operating conditions is generated; if the compliance negative coefficient does not exceed the preset compliance negative coefficient threshold, a high-normative signal for operating conditions is generated.

2. A current sensor data acquisition and analysis system according to claim 1, characterized in that: The operating condition normative analysis unit is communicatively connected to the calibration prediction analysis unit. The operating condition normative analysis unit sends an operating condition high specification signal to the calibration prediction analysis unit. When the calibration prediction analysis unit receives the operating condition high specification signal, it performs calibration prediction analysis, determines whether to generate a calibration alarm signal through analysis, and sends the calibration alarm signal to the remote monitoring server.

3. A current sensor data acquisition and analysis system according to claim 2, characterized in that: The specific analysis process of the calibration prediction analysis is as follows: obtaining the acquisition abnormality probability coefficient, comparing the acquisition abnormality probability coefficient with the preset acquisition abnormality probability coefficient threshold, and generating a calibration alarm signal if the acquisition abnormality probability coefficient exceeds the preset acquisition abnormality probability coefficient threshold; If the acquisition abnormality probability coefficient does not exceed the preset acquisition abnormality probability coefficient threshold, the time when the current sensor unit was calibrated last is obtained, the time difference between the current time and the time when the current sensor unit was calibrated last is calculated to obtain the calibration interval duration, the calibration interval duration is numerically compared with the preset calibration interval duration threshold, and if the calibration interval duration exceeds the preset calibration interval duration threshold, a calibration alarm signal is generated; If the calibration interval duration does not exceed the preset calibration interval duration threshold, the number of times the operating condition low specification signal is generated within the calibration interval duration is obtained and marked as the operating low specification detection value, and the interval duration between the production date of the current sensor unit and the current date is obtained and marked as the production time value, and several groups of preset production time value ranges are set in advance, and each group of preset production time value ranges corresponds to a group of life factors, and the life factors of the preset production time value ranges containing the corresponding production time values ​​are marked as target factors; The calibration prediction value is obtained by numerically calculating the target factor, the calibration interval duration and the running low specification detection value, and the calibration prediction value is numerically compared with the preset calibration prediction threshold. If the calibration prediction value exceeds the preset calibration prediction threshold, a calibration alarm signal is generated.

4. A current sensor data acquisition and analysis system according to claim 3, characterized in that: The calibration prediction analysis unit is communicatively connected to the acquisition abnormality probability analysis unit. When the acquisition abnormality probability analysis unit receives the operating condition high specification signal, it analyzes the acquisition abnormality probability of the current sensor unit, obtains the acquisition abnormality probability coefficient through analysis, and sends the acquisition abnormality probability coefficient to the calibration prediction analysis unit.

5. A current sensor data acquisition and analysis system according to claim 4, characterized in that: The analysis and acquisition method of the acquisition abnormality probability coefficient is specifically as follows: the number of capture interval durations per unit time is obtained and marked as an abnormal capture detection value, and the average of all capture interval durations per unit time is calculated to obtain a capture interval detection value; and the number of occurrences in which the current data collected per unit time is not within the preset reasonable current data range is obtained and marked as a capture high deviation value, and the acquisition abnormality probability coefficient is obtained by numerically calculating the abnormal capture detection value, the capture interval detection value and the capture high deviation value.

6. A current sensor data acquisition and analysis system according to claim 2, characterized in that: The calibration prediction and analysis unit is communicatively connected to the calibration push management module. The calibration prediction and analysis unit sends the calibration alarm signal to the calibration push management module. When the calibration push management module receives the calibration alarm signal, it performs a calibration push analysis to determine the push personnel, and sends the push personnel to the remote monitoring server. The remote monitoring server sends the calibration alarm signal to the push personnel's smart terminal.

7. A current sensor data acquisition and analysis method, using the current sensor data acquisition and analysis system according to any one of claims 1 to 6, characterized in that: The steps include: Step 1: The current sensor unit accurately captures the current signal; Step 2: The data output unit collects the current data output by the current sensor unit, and sends the collected current data to the intelligent engine analysis unit; Step 3: The intelligent analysis engine unit conducts in-depth mining of the collected current data, automatically identifies abnormal conditions and predicts potential faults, provides optimization suggestions, and sends the analysis results to the remote monitoring server; Step 4: the operating condition normativeness analysis unit performs real-time analysis on the operating condition of the current sensor unit, generates an operating condition high normative signal or an operating condition low normative signal through analysis, and sends the operating condition high normative signal or the operating condition low normative signal to the remote monitoring server; Step 5: The remote monitoring server displays the analysis results of the intelligent analysis engine unit and takes corresponding control measures when receiving a low-standard operating condition signal.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the current sensor data acquisition and analysis method according to claim 7 is implemented.

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