Dynamic compensation-based construction site intelligent weighing management method, system, and medium

Abstract

The application provides a construction site intelligent weighing management method and system based on dynamic compensation and a medium. The method comprises the following steps: obtaining a weighing state of a weighing device, performing zero point calibration or obtaining a corresponding original weight data set, a vibration amplitude, a tank body rotating speed and a vehicle parking inclination angle according to a preset sampling frequency, processing the tank body rotating speed and the vibration amplitude, obtaining a rotating state of the tank body, performing adaptive filtering compensation processing on the original weight data set according to the rotating state, obtaining final measured weight data, and performing abnormal value detection on the final measured weight data, and performing intelligent weighing management according to a detection result. The application determines the weighing state in real time, evaluates the rotating state of the tank body, dynamically and in real time performs adaptive filtering compensation processing, obtains the final measured weight data, and performs abnormality detection, so that the intelligent weighing management based on dynamic compensation is realized, and the adaptability and accuracy of weighing are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of engineering surveying, in particular to a construction site intelligent weighing management method, system and medium based on dynamic compensation. BACKGROUND

[0002] In the field of construction engineering, traditional weighing equipment (such as mechanical load cell, simple electronic scale) has significant technical defects: manual recording leads to low material acceptance efficiency (single batch acceptance time > 30 minutes); it is difficult to realize dynamic accounting of engineering quantity due to the inability to interface with the system; especially when weighing the concrete mixer truck, the centrifugal force generated by the rotation of the tank body to maintain the uniformity of the concrete will affect the weight distribution, and the vibration generated will also interfere with the weighing result, resulting in a significant reduction in weighing accuracy, which cannot meet the demand of the construction site for accurate measurement of material weight; therefore, a new intelligent weighing management method is needed to realize intelligent weighing management.

[0003] In view of the above problems, an effective technical solution is urgently needed. SUMMARY

[0004] The purpose of the present application is to provide a construction site intelligent weighing management method, system and medium based on dynamic compensation, which can determine the weighing state in real time, evaluate the rotation state of the tank body, dynamically and real-time self-adaptive filter compensation processing, obtain the final weight data, and perform abnormality detection, so as to realize intelligent weighing management based on dynamic compensation, and improve the adaptability and accuracy of weighing.

[0005] In a first aspect, the present application provides a construction site intelligent weighing management method based on dynamic compensation, comprising the following steps:

[0006] Obtaining the weighing state of the weighing equipment, including weighing or not weighing;

[0007] If the weighing state is not weighing, zero calibration is performed;

[0008] If the weighing state is weighing, the corresponding original weight data set, vibration amplitude, tank body rotation speed and vehicle parking inclination angle are obtained according to the preset sampling frequency;

[0009] According to the rotation state of the tank body, the original weight data set is obtained.

[0010] According to the rotation state, the original weight data set is subjected to self-adaptive filter compensation processing to obtain the final weight data;

[0011] The final weight data is subjected to abnormal value detection, and intelligent weighing management is performed according to the detection result.

[0012] Optionally, in the construction site intelligent weighing management method based on dynamic compensation provided in the present application, the obtaining of the weighing state of the weighing device comprises weighing or not weighing, which comprises:

[0013] obtaining real-time weight data of the weighing device;

[0014] if the real-time weight data is greater than or equal to a preset weight threshold, the weighing state is weighing;

[0015] if the real-time weight data is less than the preset weight threshold, the weighing state is not weighing;

[0016] if the weighing state is not weighing, processing according to the real-time weight data to obtain a real-time weight average value in a preset time period;

[0017] if the real-time weight average value is greater than a dynamic zero drift threshold, activating sensor automatic calibration processing.

[0018] Optionally, in the construction site intelligent weighing management method based on dynamic compensation provided in the present application, it further comprises:

[0019] obtaining real-time environmental monitoring data of the weighing device, including real-time temperature and real-time humidity;

[0020] comparing the real-time temperature and the real-time humidity with corresponding preset standard temperature and preset standard humidity respectively to obtain corresponding temperature deviation value and humidity deviation value;

[0021] weighting and summing the temperature deviation value and the humidity deviation value with corresponding preset temperature influence gradient value and preset humidity influence gradient value respectively to obtain an environmental influence coefficient;

[0022] obtaining the boot-up duration of the sensor and the preset service life;

[0023] comparing the boot-up duration with the preset service life to obtain a sensor aging evaluation parameter, and multiplying the sensor aging evaluation parameter with a preset aging influence gradient value to obtain an aging influence coefficient;

[0024] correcting a preset initial zero drift threshold according to the environmental influence coefficient and the aging influence coefficient combined with a preset adaptive adjustment factor to obtain a dynamic zero drift threshold.

[0025] Optionally, in the construction site intelligent weighing management method based on dynamic compensation provided in the present application, the processing according to the tank body rotating speed and the vibration amplitude to obtain the rotating state of the tank body comprises:

[0026] performing Fourier transform processing on the vibration amplitude to obtain a vibration frequency;

[0027] characteristic extraction is performed according to the vibration frequency, to obtain a main frequency component and a low-frequency energy ratio;

[0028] The tank body rotation speed, the main frequency component, and the low-frequency energy ratio are input into a preset tank body rotation state evaluation model for processing, to obtain a tank body rotation state evaluation parameter;

[0029] The rotation state evaluation parameter is compared with a first preset rotation state parameter threshold and a second preset rotation state parameter threshold respectively, to obtain a tank body rotation state, including a static state, a low-speed rotation state, or a high-speed rotation state.

[0030] Optionally, in the construction site intelligent weighing management method based on dynamic compensation provided in the present application, the adaptive filtering compensation processing of the original weight data set according to the rotation state to obtain final measured weight data includes:

[0031] The final measured weight data includes static final measured weight data, low-speed final measured weight data, or high-speed final measured weight data.

[0032] The vibration amplitude and the tank body rotation speed are combined with a preset compensation coefficient for processing, to obtain vibration compensation quality data.

[0033] If the rotation state is a static state, the original weight data set is processed by averaging according to a preset time window, to obtain static final measured weight data.

[0034] If the rotation state is a low-speed rotation state, a corresponding low-speed process noise covariance and a low-speed measurement noise covariance are obtained by querying a preset adaptive filtering compensation processing database.

[0035] The original weight data set corresponding to the preset time window is combined with the low-speed process noise covariance and the low-speed measurement noise covariance respectively for adaptive filtering compensation processing, to obtain a low-speed compensation weight data set.

[0036] The low-speed compensation weight data set is subtracted from the vibration compensation quality data respectively and processed by averaging, to obtain low-speed final measured weight data.

[0037] If the rotation state is a high-speed rotation state, a corresponding high-speed process noise covariance and a high-speed measurement noise covariance are obtained by querying a preset adaptive filtering compensation processing database.

[0038] The original weight data set corresponding to the preset time window is combined with the high-speed process noise covariance and the high-speed measurement noise covariance respectively for adaptive filtering compensation processing, to obtain a high-speed compensation weight data set.

[0039] Subtract the high-speed compensation weight data set from the vibration compensation mass data respectively and perform mean value processing to obtain high-speed final measurement weight data.

[0040] Optionally, in the dynamic compensation-based construction site intelligent weighing management method described in the application, the final measurement weight data is subjected to outlier detection, and intelligent weighing management is performed according to the detection result, which comprises:

[0041] The static final measurement weight data, the low-speed final measurement weight data or the high-speed final measurement weight data in a preset time period are subjected to feature extraction to obtain corresponding mean values and standard deviations;

[0042] The mean values and standard deviations are processed to obtain weight lower limit thresholds and weight upper limit thresholds corresponding to the static final measurement weight data, the low-speed final measurement weight data or the high-speed final measurement weight data;

[0043] The static final measurement weight data, the low-speed final measurement weight data or the high-speed final measurement weight data are respectively compared with the corresponding weight lower limit thresholds and weight upper limit thresholds;

[0044] If the weight lower limit threshold and the weight upper limit threshold are between the weight lower limit threshold and the weight upper limit threshold, it is determined that the data is normal, otherwise it is determined that the data is an outlier and is subjected to rejection processing to obtain corresponding static final measurement weight normal data, low-speed final measurement weight normal data or high-speed final measurement weight normal data in a preset time period;

[0045] The static final measurement weight normal data, the low-speed final measurement weight normal data or the high-speed final measurement weight normal data are processed by a preset isolation forest algorithm to obtain a data anomaly score;

[0046] If the data anomaly score is greater than a preset abnormality warning threshold, the data is subjected to rejection processing to obtain corresponding static final measurement weight valid data, low-speed final measurement weight valid data or high-speed final measurement weight valid data in a preset time period;

[0047] The static final measurement weight valid data, the low-speed final measurement weight valid data or the high-speed final measurement weight valid data are data packed in a preset format and sent to a management end for display.

[0048] Optionally, in the dynamic compensation-based construction site intelligent weighing management method described in the application, the dynamic compensation-based construction site intelligent weighing management method further comprises:

[0049] The vehicle parking inclination is compared with a preset inclination threshold;

[0050] If the vehicle parking inclination is greater than the preset inclination threshold, a weighing abnormality early warning response is output;

[0051] If the parking inclination of the vehicle is less than or equal to a preset inclination threshold, the final measurement weight data is obtained by processing according to the parking inclination of the vehicle and the final measurement weight data.

[0052] Optionally, in the construction site intelligent weighing management method based on dynamic compensation provided in the present application, the method further comprises the following steps:

[0053] The static final measurement weight effective data, the low-speed final measurement weight effective data or the high-speed final measurement weight effective data corresponding to a preset time period are subjected to mean value processing to obtain static final measurement weight mean value data, low-speed final measurement weight mean value data or high-speed final measurement weight mean value data.

[0054] The static final measurement weight mean value data, the low-speed final measurement weight mean value data or the high-speed final measurement weight mean value data are compared with corresponding preset mean value thresholds to obtain a mean value comparison result.

[0055] The static final measurement weight effective data, the low-speed final measurement weight effective data or the high-speed final measurement weight effective data corresponding to a preset time period are subjected to peak extraction processing to obtain static final measurement weight peak value data, low-speed final measurement weight peak value data or high-speed final measurement weight peak value data.

[0056] The static final measurement weight peak value data, the low-speed final measurement weight peak value data or the high-speed final measurement weight peak value data are compared with corresponding peak value thresholds to obtain a peak value comparison result.

[0057] The static final measurement weight effective data, the low-speed final measurement weight effective data or the high-speed final measurement weight effective data corresponding to a preset time period are compared to obtain a static final measurement weight change rate, a low-speed final measurement weight change rate or a high-speed final measurement weight change rate.

[0058] The static final measurement weight change rate, the low-speed final measurement weight change rate or the high-speed final measurement weight change rate are compared with corresponding change rate thresholds to obtain a change rate comparison result.

[0059] If the mean value comparison result, the peak value comparison result and the change rate comparison result are all not greater than the threshold, no overload warning is output.

[0060] On the contrary, an overload warning is output.

[0061] In a second aspect, the present application provides a construction site intelligent weighing management system based on dynamic compensation, which comprises a memory and a processor, the memory comprising a program of a construction site intelligent weighing management method based on dynamic compensation, the program of the construction site intelligent weighing management method based on dynamic compensation being executed by the processor to realize the following steps:

[0062] The weighing state of the weighing device is obtained, including weighing or not weighing.

[0063] If the weighing state is not in weighing, zero calibration is performed;

[0064] If the weighing state is in weighing, the corresponding raw weight data, vibration amplitude, tank rotation speed and vehicle stop inclination angle are obtained according to the preset sampling frequency;

[0065] The rotation state of the tank is obtained according to the tank rotation speed and the vibration amplitude;

[0066] The raw weight data set is adaptively filtered and compensated according to the rotation state, and the final measured weight data is obtained;

[0067] The final measured weight data is subjected to outlier detection, and intelligent weighing management is performed according to the detection result.

[0068] In a third aspect, the application also provides a computer readable storage medium, wherein a dynamic compensation based construction site intelligent weighing management method program is stored in the computer readable storage medium, and the dynamic compensation based construction site intelligent weighing management method program is executed by a processor to implement the steps of the dynamic compensation based construction site intelligent weighing management method according to any one of the above.

[0069] As can be seen from the above, the dynamic compensation based construction site intelligent weighing management method, system and medium provided by the application can determine the weighing state in real time, evaluate the rotation state of the tank, dynamically and adaptively filter and compensate in real time, obtain the final measured weight data, and perform outlier detection, so as to realize intelligent weighing management based on dynamic compensation, and improve the adaptability and accuracy of weighing.

[0070] Other features and advantages of the application will be set forth in the following description, and in part will become apparent to those skilled in the art from the description, or can be learned by practice of the application. The objects and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and claims. BRIEF DESCRIPTION OF DRAWINGS

[0071] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments of the application. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as limiting the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0072] Figure 1 The flowchart of the dynamic compensation based construction site intelligent weighing management method provided by the embodiments of the application;

[0073] Figure 2A flowchart for obtaining a rotation state of a tank body in the construction site intelligent weighing management method based on dynamic compensation provided in the embodiments of the present application is shown in FIG. 11.

[0074] Figure 3 A flowchart for obtaining final weight data in the construction site intelligent weighing management method based on dynamic compensation provided in the embodiments of the present application is shown in FIG. 12. DETAILED DESCRIPTION

[0075] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. The components of the embodiments of the present application described and shown in the accompanying drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0076] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. Meanwhile, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0077] Please refer to Figure 1 , Figure 1 is a flowchart of the construction site intelligent weighing management method based on dynamic compensation in some embodiments of the present application. The construction site intelligent weighing management method based on dynamic compensation is used in a terminal device, such as a computer, a mobile phone terminal, etc. The construction site intelligent weighing management method based on dynamic compensation includes the following steps:

[0078] S11, obtaining a weighing state of a weighing device, including weighing or not weighing;

[0079] S121, if the weighing state is not weighing, performing zero-point calibration;

[0080] S122, if the weighing state is weighing, obtaining corresponding original weight data, vibration amplitude, tank body rotation speed and vehicle parking inclination angle according to a preset sampling frequency;

[0081] S13, processing according to the tank body rotation speed and the vibration amplitude to obtain a rotation state of the tank body;

[0082] S14, performing adaptive filtering compensation processing on the original weight data set according to the rotation state, to obtain final measurement weight data;

[0083] S15, performing outlier detection on the final measurement weight data, and performing intelligent weighing management according to a detection result.

[0084] It should be noted that, in order to realize accurate weighing in a construction engineering scene, first, calibration is performed based on two cases of weighing and not weighing, when not weighing, sensor zero calibration is performed, and if weighing, it is further determined that the tank is in a stationary state or a motion state, the motion state is further distinguished between low speed and high speed, adaptive filtering compensation processing is performed according to different states, and then, outlier detection is performed on the obtained final measurement weight data, data corresponding to the data packet is generated, and the data is fed back to the management end for inspection, so that high-precision weighing, efficient interaction and improvement of work efficiency are realized.

[0085] According to the embodiment of the present application, the weighing state of the weighing device is obtained, including weighing or not weighing, comprising:

[0086] Real-time weight data of the weighing device is obtained;

[0087] If the real-time weight data is greater than or equal to a preset weight threshold, the weighing state is in weighing;

[0088] If the real-time weight data is less than the preset weight threshold, the weighing state is not in weighing;

[0089] If the weighing state is not in weighing, the real-time weight data is processed to obtain a real-time weight average value in a preset time period;

[0090] If the real-time weight average value is greater than a dynamic zero drift threshold, sensor automatic calibration processing is activated.

[0091] It should be noted that, according to the real-time weight data of the weighing device, threshold comparison is performed to determine whether it is in weighing or not in weighing, if it is not in weighing, a plurality of real-time weight data collected in a preset time period is further processed to obtain a real-time weight average value, and then compared with a dynamic zero drift threshold, if less than or equal to the dynamic zero drift threshold, it is determined that the weighing device does not need to be calibrated, and if greater than the dynamic zero drift threshold, sensor automatic calibration processing is controlled according to the real-time weight average value, to ensure the accuracy of the weighing device.

[0092] According to the embodiment of the present application, further comprising:

[0093] Real-time environmental monitoring data of the weighing device is obtained, including real-time temperature and real-time humidity;

[0094] The real-time temperature and the real-time humidity are compared with corresponding preset standard temperature and preset standard humidity respectively to obtain corresponding temperature deviation value and humidity deviation value;

[0095] The temperature deviation value and the humidity deviation value are weighted and summed with corresponding preset temperature influence gradient value and preset humidity influence gradient value respectively to obtain an environmental influence coefficient;

[0096] The boot-up duration of the sensor and a preset service life are obtained;

[0097] The boot-up duration is compared with the preset service life to obtain a sensor aging evaluation parameter, and the preset aging influence gradient value is multiplied to obtain an aging influence coefficient;

[0098] The preset initial zero drift threshold is corrected according to the environmental influence coefficient and the aging influence coefficient in combination with a preset adaptive adjustment factor to obtain a dynamic zero drift threshold.

[0099] It should be noted that since the weighing device is mostly outdoor, the sensor is easily affected by the environment, therefore, the real-time temperature and real-time humidity are collected and compared with the corresponding preset standard temperature and preset standard humidity respectively to obtain the corresponding temperature deviation value and humidity deviation value, wherein the temperature deviation value refers to the absolute value of the difference between the real-time temperature and the preset standard temperature, and the humidity deviation value refers to the absolute value of the difference between the real-time humidity and the preset standard humidity, the temperature deviation value is multiplied by a preset temperature influence gradient value, the humidity deviation value is multiplied by a preset humidity influence gradient value, and then the sum is obtained to obtain an environmental influence coefficient, for example, the temperature deviation value is 3℃, the preset temperature influence gradient value is 0.002, the humidity deviation value is 10%, and the preset humidity influence gradient value is 0.001, then 3x0.002+0.1x0.001=0.0061 is the environmental influence coefficient; the sensor's boot-up duration is compared with the preset service life, for example, the boot-up duration is 100 days, and the preset service life is 1095 days, then 100 / 1095≈0.091 is the sensor aging evaluation parameter, which is multiplied by a preset aging influence gradient value of 0.3, 0.091x0.3=0.0273 is the aging influence coefficient, finally, the obtained environmental influence coefficient and aging influence coefficient are combined with a preset adaptive adjustment factor to correct the preset initial zero drift threshold to obtain a dynamic zero drift threshold, (1+0.0061+0.0273)x preset adaptive adjustment factor value x preset initial zero drift threshold is the dynamic zero drift threshold, wherein the preset temperature influence gradient value, the preset humidity influence gradient value and the preset aging influence gradient value are calibrated by a person skilled in the art according to a large number of historical weighing device operation logs, and can be dynamically adjusted, when the weighing error is less than or equal to the preset weighing error threshold in the preset time period, the preset adaptive adjustment factor is 0.95 to improve the sensitivity, if it is greater than the preset weighing error threshold, the preset adaptive adjustment factor is 1+0.1x[(weighing error / preset weighing error threshold)-1] to increase the threshold stability.

[0100] Please refer to Figure 2 , Figure 2 is a flowchart for obtaining a rotation state of a tank body in a dynamic compensation-based construction site intelligent weighing management method in some embodiments of the present application. According to the embodiment of the present application, the processing according to the tank body rotation speed and the vibration amplitude to obtain the rotation state of the tank body comprises:

[0101] S21, performing Fourier transform processing on the vibration amplitude to obtain a vibration frequency;

[0102] S22, performing feature extraction according to the vibration frequency to obtain a main frequency component and a low-frequency energy ratio;

[0103] S23, input the tank body rotating speed, main frequency component and low frequency energy ratio into a preset tank body rotating state evaluation model for processing to obtain a rotating state evaluation parameter of the tank body;

[0104] S24, compare the rotating state evaluation parameter with a first preset rotating state parameter threshold and a second preset rotating state parameter threshold respectively to obtain a rotating state of the tank body, including a static state, a low-speed rotating state or a high-speed rotating state.

[0105] It should be noted that in the intelligent weighing management of the construction site, accurately identifying the rotating state of the concrete mixer truck tank body is a key link to realize dynamic weighing compensation. In order to accurately determine the rotating state of the tank body, in addition to the tank body rotating speed, the vibration frequency obtained by processing the vibration amplitude through Fourier transform is also considered, and the main frequency component and the low frequency energy ratio are extracted, wherein the main frequency component refers to the frequency corresponding to the spectral peak, and the low frequency energy ratio refers to the ratio of the low frequency band (0-5Hz) to the total energy, and the total energy refers to the sum of the low frequency band energy (0-5Hz) and the high frequency band energy (5-20Hz), and the noise above 20Hz is removed. The obtained tank body rotating speed, main frequency component and low frequency energy ratio are input into a preset tank body rotating state evaluation model for processing to obtain a rotating state evaluation parameter of the tank body, and the preset tank body rotating state evaluation model is obtained by training a large number of historical samples of tank body rotating speed, main frequency component and low frequency energy ratio and corresponding rotating state evaluation parameters. If the rotating state evaluation parameter is less than or equal to the first preset rotating state parameter threshold, it is determined that the rotating state of the tank body is a static state, if the rotating state evaluation parameter is greater than the first preset rotating state parameter threshold and less than or equal to the second preset rotating state parameter threshold, it is determined that the rotating state of the tank body is a low-speed rotating state, otherwise it is a high-speed rotating state.

[0106] Please refer to Figure 3 , Figure 3 is a flowchart of obtaining final weight data of the intelligent weighing management method of the construction site based on dynamic compensation in some embodiments of the present application. According to the embodiment of the present application, the adaptive filtering compensation processing of the original weight data set according to the rotating state to obtain the final weight data includes:

[0107] S31, the final weight data includes static final weight data, low-speed final weight data or high-speed final weight data;

[0108] S32, the vibration amplitude and the tank body rotating speed are combined with a preset compensation coefficient for processing to obtain vibration compensation quality data;

[0109] S33, if the rotating state is a static state, the original weight data set is processed by mean value processing according to a preset time window to obtain static final weight data;

[0110] S34, if the rotation state is a low-speed rotation state, querying a preset adaptive filter compensation processing database to obtain corresponding low-speed process noise covariance and low-speed measurement noise covariance;

[0111] S35, combining the original weight data set corresponding to the preset time window with the low-speed process noise covariance and the low-speed measurement noise covariance respectively to perform adaptive filter compensation processing, to obtain a low-speed compensation weight data set;

[0112] S36, subtracting the low-speed compensation weight data set from the vibration compensation mass data respectively and performing mean value processing to obtain low-speed final measurement weight data;

[0113] S37, if the rotation state is a high-speed rotation state, querying a preset adaptive filter compensation processing database to obtain corresponding high-speed process noise covariance and high-speed measurement noise covariance;

[0114] S38, combining the original weight data set corresponding to the preset time window with the high-speed process noise covariance and the high-speed measurement noise covariance respectively to perform adaptive filter compensation processing, to obtain a high-speed compensation weight data set;

[0115] S39, subtracting the high-speed compensation weight data set from the vibration compensation mass data respectively and performing mean value processing to obtain high-speed final measurement weight data.

[0116] It should be noted that when it is determined to be a static state, no additional compensation is needed, and the original weight data set in the preset time window is subjected to mean value processing to obtain static final measurement weight data. For example, if the sampling rate is 10 Hz and the time window is 5, 5 original weight data in 0.5 seconds are covered. When the tank body rotates, centrifugal force and vibration are two main interferences. In order to eliminate the interference, vibration compensation mass data are obtained by combining a preset compensation coefficient with vibration amplitude and tank body rotation speed, wherein the preset compensation coefficient includes a vibration compensation coefficient, a rotation speed compensation coefficient and a system reference compensation coefficient, which are determined by a person skilled in the art through multiple regression optimization under the conditions of fixed rotation speed changing load, changing rotation speed fixed load. In the present embodiment, the vibration compensation coefficient is 0.05, the rotation speed compensation coefficient is 0.001, and the system reference compensation coefficient (representing inherent deviation) is 0.1. If the rotation speed is 4 revolutions per minute and the vibration amplitude is 0.2, then 0.001x4 2+0.05x0.2+0.1=0.126 is the vibration compensation mass data; when the tank is in the low-speed rotating state or the high-speed rotating state, the corresponding process noise covariance and the measurement noise covariance are obtained by querying the preset adaptive filtering compensation processing database, wherein the preset adaptive filtering compensation processing database is constructed by the person skilled in the art according to a large number of historical sample weighing log information, and can be dynamically adjusted; in the embodiment, the low-speed process noise covariance is 0.02, the low-speed measurement noise covariance is 0.05, the high-speed process noise covariance is 0.05, and the high-speed measurement noise covariance is 0.1; if the original weight data is [1001.2, 1002.8, 1000.5, …], the Kalman filtering initialization is first performed (the initial state value is 1001.2, and the initial estimation error covariance is 1), and then the Kalman filtering iteration processing is performed, that is, the first filtering estimation error covariance is first determined (the sum of the initial estimation error covariance and the low-speed process noise covariance is 1.02), then the first Kalman gain is determined, that is, the first filtering estimation error covariance is divided by the sum of the first filtering estimation error covariance and the low-speed measurement noise covariance, that is, 1.02 / (1.02+0.05)≈0.953, then the first low-speed compensation weight data is determined, that is, 1001.2+0.953x(1001.2-1001.2)=1001.2, and the first filtering error covariance is output, that is, (1-0.953)x1.02=0.048, which is used for the second iteration processing; in the second Kalman filtering iteration processing, the predicted weight is 1001.2, the second filtering estimation error covariance is 0.048+0.02=0.068, the second Kalman gain is further determined, that is, 0.068 / (0.068+0.05)≈0.576, finally, the second low-speed compensation weight data is determined, that is, 1001.2+0.576x(1002.8-1001.2)=1002.12, and the second filtering error covariance is output, that is, (1-0.576)x0.068≈0.029, which is used for the third iteration processing, and the iteration processing is performed in a loop to obtain the low-speed compensation weight data set, and the high-speed compensation weight data set can be obtained in the same way; finally, the obtained low-speed compensation weight data set or high-speed compensation weight data set is sequentially subtracted from the vibration compensation mass data and the mean value is obtained to obtain the low-speed final measurement weight data or the high-speed final measurement weight data.

[0117] According to the embodiment of the application, the final measurement weight data is subjected to abnormal value detection, and intelligent weighing management is performed according to the detection result, which comprises:

[0118] The static final measurement weight data, the low-speed final measurement weight data or the high-speed final measurement weight data in the preset time period are subjected to feature extraction to obtain the corresponding mean value and standard deviation;

[0119] According to the mean and the standard deviation, static final measurement weight data, low-speed final measurement weight data or high-speed final measurement weight data are processed to obtain a weight lower threshold and a weight upper threshold corresponding to the static final measurement weight data, the low-speed final measurement weight data or the high-speed final measurement weight data;

[0120] The static final measurement weight data, the low-speed final measurement weight data or the high-speed final measurement weight data are compared with the corresponding weight lower threshold and weight upper threshold respectively;

[0121] If the weight is between the weight lower threshold and the weight upper threshold, it is determined as normal data, otherwise, it is determined as an abnormal value, and the elimination processing is performed to obtain corresponding static final measurement weight normal data, low-speed final measurement weight normal data or high-speed final measurement weight normal data in a preset time period;

[0122] According to the static final measurement weight normal data, the low-speed final measurement weight normal data or the high-speed final measurement weight normal data, the preset isolation forest algorithm is processed to obtain a data anomaly score;

[0123] If the data anomaly score is greater than a preset abnormal alarm threshold, the elimination processing is performed to obtain corresponding static final measurement weight valid data, low-speed final measurement weight valid data or high-speed final measurement weight valid data in a preset time period;

[0124] The static final measurement weight valid data, the low-speed final measurement weight valid data or the high-speed final measurement weight valid data are data packed in a preset format and sent to a management end for display.

[0125] It should be noted that, in order to detect the final measurement weight data obtained by the adaptive filtering compensation processing, two dimensions of determining the threshold based on 3 times the standard deviation and determining the data anomaly score based on the path length of the isolation forest algorithm are adopted for detection, wherein the weight lower threshold is the mean minus 3 times the standard deviation, and the weight upper threshold is the mean plus 3 times the standard deviation; the preset isolation forest algorithm calculates the data anomaly score based on the path length, and the higher the score, the more likely it is an abnormal value; the combination of the two solves the extreme abnormal value and can identify the local abnormal value, and finally, the static final measurement weight valid data, the low-speed final measurement weight valid data or the high-speed final measurement weight valid data that pass the detection are data packed in a preset format and sent to a management end for display.

[0126] According to the embodiment of the application, the method further comprises:

[0127] The vehicle parking inclination is compared with a preset inclination threshold;

[0128] If the vehicle parking inclination is greater than the preset inclination threshold, a weighing abnormality early warning response is output;

[0129] If the vehicle parking inclination is less than or equal to a preset inclination threshold, the vehicle parking inclination and the final weight data are processed to obtain final weight optimization data.

[0130] It should be noted that the vehicle parking inclination directly affects the accuracy of the weight measurement, and the gravity component correction needs to be performed. If the inclination is too large, the weighing is rejected, and a warning response is output. If it is not greater than the preset inclination threshold, the vehicle parking inclination and the final weight data are processed to obtain final weight optimization data, wherein the vehicle parking inclination includes a lateral inclination θ and a longitudinal inclination γ, and the final weight optimization data is the final weight data / cosθcosγ.

[0131] According to the embodiment of the application, it further comprises:

[0132] The static final weight effective data, low-speed final weight effective data or high-speed final weight effective data corresponding to the preset time period are subjected to mean value processing to obtain static final weight mean value data, low-speed final weight mean value data or high-speed final weight mean value data;

[0133] The static final weight mean value data, low-speed final weight mean value data or high-speed final weight mean value data are compared with the corresponding preset mean value threshold to obtain a mean value comparison result;

[0134] The static final weight effective data, low-speed final weight effective data or high-speed final weight effective data corresponding to the preset time period are subjected to peak extraction processing to obtain static final weight peak value data, low-speed final weight peak value data or high-speed final weight peak value data;

[0135] The static final weight peak value data, low-speed final weight peak value data or high-speed final weight peak value data are compared with the corresponding peak threshold to obtain a peak comparison result;

[0136] The static final weight effective data, low-speed final weight effective data or high-speed final weight effective data corresponding to the preset time period are compared to obtain a static final weight change rate, a low-speed final weight change rate or a high-speed final weight change rate;

[0137] The static final weight change rate, low-speed final weight change rate or high-speed final weight change rate is compared with the corresponding change rate threshold to obtain a change rate comparison result;

[0138] If the mean value comparison result, the peak comparison result and the change rate comparison result are all not greater than the threshold, no overload warning is output;

[0139] On the contrary, an overload warning is output.

[0140] It should be noted that, in order to accurately evaluate the vehicle overload problem, the embodiment is evaluated from three dimensions of mean value comparison, peak value comparison and weight change rate, and the mean value, peak value and change rate are compared with the corresponding threshold value respectively to obtain the threshold comparison result, including greater than the threshold value or not greater than the threshold value, wherein the change rate is the ratio of the absolute value of the difference between the effective data of the final weight at the next time point and the effective data of the final weight at the previous time point to the effective data of the final weight at the previous time point, and finally, the and operation is performed, only when all are not greater than the threshold value, it is determined that it is not overloaded, otherwise, as long as one exceeds the threshold value, it is determined that it is overloaded.

[0141] It is worth mentioning that, according to the embodiment of the application, further comprising:

[0142] The tank body speed is compared with the preset dynamic speed threshold value;

[0143] If the tank body speed is less than the preset dynamic speed threshold value, the main frequency component is compared with the preset main frequency threshold value, and the low frequency energy ratio is compared with the preset low frequency energy ratio threshold value;

[0144] If the main frequency component is greater than or equal to the preset main frequency threshold value, or the low frequency energy ratio is less than or equal to the preset low frequency energy ratio threshold value, it is determined that the rotation state of the tank body is a transition state;

[0145] According to the transition state, a corresponding weighing management strategy is determined.

[0146] It should be noted that the transition state refers to the dynamic change process of the equipment from one stable state (static, low speed rotation, high speed rotation) to another stable state, which has the characteristics of temporality, dynamics and uncertainty. Accurate identification of the transition state can avoid misjudgment (such as misjudgment of speed fluctuation and sensor data jump as an abnormality of the stable state). First, the tank body speed is compared with the threshold value to determine that the speed is very low (close to static). However, only the speed cannot completely determine whether it is "stable and static". Then, the main frequency component and the low frequency energy ratio are compared with the threshold value respectively. As long as the main frequency component is greater than or equal to the preset main frequency threshold value, or the low frequency energy ratio is less than or equal to the preset low frequency energy ratio threshold value, it is determined that the rotation state of the tank body is a transition state, otherwise it is a static state.

[0147] The application also discloses a construction site intelligent weighing management system based on dynamic compensation, which comprises a memory and a processor, the memory comprises a construction site intelligent weighing management method program based on dynamic compensation, and the construction site intelligent weighing management method program based on dynamic compensation is executed by the processor to realize the following steps:

[0148] Obtaining the weighing state of the weighing device, including weighing or not weighing;

[0149] If the weighing state is not in weighing, zero calibration is performed;

[0150] If the weighing state is in weighing, the corresponding original weight data, vibration amplitude, tank rotation speed and vehicle parking inclination are obtained according to the preset sampling frequency;

[0151] According to the tank rotation speed and vibration amplitude, the rotation state of the tank is obtained;

[0152] According to the rotation state, the original weight data set is adaptively filtered and compensated to obtain final measured weight data;

[0153] The final measured weight data is subjected to outlier detection, and intelligent weighing management is performed according to the detection result.

[0154] It should be noted that in order to realize accurate weighing in the construction engineering scene, first, calibration is performed based on the two conditions of weighing and not weighing. When not in weighing, sensor zero calibration is performed. If in weighing, it is further determined whether the tank is in a stationary state or a motion state. The motion state is further divided into low speed and high speed. Adaptive filtering compensation processing is performed according to different states. Then, the final measured weight data obtained is subjected to abnormality detection. The data that passes the detection is generated into a corresponding data packet and fed back to the management end for inspection, so as to realize high-precision weighing, efficient interaction and improve work efficiency.

[0155] According to the embodiment of the application, the weighing state of the weighing device is obtained, including weighing or not weighing, comprising:

[0156] Real-time weight data of the weighing device is obtained;

[0157] If the real-time weight data is greater than or equal to a preset weight threshold, the weighing state is in weighing;

[0158] If the real-time weight data is less than the preset weight threshold, the weighing state is not in weighing;

[0159] If the weighing state is not in weighing, the real-time weight data is processed to obtain the real-time weight average value in a preset time period;

[0160] If the real-time weight average value is greater than a dynamic zero drift threshold, sensor automatic calibration processing is activated.

[0161] It should be noted that according to the real-time weight data of the weighing device, the threshold comparison is carried out to determine whether the weighing is carried out or not, if the weighing is not carried out, the plurality of real-time weight data collected in the preset time period is further processed by averaging to obtain the real-time weight average, and then compared with the dynamic zero drift threshold, if less than or equal to the dynamic zero drift threshold, it is determined that the weighing device does not need to be calibrated, if greater than the dynamic zero drift threshold, the real-time weight average is used to control the automatic calibration of the sensor to ensure the accuracy of the weighing device.

[0162] According to the embodiment of the application, further comprising:

[0163] The real-time environmental monitoring data of the weighing device is obtained, including real-time temperature and real-time humidity;

[0164] The real-time temperature and real-time humidity are compared with the corresponding preset standard temperature and preset standard humidity respectively to obtain the corresponding temperature deviation value and humidity deviation value;

[0165] The temperature deviation value and humidity deviation value are weighted and summed with the corresponding preset temperature influence gradient value and preset humidity influence gradient value respectively to obtain the environmental influence coefficient;

[0166] The on duration of the sensor and the preset service life are obtained;

[0167] The on duration is compared with the preset service life to obtain the sensor aging evaluation parameter, and multiplied by the preset aging influence gradient value to obtain the aging influence coefficient;

[0168] The preset initial zero drift threshold is corrected according to the environmental influence coefficient and the aging influence coefficient combined with the preset adaptive adjustment factor to obtain the dynamic zero drift threshold.

[0169] It should be noted that since the weighing device is mostly outdoor, the sensor is easily affected by the environment, therefore, the real-time temperature and real-time humidity are collected and compared with the corresponding preset standard temperature and preset standard humidity respectively to obtain a corresponding temperature deviation value and humidity deviation value, wherein the temperature deviation value refers to the absolute value of the difference between the real-time temperature and the preset standard temperature, and the humidity deviation value refers to the absolute value of the difference between the real-time humidity and the preset standard humidity, the temperature deviation value is multiplied by a preset temperature influence gradient value, the humidity deviation value is multiplied by a preset humidity influence gradient value, and then the sum is obtained to obtain an environmental influence coefficient, for example, the temperature deviation value is 3 DEG C, the preset temperature influence gradient value is 0.002, the humidity deviation value is 10%, and the preset humidity influence gradient value is 0.001, then 3x0.002+0.1x0.001=0.0061 is the environmental influence coefficient; the sensor's boot-up duration is compared with the preset service life, for example, the boot-up duration is 100 days, and the preset service life is 1095 days, then 100 / 1095≈0.091 is the sensor aging evaluation parameter, which is multiplied by a preset aging influence gradient value of 0.3, 0.091x0.3=0.0273 is the aging influence coefficient, finally, the obtained environmental influence coefficient and aging influence coefficient are combined with a preset adaptive adjustment factor to correct the preset initial zero drift threshold to obtain a dynamic zero drift threshold, (1+0.0061+0.0273)x preset adaptive adjustment factor value x preset initial zero drift threshold is the dynamic zero drift threshold, wherein the preset temperature influence gradient value, the preset humidity influence gradient value and the preset aging influence gradient value are calibrated by a person skilled in the art according to a large number of historical weighing device operation logs, and can be dynamically adjusted, when the weighing error is less than or equal to the preset weighing error threshold in the preset time period, the preset adaptive adjustment factor is 0.95 to improve the sensitivity, and if it is greater than the preset weighing error threshold, the preset adaptive adjustment factor is 1+0.1x[(weighing error / preset weighing error threshold)-1] to increase the threshold stability.

[0170] According to the embodiment of the application, the processing according to the tank body rotating speed and the vibration amplitude value to obtain the rotating state of the tank body comprises:

[0171] The vibration amplitude value is subjected to Fourier transform processing to obtain a vibration frequency;

[0172] According to the vibration frequency, feature extraction is performed to obtain a main frequency component and a low frequency energy ratio;

[0173] The tank body rotating speed, the main frequency component and the low frequency energy ratio are input into a preset tank body rotating state evaluation model for processing to obtain a tank body rotating state evaluation parameter;

[0174] The rotation state evaluation parameter is compared with a first preset rotation state parameter threshold and a second preset rotation state parameter threshold respectively, and a rotation state of the tank body is obtained, including a static state, a low-speed rotation state or a high-speed rotation state.

[0175] It should be noted that in the intelligent weighing management of the construction site, accurately identifying the rotation state of the concrete mixer truck tank body is a key link to realize dynamic weighing compensation. In order to accurately determine the rotation state of the tank body, in addition to the tank body speed, the vibration amplitude is also considered, and the vibration frequency obtained after Fourier transform processing is obtained. At the same time, the main frequency component and the low frequency energy ratio are extracted, wherein the main frequency component refers to the frequency corresponding to the spectrum peak, and the low frequency energy ratio refers to the ratio of the low frequency band (0-5Hz) to the total energy. The total energy refers to the sum of the 0-5Hz low frequency band energy and the 5-20Hz high frequency band energy. Higher than 20Hz is determined as noise and is eliminated. The obtained tank body speed, main frequency component and low frequency energy ratio are input into a preset tank body rotation state evaluation model for processing to obtain a tank body rotation state evaluation parameter. The preset tank body rotation state evaluation model is obtained by training a large number of historical samples of tank body speed, main frequency component and low frequency energy ratio and corresponding rotation state evaluation parameters. If the rotation state evaluation parameter is less than or equal to the first preset rotation state parameter threshold, it is determined that the rotation state of the tank body is a static state. If the rotation state evaluation parameter is greater than the first preset rotation state parameter threshold and less than or equal to the second preset rotation state parameter threshold, it is determined that the rotation state of the tank body is a low-speed rotation state. Otherwise, it is a high-speed rotation state.

[0176] According to the embodiment of the application, the adaptive filtering compensation processing is performed on the original weight data set according to the rotation state to obtain final measurement weight data, including:

[0177] The final measurement weight data includes static final measurement weight data, low-speed final measurement weight data or high-speed final measurement weight data.

[0178] The vibration compensation quality data is obtained by processing the vibration amplitude and the tank body speed in combination with a preset compensation coefficient.

[0179] If the rotation state is a static state, the original weight data set is processed by averaging according to a preset time window to obtain static final measurement weight data.

[0180] If the rotation state is a low-speed rotation state, the corresponding low-speed process noise covariance and low-speed measurement noise covariance are obtained by querying a preset adaptive filtering compensation processing database.

[0181] The original weight data set corresponding to the preset time window is respectively subjected to adaptive filtering compensation processing in combination with the low-speed process noise covariance and the low-speed measurement noise covariance to obtain a low-speed compensation weight data set.

[0182] subtracting the low-speed compensated weight data set from the vibration-compensated mass data and performing mean value processing, to obtain low-speed final-measured weight data;

[0183] If the rotation state is a high-speed rotation state, a corresponding high-speed process noise covariance and a high-speed measurement noise covariance are obtained by querying a preset adaptive filter compensation processing database;

[0184] performing adaptive filter compensation processing on the original weight data set corresponding to the preset time window in combination with the high-speed process noise covariance and the high-speed measurement noise covariance, to obtain a high-speed compensated weight data set;

[0185] subtracting the high-speed compensated weight data set from the vibration-compensated mass data and performing mean value processing, to obtain high-speed final-measured weight data.

[0186] It should be noted that when it is determined to be a static state, no additional compensation is needed, and the original weight data set in the preset time window is subjected to mean value processing, to obtain static final-measured weight data. For example, if the sampling rate is 10 Hz and the time window is 5, 5 original weight data in 0.5 seconds are covered. When the tank body rotates, centrifugal force and vibration are two main interferences. In order to eliminate the interference, vibration-compensated mass data are obtained by processing in combination with a preset compensation coefficient according to the vibration amplitude and the rotation speed of the tank body. The preset compensation coefficient includes a vibration compensation coefficient, a rotation speed compensation coefficient, and a system reference compensation coefficient, which are determined by a person skilled in the art through multivariate regression optimization under the conditions of fixed rotation speed changing load, changing rotation speed fixed load, and the like. In this embodiment, the vibration compensation coefficient is 0.05, the rotation speed compensation coefficient is 0.001, and the system reference compensation coefficient (representing inherent deviation) is 0.1. If the rotation speed is 4 revolutions per minute and the vibration amplitude is 0.2, then 0.001 x 4 20.05x0.2+0.1=0.126, which is the vibration compensation mass data; when the tank is in the low-speed rotating state or the high-speed rotating state, the corresponding process noise covariance and the measurement noise covariance are obtained by querying the preset adaptive filtering compensation processing database, wherein the preset adaptive filtering compensation processing database is constructed by the person skilled in the art according to a large number of historical sample weighing log information and can be dynamically adjusted; in the embodiment, the low-speed process noise covariance is 0.02, the low-speed measurement noise covariance is 0.05, the high-speed process noise covariance is 0.05, and the high-speed measurement noise covariance is 0.1; if the original weight data is [1001.2, 1002.8, 1000.5, …], the Kalman filtering initialization is first performed (the initial state value is 1001.2, and the initial estimation error covariance is set to 1), and then the Kalman filtering iteration processing is performed, that is, the first filtering estimation error covariance is first determined (the sum of the initial estimation error covariance and the low-speed process noise covariance is 1.02), then the first Kalman gain is determined, which is the first filtering estimation error covariance divided by the sum of the first filtering estimation error covariance and the low-speed measurement noise covariance, that is, 1.02 / (1.02+0.05)≈0.953, then the first low-speed compensation weight data is determined, that is, 1001.2+0.953x(1001.2-1001.2)=1001.2, and the first filtering error covariance is output, that is, (1-0.953)x1.02=0.048, which is used for the second iteration processing; in the second Kalman filtering iteration processing, the predicted weight is 1001.2, the second filtering estimation error covariance is 0.048+0.02=0.068, the second Kalman gain is further determined, that is, 0.068 / (0.068+0.05)≈0.576, finally, the second low-speed compensation weight data is determined, that is, 1001.2+0.576x(1002.8-1001.2)=1002.12, and the second filtering error covariance is output, that is, (1-0.576)x0.068≈0.029, which is used for the third iteration processing, and the iteration processing is performed in a loop to obtain the low-speed compensation weight data set, and the high-speed compensation weight data set can be obtained in the same way; finally, the obtained low-speed compensation weight data set or high-speed compensation weight data set is sequentially subtracted from the vibration compensation mass data and the mean value processing is performed to obtain the low-speed final measurement weight data or the high-speed final measurement weight data.

[0187] According to the embodiment of the application, the final measurement weight data is subjected to abnormal value detection, and intelligent weighing management is performed according to the detection result, which comprises:

[0188] The static final measurement weight data, the low-speed final measurement weight data or the high-speed final measurement weight data in the preset time period are subjected to feature extraction to obtain the corresponding mean value and standard deviation;

[0189] According to the mean and the standard deviation, static final measurement weight data, low-speed final measurement weight data or high-speed final measurement weight data are processed to obtain a weight lower threshold and a weight upper threshold corresponding to the static final measurement weight data, the low-speed final measurement weight data or the high-speed final measurement weight data;

[0190] The static final measurement weight data, the low-speed final measurement weight data or the high-speed final measurement weight data are compared with the corresponding weight lower threshold and weight upper threshold respectively;

[0191] If the weight is between the weight lower threshold and the weight upper threshold, it is determined as normal data, otherwise, it is determined as an abnormal value, and the elimination processing is performed to obtain corresponding static final measurement weight normal data, low-speed final measurement weight normal data or high-speed final measurement weight normal data in a preset time period;

[0192] According to the static final measurement weight normal data, the low-speed final measurement weight normal data or the high-speed final measurement weight normal data, the preset isolation forest algorithm is processed to obtain a data anomaly score;

[0193] If the data anomaly score is greater than a preset abnormal alarm threshold, the elimination processing is performed to obtain corresponding static final measurement weight valid data, low-speed final measurement weight valid data or high-speed final measurement weight valid data in a preset time period;

[0194] The static final measurement weight valid data, the low-speed final measurement weight valid data or the high-speed final measurement weight valid data are data packed in a preset format and sent to a management end for display.

[0195] It should be noted that, in order to detect the final measurement weight data obtained by the adaptive filtering compensation processing, two dimensions of determining the threshold based on 3 times the standard deviation and determining the data anomaly score based on the path length of the isolation forest algorithm are adopted for detection, wherein the weight lower threshold is the mean minus 3 times the standard deviation, and the weight upper threshold is the mean plus 3 times the standard deviation; the preset isolation forest algorithm calculates the data anomaly score based on the path length, and the higher the score, the more likely it is an abnormal value; the combination of the two solves the extreme abnormal value and can identify the local abnormal value; finally, the static final measurement weight valid data, the low-speed final measurement weight valid data or the high-speed final measurement weight valid data that pass the detection are data packed in a preset format and sent to a management end for display.

[0196] According to the embodiment of the application, the method further comprises:

[0197] The vehicle parking inclination is compared with a preset inclination threshold;

[0198] If the vehicle parking inclination is greater than the preset inclination threshold, a weighing abnormality early warning response is outputted;

[0199] If the vehicle parking inclination is less than or equal to a preset inclination threshold, the vehicle parking inclination and the final weight data are processed to obtain final weight optimization data.

[0200] It should be noted that the vehicle parking inclination directly affects the accuracy of the weight measurement, and the gravity component correction needs to be performed. If the inclination is too large, the weighing is rejected, and a warning response is output. If it is not greater than the preset inclination threshold, the vehicle parking inclination and the final weight data are processed to obtain final weight optimization data, wherein the vehicle parking inclination includes a transverse inclination θ and a longitudinal inclination γ, and the final weight optimization data is the final weight data / cosθcosγ.

[0201] According to the embodiment of the application, the method further comprises:

[0202] The static final weight effective data, low-speed final weight effective data or high-speed final weight effective data corresponding to the preset time period are subjected to mean value processing to obtain static final weight mean value data, low-speed final weight mean value data or high-speed final weight mean value data;

[0203] The static final weight mean value data, low-speed final weight mean value data or high-speed final weight mean value data are compared with corresponding preset mean value thresholds to obtain a mean value comparison result;

[0204] The static final weight effective data, low-speed final weight effective data or high-speed final weight effective data corresponding to the preset time period are subjected to peak extraction processing to obtain static final weight peak value data, low-speed final weight peak value data or high-speed final weight peak value data;

[0205] The static final weight peak value data, low-speed final weight peak value data or high-speed final weight peak value data are compared with corresponding peak value thresholds to obtain a peak value comparison result;

[0206] The static final weight effective data, low-speed final weight effective data or high-speed final weight effective data corresponding to the preset time period are compared to obtain a static final weight change rate, a low-speed final weight change rate or a high-speed final weight change rate;

[0207] The static final weight change rate, low-speed final weight change rate or high-speed final weight change rate is compared with a corresponding change rate threshold to obtain a change rate comparison result;

[0208] If the mean value comparison result, the peak value comparison result and the change rate comparison result are all not greater than the threshold, no overload warning is output.

[0209] On the contrary, an overload warning is output.

[0210] It should be noted that, in order to accurately evaluate the vehicle overload problem, the embodiment is evaluated from three dimensions of mean value comparison, peak value comparison and weight change rate, and the mean value, peak value and change rate are compared with the corresponding threshold value respectively to obtain the threshold comparison result, including greater than the threshold value or not greater than the threshold value, wherein the change rate is the ratio of the absolute value of the difference between the effective data of the final weight at the next time point and the effective data of the final weight at the previous time point to the effective data of the final weight at the previous time point, and finally, the and operation is performed. Only when all of them are not greater than the threshold value, it is determined that it is not overloaded, otherwise, as long as one of them exceeds the threshold value, it is determined that it is overloaded.

[0211] It is worth mentioning that, according to the embodiment of the application, further comprising:

[0212] The tank body speed is compared with a preset dynamic speed threshold value;

[0213] If the tank body speed is less than the preset dynamic speed threshold value, the main frequency component is compared with a preset main frequency threshold value, and the low frequency energy ratio is compared with a preset low frequency energy ratio threshold value;

[0214] If the main frequency component is greater than or equal to the preset main frequency threshold value, or the low frequency energy ratio is less than or equal to the preset low frequency energy ratio threshold value, it is determined that the rotation state of the tank body is a transition state;

[0215] According to the transition state, a corresponding weighing management strategy is determined.

[0216] It should be noted that the transition state refers to the dynamic change process of the equipment from one stable state (static, low speed rotation, high speed rotation) to another stable state, which has the characteristics of temporality, dynamics and uncertainty. Accurate identification of the transition state can avoid misjudgment (such as misjudgment of speed fluctuation and sensor data jump as an abnormality of the stable state). First, the tank body speed is compared with a threshold value to determine that the speed is very low (close to static). However, only the speed cannot completely determine whether it is "stable and static". Then, the main frequency component and the low frequency energy ratio are compared with a threshold value respectively. As long as the main frequency component is greater than or equal to the preset main frequency threshold value, or the low frequency energy ratio is less than or equal to the preset low frequency energy ratio threshold value, it is determined that the rotation state of the tank body is a transition state, otherwise it is a static state.

[0217] The third aspect of the application provides a readable storage medium, wherein a dynamic compensation-based construction site intelligent weighing management method program is stored in the readable storage medium. When the dynamic compensation-based construction site intelligent weighing management method program is executed by a processor, the steps of the dynamic compensation-based construction site intelligent weighing management method according to any one of the above are implemented.

[0218] The application discloses a construction site intelligent weighing management method and system based on dynamic compensation and a medium.

[0219] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other manners. The above described device embodiments are merely schematic, for example, the division of the units is only a logical function division, and there can be another division manner in actual implementation, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling, or direct coupling or communication connection between the components can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0220] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units; they can be located in one place, or distributed on multiple network units; and part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0221] In addition, each functional unit in each embodiment of the application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; and the integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0222] Those skilled in the art can understand that all or part of the steps of the above method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a readable storage medium, and the program is executed to perform the steps of the above method embodiments; and the foregoing storage medium includes mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks or optical disks, and various storage media that can store program codes.

[0223] Alternatively, the above-mentioned integrated unit of the present application, if realized in the form of a software function module and sold or used as an independent product, can also be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, which is stored in a storage medium, includes several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute all or part of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: mobile storage devices, ROM, RAM, magnetic or optical disks, and various media that can store program codes.

Claims

1. A method for intelligent weighing management of construction sites based on dynamic compensation, characterized in that: Includes the following steps: Obtain the weighing status of the weighing equipment, including whether it is weighing or not. If the weighing status is "not weighing", then perform zero-point calibration; If the weighing status is "weighing", then the corresponding original weight dataset, vibration amplitude, tank rotation speed and vehicle parking tilt angle are obtained according to the preset sampling frequency. The rotational state of the tank is obtained by processing the tank's rotational speed and vibration amplitude. The original weight dataset is subjected to adaptive filtering compensation processing based on the rotation state to obtain the final measured weight data. The final measured weight data is subjected to outlier detection, and intelligent weighing management is performed based on the detection results. The step of performing adaptive filtering compensation processing on the original weight dataset based on the rotation state to obtain the final measured weight data includes: The final weight data includes static final weight data, low-speed final weight data, or high-speed final weight data. The vibration compensation quality data is obtained by processing the vibration amplitude and tank rotation speed in combination with a preset compensation coefficient. If the rotation state is a stationary state, the original weight dataset is averaged according to a preset time window to obtain static final weight data. If the rotation state is a low-speed rotation state, then query the preset adaptive filtering compensation processing database to obtain the corresponding low-speed process noise covariance and low-speed measurement noise covariance. The original weight dataset corresponding to the preset time window is combined with the low-speed process noise covariance and the low-speed measurement noise covariance for adaptive filtering and compensation processing to obtain the low-speed compensated weight dataset. The low-speed compensated weight dataset is subtracted from the vibration compensated mass data and the mean is calculated to obtain the final low-speed weight data. If the rotation state is a high-speed rotation state, then query the preset adaptive filtering compensation processing database to obtain the corresponding high-speed process noise covariance and high-speed measurement noise covariance. The original weight dataset corresponding to the preset time window is combined with the high-speed process noise covariance and the high-speed measurement noise covariance for adaptive filtering and compensation processing to obtain the high-speed compensated weight dataset. The high-speed compensation weight dataset is subtracted from the vibration compensation mass data and the mean is calculated to obtain the final high-speed measurement weight data.

2. The intelligent weighing management method for construction sites based on dynamic compensation according to claim 1, characterized in that, The acquisition of the weighing status of the weighing device, including whether it is weighing or not, includes: Obtain real-time weight data from the weighing equipment; If the real-time weight data is greater than or equal to the preset weight threshold, then the weighing status is "weighing in progress". If the real-time weight data is less than the preset weight threshold, the weighing status is "not weighing". If the weighing status is not weighing, the real-time weight data is processed to obtain the average real-time weight within a preset time period. If the real-time average weight is greater than the dynamic zero-point drift threshold, the sensor automatic calibration process is activated.

3. The intelligent weighing management method for construction sites based on dynamic compensation according to claim 2, characterized in that, Also includes: Acquire real-time environmental monitoring data of the weighing equipment, including real-time temperature and humidity; The real-time temperature and real-time humidity are compared with the corresponding preset standard temperature and preset standard humidity to obtain the corresponding temperature deviation value and humidity deviation value; The temperature deviation value and humidity deviation value are respectively weighted and summed with the corresponding preset temperature influence gradient value and preset humidity influence gradient value to obtain the environmental influence coefficient. Obtain the sensor's power-on duration and preset lifespan; The power-on time is compared with the preset lifespan to obtain sensor aging evaluation parameters, and then multiplied with the preset aging influence gradient value to obtain the aging influence coefficient. The preset initial zero-point drift threshold is corrected by combining the environmental impact coefficient and aging impact coefficient with a preset adaptive adjustment factor to obtain the dynamic zero-point drift threshold.

4. The intelligent weighing management method for construction sites based on dynamic compensation according to claim 3, characterized in that, The process of processing the tank's rotational speed and vibration amplitude to obtain the tank's rotational state includes: The vibration amplitude is subjected to Fourier transform to obtain the vibration frequency; Feature extraction is performed based on the vibration frequency to obtain the dominant frequency component and the low-frequency energy ratio; The tank rotation speed, main frequency component and low frequency energy ratio are input into a preset tank rotation state evaluation model for processing to obtain the tank rotation state evaluation parameters; The rotation state evaluation parameters are compared with the first preset rotation state parameter threshold and the second preset rotation state parameter threshold to obtain the rotation state of the tank, including a stationary state, a low-speed rotation state, or a high-speed rotation state.

5. The intelligent weighing management method for construction sites based on dynamic compensation according to claim 1, characterized in that, The step of performing outlier detection on the final measured weight data and performing intelligent weighing management based on the detection results includes: Feature extraction is performed on static final weight data, low-speed final weight data, or high-speed final weight data within a preset time period to obtain the corresponding mean and standard deviation. The mean and standard deviation are processed to obtain the lower and upper weight thresholds corresponding to the static final weight data, low-speed final weight data, or high-speed final weight data. The static final weight data, low-speed final weight data, or high-speed final weight data are compared with the corresponding lower weight threshold and upper weight threshold, respectively. If the weight is between the lower and upper weight thresholds, it is considered normal data; otherwise, it is considered an outlier and is removed. This process yields the corresponding static final weight normal data, low-speed final weight normal data, or high-speed final weight normal data within a preset time period. Based on the static final weight normal data, low-speed final weight normal data, or high-speed final weight normal data, the data anomaly score is obtained by processing the data using a preset isolated forest algorithm. If the abnormal data score is greater than the preset abnormal warning threshold, the data will be removed to obtain the corresponding static final weight valid data, low-speed final weight valid data or high-speed final weight valid data within the preset time period. The valid static final weight data, valid low-speed final weight data, or valid high-speed final weight data are packaged according to a preset format and sent to the management terminal for display.

6. The intelligent weighing management method for construction sites based on dynamic compensation according to claim 4, characterized in that, Also includes: The vehicle parking angle is compared with a preset tilt angle threshold; If the vehicle's parking angle is greater than a preset angle threshold, an abnormal weighing warning response will be output. If the vehicle parking angle is less than or equal to a preset angle threshold, then the vehicle parking angle and final weight data are processed to obtain optimized final weight data.

7. The intelligent weighing management method for construction sites based on dynamic compensation according to claim 1, characterized in that, Also includes: The effective static final weight data, effective low-speed final weight data, or effective high-speed final weight data within a preset time period are averaged to obtain the average static final weight data, average low-speed final weight data, or average high-speed final weight data. The average static final weight data, the average low-speed final weight data, or the average high-speed final weight data are compared with the corresponding preset average thresholds to obtain the average comparison results. Peak extraction processing is performed on the static final weight valid data, low-speed final weight valid data or high-speed final weight valid data corresponding to the preset time period to obtain static final weight peak data, low-speed final weight peak data or high-speed final weight peak data. The peak data of static final weight measurement, peak data of low-speed final weight measurement, or peak data of high-speed final weight measurement are compared with the corresponding peak thresholds to obtain peak comparison results. The static final weight change rate, low-speed final weight change rate, or high-speed final weight change rate are obtained by comparing the corresponding static final weight change data, low-speed final weight change rate, or high-speed final weight change rate within a preset time period. The static final weight change rate, low-speed final weight change rate, or high-speed final weight change rate are compared with the corresponding change rate thresholds to obtain the change rate comparison results. If the mean comparison result, peak value comparison result, and rate of change comparison result are all not greater than the threshold, then no overload warning will be output. Conversely, an overload warning will be issued.

8. A construction site intelligent weighing management system based on dynamic compensation, characterized in that: The system includes a memory and a processor. The memory contains a program for a dynamic compensation-based intelligent weighing management method for construction sites. When the processor executes the program, the dynamic compensation-based intelligent weighing management method for construction sites performs the following steps: Obtain the weighing status of the weighing equipment, including whether it is weighing or not. If the weighing status is "not weighing", then perform zero-point calibration; If the weighing status is "weighing", then the corresponding original weight dataset, vibration amplitude, tank rotation speed and vehicle parking tilt angle are obtained according to the preset sampling frequency. The rotational state of the tank is obtained by processing the tank's rotational speed and vibration amplitude. The original weight dataset is subjected to adaptive filtering compensation processing based on the rotation state to obtain the final measured weight data. The final measured weight data is subjected to outlier detection, and intelligent weighing management is performed based on the detection results. The step of performing adaptive filtering compensation processing on the original weight dataset based on the rotation state to obtain the final measured weight data includes: The final weight data includes static final weight data, low-speed final weight data, or high-speed final weight data. The vibration compensation quality data is obtained by processing the vibration amplitude and tank rotation speed in combination with a preset compensation coefficient. If the rotation state is a stationary state, the original weight dataset is averaged according to a preset time window to obtain static final weight data. If the rotation state is a low-speed rotation state, then query the preset adaptive filtering compensation processing database to obtain the corresponding low-speed process noise covariance and low-speed measurement noise covariance. The original weight dataset corresponding to the preset time window is combined with the low-speed process noise covariance and the low-speed measurement noise covariance for adaptive filtering and compensation processing to obtain the low-speed compensated weight dataset. The low-speed compensated weight dataset is subtracted from the vibration compensated mass data and the mean is calculated to obtain the final low-speed weight data. If the rotation state is a high-speed rotation state, then query the preset adaptive filtering compensation processing database to obtain the corresponding high-speed process noise covariance and high-speed measurement noise covariance. The original weight dataset corresponding to the preset time window is combined with the high-speed process noise covariance and the high-speed measurement noise covariance for adaptive filtering and compensation processing to obtain the high-speed compensated weight dataset. The high-speed compensation weight dataset is subtracted from the vibration compensation mass data and the mean is calculated to obtain the final high-speed measurement weight data.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for a construction site intelligent weighing management method based on dynamic compensation. When the program is executed by a processor, it implements the steps of the construction site intelligent weighing management method based on dynamic compensation as described in any one of claims 1 to 7.

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