CN114485878B - Method and system for measuring dynamic weight of vehicle based on dynamic energy spectrum analysis - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01G—WEIGHING
- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/02—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles
- G01G19/03—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing during motion
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Abstract
The invention discloses a method for measuring vehicle dynamic weight based on dynamic energy spectrum analysis, which mainly comprises the following steps: converting the vibration signal into an energy signal, and mapping the energy signal into a frequency domain-time domain diagram to form an energy spectrum of the vibration signal; based on a preset vehicle dynamic energy spectrum recognition model, dynamically recognizing and separating dynamic energy spectrum signals of each vehicle in real time in a frequency domain-time domain diagram; and calculating the energy spectrum signal intensity based on the separated dynamic energy spectrum signals of each vehicle to obtain the dynamic weight information of the vehicle. The invention firstly acquires all the composite vibration signals generated by the vehicles, then converts the vibration signals into energy spectrums, then analyzes and identifies the energy spectrums, can analyze the energy spectrums generated by each vehicle, can distinguish the individual vehicles, and obtains the dynamic weight of the vehicles through the time distribution of the energy spectrums.
Description
Technical Field
The invention relates to the technical field of vehicle dynamic weight measurement, in particular to a vehicle dynamic weight measurement technology based on dynamic energy spectrum analysis.
Background
The vehicle dynamic weight measurement technique (WIM) refers to a technique of measuring the dynamic weight of a running vehicle in real time. The dynamic weight pressure of the vehicle is equivalent to the pressure generated on the road ground during the movement of the vehicle, and the pressure generated on the road ground during the movement of the vehicle cannot be simply replaced by the weight of the vehicle when the vehicle is stationary, and the pressure is closely related to the condition of the road ground and the speed of the vehicle besides the weight of the whole vehicle when the vehicle is stationary. The WIM technology is widely applied to the fields of road operation and maintenance management, bridge maintenance and the like. The vehicle itself is a complex elastic system, so that the vehicle generates continuous mechanical vibration during the running on the ground, and the vibration causes continuous change of the contact surface between the vehicle and the ground, so that the dynamic weight continuously fluctuates around a certain constant value, which is why the dynamic weight cannot be accurately measured.
Conventional vehicle dynamic weight measurement techniques suffer from several problems during application:
1) Conventional vehicle dynamic weight measurement typically uses a piezostrain resistor or piezoelectric quartz as a measurement sensor, and usually only spot-like or linear forces can be measured.
2) At present, vehicles with two or more axles are common in road operation, an indirect measurement method is adopted in traditional vehicle dynamic weight measurement, impulse force generated when a vehicle single axle passes is measured to calculate the vehicle single axle dynamic weight, and the vehicle dynamic weight is calculated after combination. However, in some dynamic weighing cases, it can be seen that when the driver passes through the conventional dynamic weighing system, the vehicle is instantaneously fueled or braked, which can have an effect on the measured value of approximately 20%, resulting in a great deviation of the measured result.
3) Conventional dynamic weight measurement systems are mostly instantaneous value measurement. In the vehicle movement process, a plurality of factors influencing the dynamic weight can be changed continuously along with the vehicle movement. The instantaneous value measurement does not truly represent the dynamic weight of the current vehicle.
4) Traditional dynamic weight equipment vendors, when implementing equipment installation, will make appropriate modifications to the equipment installation measurement environment. The measuring equipment is large in size and high in cost, the construction cost of the measuring system is high, a special construction installation space is needed, the existing road is required to be destroyed, and meanwhile, a special construction period is needed. The measurement accuracy of the system is affected by the installation environment and the installation condition, and after the installation is completed, the measurement accuracy under the experimental environment (under ideal conditions) is not achieved in general. In order to ensure the accuracy of the measurement, some preconditions are used for limitation (e.g., controlling the running speed and direction of the vehicle, measuring the flatness and levelness of the road surface, etc.), and the correction of the instantaneous measurement value is performed by measuring some transient changes in the passing process of the vehicle. The measurement method has higher requirements on measurement conditions, so that the construction cost of the measurement system is also high.
5) Traditional vehicle dynamic weight measurement technology can only measure one passing vehicle at a time, and does not support simultaneous measurement of multiple vehicles.
6) When the traditional vehicle dynamic weight measurement is carried out, the running speed and the lane of the vehicle are definitely limited, the speed and the direction of the vehicle are strictly required when the vehicle passes, otherwise, larger measurement deviation is generated, the measured weight value and the dynamic weight of the vehicle in actual running have certain deviation, and the situation of the vehicle in running cannot be completely reflected.
Disclosure of Invention
The invention aims to solve the problems, and provides a method for measuring dynamic weight of a vehicle (one or more) running on a specific road (or bridge) at the same time, which can be used for monitoring overload of the vehicle or health and safety of the road (or bridge).
In the road vehicle motion model, the vehicle may be regarded as being formed by connecting a plurality of rigid bodies with springs perpendicular to the ground. In road surface driving, the vehicle generates continuous mechanical vibration (which is formed by combining a plurality of mechanical vibration waves with different frequencies) and is transmitted to the ground through the contact surface of the wheels and the ground, the continuous mechanical vibration is measured by a vibration sensor arranged on the ground, and the integral of the amplitude of the vibration in time and the dynamic weight of the vehicle are in a linear relation.
By analyzing the vibration waveform, we can find that: the amplitude of the vibration waveform and the ground pressure of the vehicle are related; the amplitude envelope area of the vibration waveform has correlation with the dynamic weight of the vehicle; the peak value of the vibration waveform has correlation with the axle of the vehicle; each frequency characteristic component of the vibration waveform in the frequency domain has correlation with the structure of the vehicle suspension system, and can be used for vehicle type identification; the effective length of the vibration waveform is correlated with the vehicle speed.
Furthermore, the vibration waveform is analyzed in real time through a design algorithm, so that the information of the characteristics of the moving vehicle type, the speed and the dynamic weight can be obtained at the same time.
However, in the case of passing a plurality of vehicles on the road, the sensor measures the superposition of vibrations (complex vibrations) generated by passing all the vehicles on the road at the present moment. Because of the wave characteristics of the mechanical wave, the wave peaks and wave troughs cancel each other at a certain moment, and therefore, the continuous integration of the complex vibration over time and the dynamic weight of the vehicle have no direct linear relationship.
However, according to the law of conservation of energy, the sum of the vibrational energy measured by the sensor over time is still the sum of all the vibrational energy generated by the vehicle.
Therefore, the dynamic energy spectrum is introduced into the invention, and the dynamic weight of the vehicle is obtained by analyzing and identifying the energy spectrum of the acquired vibration signal, analyzing each energy spectrum generated by the vehicle and distributing the energy spectrum in time. The technical scheme adopted by the invention is as follows:
a method for measuring the dynamic weight of a vehicle based on dynamic energy spectrum analysis, comprising the following,
s1, obtaining vibration signals generated by all vehicles passing through;
s2, converting the vibration signal into an energy signal, and mapping the energy signal into a frequency domain-time domain diagram to form an energy spectrum of the vibration signal;
s3, dynamically identifying and separating dynamic energy spectrum signals of each vehicle in real time in a frequency domain-time domain diagram based on a preset vehicle dynamic energy spectrum identification model;
s4, calculating the energy spectrum signal intensity based on the separated dynamic energy spectrum signals of each vehicle, and obtaining the dynamic weight information of the vehicle.
The dynamic energy spectrum identification model of the vehicles of different vehicle types comprises the following specific contents: firstly, acquiring characteristic energy spectrums under the condition that vehicles of different vehicle types independently pass at a certain speed at a constant speed, establishing association relation between the characteristic energy spectrums and road surfaces, vehicle types, vehicle speeds and vehicle weights, and constructing characteristic dynamic energy spectrum libraries of the vehicles of different vehicle types; and then adopting a machine learning algorithm to construct a vehicle dynamic energy spectrum identification model under the conditions of different road surfaces, different vehicle speeds and different vehicle weights.
As described above, all the generated composite vibration signals passing through the vehicle are acquired, the vibration signals are converted into energy spectrums, the energy spectrums are analyzed and identified, the energy spectrums generated by each passing through the vehicle are analyzed, and the dynamic weight of the vehicle is obtained through the time distribution of the energy spectrums.
By adopting the technical scheme, the invention has the following beneficial effects:
1. the method for measuring the dynamic weight of the vehicle based on dynamic energy spectrum analysis firstly acquires all the generated complex vibration signals of the vehicle, then converts the vibration signals into energy spectrums, then analyzes and identifies the energy spectrums, can analyze the energy spectrums generated by each vehicle, can distinguish the vehicle individuals, and obtains the dynamic weight of the vehicle according to the time distribution condition of the energy spectrums.
2. The method can be used for measuring when multiple vehicles pass so as to obtain the dynamic weight of each vehicle; the method can also be used for measuring when a bicycle passes so as to remove part of interference data and improve the measurement accuracy of the dynamic weight of the bicycle.
Drawings
FIG. 1 is a flow chart of a method of measuring vehicle dynamic weight according to the present invention.
FIG. 2 is a system block diagram of a vehicle dynamic weight measurement system of the present invention.
Detailed Description
The following is a further description of the specific embodiments of the invention with reference to the accompanying drawings.
Example 1
As shown in fig. 1, the method for measuring the dynamic weight of the vehicle based on the dynamic energy spectrum analysis of the present embodiment 1 includes the following,
s1, obtaining vibration signals generated by all vehicles passing through;
s2, converting the vibration signal into an energy signal, and mapping the energy signal into a frequency domain-time domain diagram to form an energy spectrum of the vibration signal;
s3, dynamically identifying and separating dynamic energy spectrum signals of each vehicle in real time in a frequency domain-time domain diagram based on a preset vehicle dynamic energy spectrum identification model;
s4, calculating the energy spectrum signal intensity based on the separated dynamic energy spectrum signals of each vehicle, and obtaining the dynamic weight information of the vehicle.
In step S1, the vibration signal is obtained by measuring the road surface or bridge road surface measurement area with a vibration sensor or a vibration sensor array, and then uploading the vibration signal to the processing center.
The specific content of step S2 is as follows: firstly, carrying out consistency processing on vibration signals measured by a vibration sensor or a vibration sensor array; and converting the vibration signal into an energy signal, and mapping the energy signal into a frequency domain-time domain diagram through a short-time Fourier transform algorithm to form an energy spectrum of the vibration signal divided according to frequency.
In step S3, the specific content of the dynamic energy spectrum recognition model of the vehicles of different vehicle types is as follows: firstly, acquiring characteristic energy spectrums under the condition that vehicles of different vehicle types independently pass at a certain speed at a constant speed, establishing association relation between the characteristic energy spectrums and road surfaces, vehicle types, vehicle speeds and vehicle weights, and constructing characteristic dynamic energy spectrum libraries of the vehicles of different vehicle types; and then adopting a machine learning algorithm to construct a vehicle dynamic energy spectrum identification model under the conditions of different road surfaces, different vehicle speeds and different vehicle weights.
As described above, all the generated composite vibration signals passing through the vehicle are acquired, the vibration signals are converted into energy spectrums, the energy spectrums are analyzed and identified, the energy spectrums generated by each passing through the vehicle are analyzed, and the dynamic weight of the vehicle is obtained through the time distribution of the energy spectrums.
As an option, based on the foregoing example, in an example, step S1 further obtains license plate information of the vehicle passing through the measurement area output by the license plate recognition unit provided at the entrance of the measurement area. Therefore, the vehicle information can be input, the vehicle information including license plates, dynamic weight, vehicle speed and the like is output, the license plates can be compared with a continuously updated vehicle information base, the vehicle types corresponding to the license plates can be obtained in real time, and then the vehicle type information is combined for recognition and analysis operation. The following will specifically explain.
Firstly, preparing an algorithm, namely preparing a vehicle dynamic energy spectrum identification model before carrying out dynamic energy spectrum identification and analysis, wherein the model comprises the following specific steps:
1. the method comprises the steps of establishing a characteristic dynamic energy spectrum library of different vehicle types, under the condition that a specific vehicle singly passes through a measured pavement at a constant speed, obtaining a characteristic energy spectrum of the vehicle, and establishing association relations between the characteristic energy spectrum and the pavement, the vehicle speed, the vehicle weight and the vehicle types in a database by analyzing the characteristic energy spectrum, wherein the characteristic energy spectrum comprises information related to the measured pavement, the vehicle speed, the vehicle dynamic weight and the vehicle types;
2. then, a vehicle dynamic energy spectrum model identification algorithm under the conditions of different road surfaces, different vehicle speeds and different vehicle weights is constructed through a machine learning algorithm.
Secondly, the construction process is as follows:
1. the method comprises the steps of selecting a hardened highway with a measurement area of two-way two lanes, wherein the length of a road section is 40 meters, the width of the road is about 20 meters, and taking the area as the measurement area; wherein the measuring area needs to be longer and the length is more than 30 meters;
2. a camera with license plate recognition function is arranged at the entrance end of the measuring area so as to recognize license plate information of passing vehicles;
3. 4 triaxial high-precision vibration sensors are adopted to construct a sensor array, the sampling frequency is 200Hz, and the sensors are connected with a computing host through wireless WIFI;
4. the computing host is an industrial computer notebook computer, 8G memory, i5CPU and main frequency 2Ghz;
5. the computer runs algorithm software, calculates the dynamic weight and speed of the running vehicle in real time, binds the running vehicle with the license plate, and the vehicle information base can update related information such as the license plate and the like with transmission data of a traffic system and the like.
Thirdly, environmental index calibration is needed after the on-site monitoring system is installed, and specifically, the method comprises the following steps:
1. according to the sampling frequency, sampling precision and road surface basic smoothness of the field sensor, selecting a required calculation step length and a calculation period of a short-time Fourier transform algorithm through a parameter selection algorithm, wherein the purpose is to ensure that a converted result can have clear contrast and resolution;
2. and through calibration, the vehicle passes through the monitoring road section under various conditions to acquire the reference monitoring data.
Fourthly, monitoring the dynamic weight of the vehicle, and monitoring after calibration is completed, wherein the method comprises the following steps:
1. firstly, carrying out data consistency processing on the acquired result of the vibration sensor; because the influence of the vertical distance between the vehicle and the sensor needs to be considered, the algorithm compensation is carried out in a vibration sensor array (multiple vibration sensors) mode, and the vibration signal acquisition precision is improved;
2. then converting the vibration signal into an energy signal, and mapping the energy signal into a frequency domain-time domain diagram through a short-time Fourier transform algorithm to construct an energy spectrum of the vibration signal divided according to frequency;
3. based on a characteristic energy spectrum model algorithm, dynamically identifying and separating dynamic energy spectrum signals of each vehicle in real time in a frequency domain-time domain diagram, wherein the energy spectrum signals are energy spectrum signals of the vehicle when passing through in a distribution frequency domain and a time domain of the energy signals;
4. finally, by analyzing and calculating the energy spectrum signals of the independent vehicles, the vehicle speed and the dynamic weight information of the vehicle quantity can be obtained. As previously mentioned, the total signal intensity of the individual vehicle spectrum is linear with the dynamic weight of the vehicle, so the dynamic weight of the vehicle can be calculated from the total signal intensity of the spectrum. And the width and time of the energy spectrum signal of the vehicle are related, and the speed of the vehicle when the vehicle passes can be calculated by knowing the passing distance and time. By selecting the vibration sensor array, the vehicle speed and the vehicle running direction can be calculated, and the length of the vehicle axle can be read.
As described above, the overall calculation process uses signal extraction and machine learning techniques. When the method is implemented on site, the mechanical vibration wave signals are captured in the whole measuring area in a vibration sensor array mode, the inherent deviation of the sensors can be counteracted, and the real-time measuring resolution and measuring precision of multiple vehicles are improved. The measuring area is usually selected from hardened road surfaces or large bridge road surfaces, and the resonance effect of the axle resonance body is good under the condition, so that the measurement is convenient. The vibration sensor array can work together with auxiliary sensing equipment such as license plate recognition equipment and the like to carry out license plate recognition, and measurement resolution and measurement accuracy are improved.
In addition, the vibration sensor used by the measuring method can be purchased directly in the market under the condition of meeting the precision requirement; the road outer roadbed is usually adopted for installation, and the road surface is not required to be specially modified. Therefore, the measuring method has low implementation cost, is quick and convenient to install, does not damage the road surface, and can be immediately used after being installed. The multi-vehicle dynamic weight simultaneous measurement is supported, and the running condition of the road traffic flow is not influenced. The consistency of the measurement data is good, the weight of each shaft can be measured at the same time, the weight measurement of the whole vehicle is finished at one time, and the measurement result is not interfered by the driving habit of a driver. After the system is installed and stably operates, the vehicle in operation can not be interfered, and the measurement accuracy can be continuously improved through continuous algorithm optimization.
Example 2
This embodiment 2 is the system of the foregoing embodiment 1, and please refer to the foregoing embodiment 1 for no further explanation.
As shown in fig. 2, the dynamic weight measurement system for a vehicle based on dynamic energy spectrum analysis of this embodiment 2, including the following,
the vibration acquisition module is used for acquiring vibration signals generated by all vehicles passing through;
the energy spectrum conversion module is used for converting the vibration signal into an energy signal and mapping the energy signal into a frequency domain-time domain graph to form an energy spectrum of the vibration signal;
the identification and separation module is used for dynamically identifying and separating dynamic energy spectrum signals of each vehicle in real time in a frequency domain-time domain diagram based on a preset vehicle dynamic energy spectrum identification model;
the weight obtaining module is used for calculating the energy spectrum signal intensity based on the separated dynamic energy spectrum signals of each vehicle and obtaining the dynamic weight information of the vehicle.
In the vibration acquisition module, vibration signals are obtained by measuring a road surface or bridge road surface measuring area through a vibration sensor array.
The specific contents of the energy spectrum conversion module are as follows: for first performing a consistency process on vibration signals measured by the vibration sensor or the vibration sensor array; and converting the vibration signal into an energy signal, and mapping the energy signal into a frequency domain-time domain diagram through a short-time Fourier transform algorithm to form an energy spectrum of the vibration signal divided according to frequency.
In the identification and separation module, the dynamic energy spectrum identification model of the vehicles of different vehicle types comprises the following specific contents: firstly, acquiring characteristic energy spectrums under the condition that vehicles of different vehicle types independently pass at a certain speed at a constant speed, establishing association relation between the characteristic energy spectrums and road surfaces, vehicle types, vehicle speeds and vehicle weights, and constructing characteristic dynamic energy spectrum libraries of the vehicles of different vehicle types; and then adopting a machine learning algorithm to construct a vehicle dynamic energy spectrum identification model under the conditions of different road surfaces, different vehicle speeds and different vehicle weights.
As described above, all the generated plural vibration signals passing through the vehicle are acquired first, then the vibration signals are converted into energy spectrums, then the energy spectrums are analyzed and identified, the energy spectrums generated by each passing through the vehicle are analyzed, and then the dynamic weight of the vehicle is obtained through the time distribution of the energy spectrums.
As an option, based on the foregoing example, in one example, the obtaining vibration module is further configured to obtain license plate information of the vehicle passing through the measurement area output by a license plate recognition unit disposed at an entrance of the measurement area. Therefore, the vehicle information can be input, the vehicle information including license plates, dynamic weight, vehicle speed and the like is output, the license plates can be compared with a continuously updated vehicle information base, the vehicle types corresponding to the license plates can be obtained in real time, and then the vehicle type information is combined for recognition and analysis operation.
It should be noted that, the examples of the above embodiments may be preferably one or more than two of them combined according to actual needs, and the examples are illustrated by a set of drawings combining technical features, which are not described in detail herein.
The foregoing description is directed to the details and illustrations of the preferred embodiments of the invention, but these descriptions are not intended to limit the scope of the invention claimed, and all equivalent changes or modifications that may be accomplished under the teachings of the invention should be construed to fall within the scope of the invention as defined by the appended claims.
Claims (8)
1. A method for measuring the dynamic weight of a vehicle based on dynamic energy spectrum analysis is characterized in that: including the following that are included in the description,
s1, obtaining vibration signals generated by all vehicles passing through;
s2, converting the vibration signal into an energy signal, and mapping the energy signal into a frequency domain-time domain diagram to form an energy spectrum of the vibration signal;
the specific content of the step S2 is as follows: firstly, carrying out consistency processing on vibration signals measured by a vibration sensor or a vibration sensor array; converting the vibration signal into an energy signal, and mapping the energy signal into a frequency domain-time domain diagram through a short-time Fourier transform algorithm to form an energy spectrum of the vibration signal divided according to frequency;
s3, dynamically identifying and separating dynamic energy spectrum signals of each vehicle in real time in a frequency domain-time domain diagram based on a preset vehicle dynamic energy spectrum identification model;
in the step S3, the specific content of the dynamic energy spectrum recognition model of the vehicles of different vehicle types is as follows: firstly, acquiring characteristic energy spectrums under the condition that vehicles of different vehicle types independently pass at a certain speed at a constant speed, establishing association relation between the characteristic energy spectrums and road surfaces, vehicle types, vehicle speeds and vehicle weights, and constructing characteristic dynamic energy spectrum libraries of the vehicles of different vehicle types; then adopting a machine learning algorithm to construct a vehicle dynamic energy spectrum identification model under the conditions of different road surfaces, different vehicle speeds and different vehicle weights;
s4, calculating the energy spectrum signal intensity based on the separated dynamic energy spectrum signals of each vehicle, and obtaining the dynamic weight information of the vehicle.
2. The method for dynamic weight measurement of a vehicle based on dynamic energy spectrum analysis of claim 1, wherein: in the step S1, the vibration signal is measured by using a vibration sensor or a vibration sensor array in a road surface or bridge road surface measurement area.
3. The method for dynamic weight measurement of a vehicle based on dynamic energy spectrum analysis of claim 1, wherein: in the step 1, license plate information of the vehicle passing through the measuring area, which is output by a license plate recognition unit arranged at the entrance of the measuring area, is also obtained.
4. The dynamic vehicle weight measurement system based on dynamic energy spectrum analysis of claim 1, wherein: including the following that are included in the description,
the vibration acquisition module is used for acquiring vibration signals generated by all vehicles passing through;
the energy spectrum conversion module is used for converting the vibration signal into an energy signal and mapping the energy signal into a frequency domain-time domain graph to form an energy spectrum of the vibration signal;
the identification and separation module is used for dynamically identifying and separating dynamic energy spectrum signals of each vehicle in real time in a frequency domain-time domain diagram based on a preset vehicle dynamic energy spectrum identification model;
the weight obtaining module is used for calculating the energy spectrum signal intensity based on the separated dynamic energy spectrum signals of each vehicle and obtaining the dynamic weight information of the vehicle.
5. The dynamic vehicle weight measurement system based on dynamic spectroscopy of claim 4, wherein: in the vibration acquisition module, the vibration signal is obtained by measuring a road surface or bridge road surface measuring area by using a vibration sensor or a vibration sensor array.
6. The dynamic energy spectrum analysis based vehicle dynamic weight measurement system of claim 5, wherein: the specific contents of the energy spectrum conversion module are as follows: for first performing a consistency process on vibration signals measured by the vibration sensor or the vibration sensor array; and converting the vibration signal into an energy signal, and mapping the energy signal into a frequency domain-time domain diagram through a short-time Fourier transform algorithm to form an energy spectrum of the vibration signal divided according to frequency.
7. The dynamic vehicle weight measurement system based on dynamic spectroscopy of claim 4, wherein: in the identification and separation module, the dynamic energy spectrum identification model of the vehicles of different vehicle types comprises the following specific contents: firstly, acquiring characteristic energy spectrums under the condition that vehicles of different vehicle types independently pass at a certain speed at a constant speed, establishing association relation between the characteristic energy spectrums and road surfaces, vehicle types, vehicle speeds and vehicle weights, and constructing characteristic dynamic energy spectrum libraries of the vehicles of different vehicle types; and then adopting a machine learning algorithm to construct a vehicle dynamic energy spectrum identification model under the conditions of different road surfaces, different vehicle speeds and different vehicle weights.
8. The dynamic vehicle weight measurement system based on dynamic spectroscopy of claim 4, wherein: and the vibration acquisition module is also used for acquiring license plate information of the vehicle passing through the measuring area, which is output by a license plate recognition unit arranged at the entrance of the measuring area.
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