CN112985608B - Method and system for monitoring temperature in asphalt conveying process - Google Patents
Method and system for monitoring temperature in asphalt conveying process Download PDFInfo
- Publication number
- CN112985608B CN112985608B CN202110137412.XA CN202110137412A CN112985608B CN 112985608 B CN112985608 B CN 112985608B CN 202110137412 A CN202110137412 A CN 202110137412A CN 112985608 B CN112985608 B CN 112985608B
- Authority
- CN
- China
- Prior art keywords
- temperature
- asphalt mixture
- asphalt
- contact
- monitoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000010426 asphalt Substances 0.000 title claims abstract description 144
- 238000012544 monitoring process Methods 0.000 title claims abstract description 95
- 238000000034 method Methods 0.000 title claims abstract description 58
- 239000000203 mixture Substances 0.000 claims abstract description 100
- 239000000523 sample Substances 0.000 claims abstract description 57
- 238000012545 processing Methods 0.000 claims abstract description 24
- 230000005855 radiation Effects 0.000 claims abstract description 24
- 239000000463 material Substances 0.000 claims abstract description 16
- 238000012549 training Methods 0.000 claims description 20
- 239000012774 insulation material Substances 0.000 claims description 17
- 238000003062 neural network model Methods 0.000 claims description 17
- 238000013528 artificial neural network Methods 0.000 claims description 15
- 230000005540 biological transmission Effects 0.000 claims description 15
- 238000012360 testing method Methods 0.000 claims description 11
- 229910001220 stainless steel Inorganic materials 0.000 claims description 10
- 239000010935 stainless steel Substances 0.000 claims description 10
- 238000009826 distribution Methods 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 4
- 239000004831 Hot glue Substances 0.000 claims description 3
- 238000009529 body temperature measurement Methods 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 2
- 230000002787 reinforcement Effects 0.000 claims description 2
- 239000011810 insulating material Substances 0.000 claims 3
- 238000004321 preservation Methods 0.000 claims 1
- 238000011160 research Methods 0.000 claims 1
- 238000010276 construction Methods 0.000 description 7
- 238000005096 rolling process Methods 0.000 description 7
- 238000009430 construction management Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000005259 measurement Methods 0.000 description 4
- 238000013461 design Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000009413 insulation Methods 0.000 description 2
- 230000035882 stress Effects 0.000 description 2
- 230000032683 aging Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005056 compaction Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000007599 discharging Methods 0.000 description 1
- 230000008595 infiltration Effects 0.000 description 1
- 238000001764 infiltration Methods 0.000 description 1
- 230000005389 magnetism Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 239000011541 reaction mixture Substances 0.000 description 1
- 230000003014 reinforcing effect Effects 0.000 description 1
- 239000003190 viscoelastic substance Substances 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
- G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention relates to a temperature monitoring method and a system in an asphalt transportation process, wherein the method comprises the steps of collecting multiple groups of data, wherein each group of data comprises four parameters of wind speed, air temperature, the outward radiation temperature of an asphalt mixture and the actual temperature of the asphalt mixture at each temperature measuring point; step two, constructing a temperature monitoring model; and step three, utilizing the temperature monitoring model obtained in the step two to monitor the temperature of the asphalt mixture in the transportation process. The system comprises intelligent processing equipment, a data acquisition instrument, a non-contact infrared temperature detector, a contact temperature probe, a wind speed sensor and a thermometer; the data acquisition instrument is communicated with the intelligent processing equipment, and the non-contact infrared temperature detector and the contact temperature probe are both connected with the data acquisition instrument; and the wind speed sensor and the thermometer transmit the acquired data to the intelligent processing equipment. The method realizes non-contact intelligent monitoring in the transportation process, and solves the problem that the temperature is difficult to monitor in real time in the material transportation process of the asphalt mixture.
Description
Technical Field
The invention relates to the technical field of road construction, in particular to a temperature monitoring method and system in an asphalt conveying process.
Background
Because the asphalt is a viscoelastic material, various parameters and properties of the asphalt are greatly influenced by temperature, and particularly the temperature of the asphalt mixture used for paving, rolling and forming can directly influence the paving and compacting quality of the asphalt pavement. When the temperature is higher, higher compactness and better compaction effect can be obtained by using fewer rolling times, but transverse cracks or uneven pavement pushing can be generated due to overhigh rolling temperature, and a pit is easy to form due to the driving of a vehicle in the use process; the rolling temperature is too low, the fluidity of the asphalt mixture is too poor, the pavement is difficult to compact, large gaps are easy to form to cause water infiltration, the cohesive force is reduced to enable the asphalt to be peeled off from the surface, the asphalt is easy to contact with the atmosphere, the aging is accelerated, and the service life of the pavement is influenced.
In the existing pavement paving construction, the method for controlling the temperature of asphalt materials is to enable the temperature of the asphalt during discharging to be higher than the actual use temperature by about 20 ℃ when the asphalt is mixed in a mixing station so as to overcome the heat loss in the material conveying process. However, in the existing asphalt transportation process, no means is used for monitoring the asphalt temperature, and the conditions that the temperature of asphalt during paving and preliminary rolling does not meet the use requirement can occur.
One of the traditional methods for measuring the asphalt temperature is to use a contact sensor such as a probe and the like to directly and manually measure, the measuring mode is usually to randomly select some points and carry out spot check, only the temperature at a certain point can be obtained, the asphalt temperature at a deeper position can not be measured, the attenuation condition of the asphalt temperature can not be obtained, and the reflected asphalt temperature distribution information is limited. The other type is that the contact type sensor is installed at a fixed position in a car hopper and measures temperature data in real time, but actually, the stress of a material transporting vehicle for loading and unloading asphalt mixture can reach three tons, the contact type sensor cannot bear the stress, and the contact type sensor is easily damaged or position deviation occurs, so that inaccurate measurement is caused.
In conclusion, the temperature monitoring method for the asphalt conveying process can realize the non-contact real-time monitoring of the asphalt mixture.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for monitoring the temperature in the asphalt conveying process.
The technical scheme for solving the technical problem is as follows:
a temperature monitoring method in an asphalt conveying process is characterized by comprising the following steps:
acquiring multiple groups of data, wherein each group of data comprises four parameters of wind speed, air temperature, external radiation temperature of the asphalt mixture and actual temperature of the asphalt mixture at each temperature measurement point;
step two, constructing a temperature monitoring model;
(1) and (3) data standardization treatment: standardizing the data acquired in the first step, and dividing the standardized data into a training set and a test set by taking a group as a unit;
(2) designing a BP neural network model: the number of BP neural network input layer nodes is four, and the nodes are respectively the outward radiation temperature, the air speed and the presence or absence of heat insulation materials of the asphalt mixture; the number of output layer nodes of the BP neural network is one, namely the actual temperature of the asphalt mixture; the BP neural network comprises an implicit layer;
(3) training a BP neural network model;
(4) testing the BP neural network model;
and step three, utilizing the temperature monitoring model obtained in the step two to monitor the temperature of the asphalt mixture in the transportation process.
The specific process of the step one is as follows:
collecting the temperature of the external radiation of the asphalt mixture by using a non-contact infrared temperature detector, collecting the wind speed by using a wind speed sensor, and collecting the temperature by using a thermometer; dividing a material transporting car hopper into a plurality of areas, wherein each area is provided with at least one contact type temperature probe for acquiring temperature; vertically inserting a contact temperature probe into one-half depth of the asphalt mixture, and acquiring temperatures at different depths, namely actual temperature of the asphalt mixture by using a plurality of temperature measuring points on the contact temperature probe; the data acquisition instrument synchronously records and stores the external radiation temperature of the asphalt mixture and the actual temperature of the asphalt mixture in real time at a fixed sampling frequency.
A temperature monitoring model is established at each temperature measuring point, and during actual application, the temperature monitoring is carried out on each temperature measuring point by calling the temperature monitoring models at all the temperature measuring points to obtain the integral temperature distribution condition, so that the integral monitoring of the asphalt mixture in the hopper is realized; or the key monitoring of a certain area is realized by calling temperature monitoring models at all temperature measuring points in the area; or the key monitoring of a certain temperature measuring point is realized by calling a temperature monitoring model at the temperature measuring point.
And step two, inputting thermal insulation materials in the form of 0/1, inputting 1 if the thermal insulation materials exist, and inputting 0 if the thermal insulation materials do not exist.
The invention also provides a temperature monitoring system in the asphalt conveying process, which is characterized by comprising intelligent processing equipment, a data acquisition instrument, a non-contact infrared temperature detector, a contact temperature probe, a wind speed sensor and a thermometer;
the data acquisition instrument is communicated with the intelligent processing equipment, and the non-contact infrared temperature detector and the contact temperature probe are both connected with the data acquisition instrument; a non-contact infrared temperature detector collects the temperature of the external radiation of the asphalt mixture; the contact temperature probe is inserted into the asphalt mixture and used for acquiring the actual temperature of the asphalt mixture, and the data acquisition instrument transmits the acquired data to the intelligent processing equipment; the wind speed sensor and the thermometer are used for collecting wind speed, weather and air temperature and transmitting collected data to the intelligent processing equipment.
The contact type temperature probe comprises a stainless steel pipe and a data transmission line, a plurality of digital temperature sensor chips are arranged in the data transmission line at equal intervals from top to bottom, heat insulation materials are filled between the data transmission line and the stainless steel pipe, the plurality of digital temperature sensor chips are all arranged in the data transmission line and used for measuring the temperature of the asphalt mixture, the position of each digital temperature sensor chip is a temperature measuring point, and therefore a plurality of temperature measuring points are distributed on the contact type temperature probe.
The probe of contact temperature probe adopts the tip design, and the probe afterbody is provided with high temperature resistant and holds the handle, holds the handle and passes through the bolt fastening with stainless steel pipe upper end, consolidates simultaneously and handles, and the hot melt adhesive is sealed fixed.
The intelligent processing equipment is a cloud server or an upper computer and is in communication with the data acquisition instrument, the wind speed sensor and the thermometer through the wireless transmission module.
The wind speed sensor is a three-cup type wind speed sensor, and the thermometer is an electronic thermometer.
The system also includes a receiving device; the receiving device is a tablet computer, a mobile phone or a vehicle-mounted terminal.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the method, the relation between the temperature of the external radiation of the asphalt mixture and the actual temperature is established through the temperature monitoring model, the non-contact infrared temperature detector collects the temperature of the external radiation of the asphalt mixture during actual application, the actual temperature of the asphalt mixture at the temperature measuring point is obtained after passing through the temperature monitoring model, non-contact intelligent monitoring during transportation is achieved, the problem that the temperature is not easy to measure during transportation of the asphalt mixture is solved, and the blank of temperature monitoring during transportation of the asphalt mixture is filled.
2. The temperature monitoring models are constructed at all temperature measuring points, and in practical application, the temperature monitoring models at all the temperature measuring points can be called to monitor the temperature of each temperature measuring point, so that the integral monitoring of the asphalt mixture in the hopper is realized, and the integral temperature distribution condition is obtained; or calling temperature monitoring models at all temperature measuring points in a certain area to realize key monitoring of the area; or calling a temperature monitoring model at a certain temperature measuring point to realize key monitoring at the certain point.
3. The temperature attenuation condition of the asphalt mixture is obtained according to the temperature parameters acquired by the method, so that bases are provided for scheduling of a material transport vehicle, researching of an insulation scheme in the transportation process of the asphalt mixture, quality tracing and the like, manpower and material resources are saved, and the construction cost is reduced.
4. The temperature monitoring system in the asphalt conveying process can be connected to the existing construction management system, and the construction management system generally comprises an asphalt production quality monitoring module, an asphalt transportation monitoring module, a paving and rolling intelligent monitoring module and the like, so that the intelligent construction management system in the whole production process can be formed.
5. The non-contact infrared temperature detector is arranged on the hopper through strong magnetism and does not need to be in direct contact with the asphalt mixture, so that the damage probability of the non-contact infrared temperature detector can be effectively reduced, and the service life of the non-contact infrared temperature detector is prolonged.
6. The contact type temperature probe is made of stainless steel pipes, is high in rigidity and not prone to deformation, can be made into a large-size probe, is suitable for being used in the transportation process of asphalt mixtures, can be smoothly inserted into asphalt without deformation, and is convenient to pull out and use after the transportation is finished due to the reinforcing design of the handle. Each probe comprises a plurality of temperature measuring points in the height direction, and can simultaneously measure the temperature of the same position at different depths.
Drawings
FIG. 1 is an overall flow chart of the method of the present invention;
FIG. 2 is a diagram illustrating a measurement state of a contact temperature probe according to an embodiment of the present invention;
FIG. 3 is a schematic view of the hopper of the material transporting vehicle divided into regions according to the embodiment of the present invention;
FIG. 4 is a control block diagram of the system of the present invention;
in the figure, 1, an intelligent processing device; 2. a data acquisition instrument; 3. a non-contact infrared temperature detector; 4. a contact temperature probe; 5. a wind speed sensor; 6. a thermometer; 7. a receiving device; 8. a material conveying vehicle.
Detailed Description
Specific examples of the present invention are given below, and the specific examples are only used to further illustrate the technical solutions of the present invention in detail, and do not limit the scope of protection of the present application.
The invention relates to a temperature monitoring method (a method for short, see figures 1-3) in an asphalt conveying process, which comprises the following steps:
acquiring multiple groups of data, wherein each group of data comprises four parameters of wind speed, air temperature, external radiation temperature of the asphalt mixture and actual temperature of the asphalt mixture at each temperature measurement point;
step two, constructing a temperature monitoring model;
(1) and (3) data standardization treatment: standardizing the data acquired in the first step, and dividing the standardized data into a training set and a test set by taking a group as a unit;
(2) designing a BP neural network model: the number of BP neural network input layer nodes is four, and the nodes are respectively the outward radiation temperature, the air speed and the presence or absence of heat insulation materials of the asphalt mixture; the number of output layer nodes of the BP neural network is one, namely the actual temperature of the asphalt mixture; the BP neural network comprises an implicit layer;
(3) training a BP neural network model;
(4) testing the BP neural network model;
and step three, utilizing the temperature monitoring model obtained in the step two to monitor the temperature of the asphalt mixture in the transportation process.
The specific process of the step one is as follows:
collecting the temperature of the external radiation of the asphalt mixture by using a non-contact infrared temperature detector, collecting the wind speed by using a wind speed sensor, and collecting the temperature by using a thermometer; dividing a material transporting car hopper into a plurality of areas, wherein each area is provided with at least one contact type temperature probe for acquiring temperature; vertically inserting a contact temperature probe into one half depth of the asphalt mixture, and collecting temperatures at different depths of the asphalt mixture, namely actual temperature of the asphalt mixture, by using a plurality of temperature measuring points on the contact temperature probe;
the data acquisition instrument synchronously records and stores the external radiation temperature of the asphalt mixture and the actual temperature of the asphalt mixture in real time at a fixed sampling frequency.
A temperature monitoring model is built at each temperature measuring point, and during actual application, the temperature monitoring is carried out on each temperature measuring point by calling the temperature monitoring models at all the temperature measuring points to obtain the overall temperature distribution condition, so that the overall monitoring of the asphalt mixture in the hopper is realized; or the key monitoring of a certain area is realized by calling temperature monitoring models at all temperature measuring points in the area; or the key monitoring of a certain temperature measuring point is realized by calling a temperature monitoring model at the temperature measuring point.
And step two, inputting thermal insulation materials in the form of 0/1, inputting 1 if the thermal insulation materials exist, and inputting 0 if the thermal insulation materials do not exist.
The temperature monitoring system (for short, see fig. 4) for the asphalt transportation process comprises intelligent processing equipment 1, a data acquisition instrument 2, a non-contact infrared temperature detector 3, a contact temperature probe 4, a wind speed sensor 5, a thermometer 6 and receiving equipment 7;
the data acquisition instrument 2 is placed on the material conveying vehicle 8 and is communicated with the intelligent processing equipment 1, and the non-contact infrared temperature detector 3 and the contact temperature probe 4 are both connected with the data acquisition instrument 2; the non-contact infrared temperature detector 3 is adsorbed on a hopper of the material transporting vehicle 8 through a strong magnet, and a detection end is positioned in an area of the temperature radiation of the asphalt mixture and is used for collecting the temperature of the outward radiation of the asphalt mixture; the contact temperature probe 4 is doped into the asphalt mixture and used for acquiring the actual temperature of the asphalt mixture, and the data acquisition instrument 2 transmits the acquired data to the intelligent processing equipment 1;
the wind speed sensor 5 and the thermometer 6 are both installed on an asphalt mixing station or a construction site and used for collecting wind speed and weather temperature, the collection frequency of the wind speed sensor 5 and the collection frequency of the thermometer 6 are both 0.5 h/time, and measurement data are transmitted to the intelligent processing equipment 1 in real time.
The intelligent processing equipment 1 is a cloud server or an upper computer, and is communicated with the data acquisition instrument, the wind speed sensor and the thermometer through a 4G transmission module.
The contact type temperature probe 4 comprises a stainless steel pipe and a data transmission line, a plurality of digital temperature sensor chips are arranged in the data transmission line at equal intervals from top to bottom, heat insulation materials are filled between the data transmission line and the stainless steel pipe, the plurality of digital temperature sensor chips are all arranged in the data transmission line and used for measuring the temperature of the asphalt mixture, and the position of each digital temperature sensor chip is a temperature measuring point, so that a plurality of temperature measuring points are distributed on the contact type temperature probe.
The probe of the contact type temperature probe 4 adopts a pointed head design, a high-temperature-resistant hand-held handle is arranged at the tail part of the probe, the hand-held handle is fixed with the upper end of the stainless steel pipe through a bolt, and meanwhile, reinforcement treatment is carried out, and the hot melt adhesive is sealed and fixed.
Because the difference between the wind speed and the air temperature is small in the same region at the same time, the wind speed sensor 5 and the thermometer 6 are installed on an asphalt mixing station or a construction site and used for acquiring real-time wind speed data and real-time air temperature data. The wind speed sensor 5 is a three-cup type wind speed sensor, and the thermometer 6 is an electronic thermometer.
The receiving device 7 is an electronic device such as a tablet computer, a mobile phone or a vehicle-mounted terminal, and can receive the instruction transmitted by the intelligent processing device 1.
The working principle and the process of the temperature monitoring system in the asphalt conveying process are as follows:
the data collected by the non-contact infrared temperature detector 3, the contact temperature probe 4, the wind speed sensor 5 and the thermometer 6 are transmitted to the intelligent processing equipment 1 in real time, the intelligent processing equipment 1 stores and analyzes all the data to obtain the temperature of the asphalt mixture radiating outwards and the actual temperature, and transmits the analysis result to the receiving equipment 1 to inform the receiving equipment 1 of the temperature condition of the asphalt mixture so that a driver can flexibly arrange for unloading.
The temperature monitoring system in the asphalt conveying process can also be connected to the existing construction management system, and the construction management system usually comprises an asphalt production quality monitoring module, an asphalt transportation monitoring module, a paving and rolling intelligent monitoring module and the like, so that the intelligent construction management system in the whole production process can be formed.
Examples
The temperature monitoring method for the asphalt conveying process comprises the following steps:
acquiring data by using a temperature monitoring system, wherein the data comprises wind speed, air temperature, the outward radiation temperature of the asphalt mixture and the actual temperature of the asphalt mixture;
the non-contact infrared temperature detector 3 is arranged on the front side wall of the hopper, and the measuring end of the non-contact infrared temperature detector is 12cm away from the surface of the asphalt mixture and is used for collecting the outward radiation temperature of the asphalt mixture; the wind speed sensor 5 collects wind speed, and the thermometer 6 collects air temperature;
regarding the top view of the hopper of the material transporting vehicle 8 as a rectangle approximately, dividing the hopper into 4 areas (viewed from the top view of the hopper) by taking two middle lines of the rectangle as references; each zone has a contact temperature probe 4 to collect temperature; the contact temperature probe 4 is provided with a plurality of temperature measuring points at intervals from top to bottom, the contact temperature probe 4 is vertically inserted into the asphalt mixture, and the temperature of the asphalt mixture at different depths, namely the actual temperature of the asphalt mixture, is collected by using the temperature measuring points;
because the asphalt mixture is closer to the position of the side wall of the car hopper, the temperature is reduced faster, in order to ensure that the temperature of the asphalt mixture transported to a construction site meets the construction standard, the position where the contact temperature probe is inserted into the asphalt mixture is close to the side wall of the car hopper, and the distance between the position where at least one contact temperature probe is inserted and the side wall of the car hopper in the area where the contact temperature probe is located is 0.1 time of the width of the car hopper;
because the surface temperature of the asphalt mixture is decreased faster and faster, but the surface temperature is difficult to avoid, the proportion of the asphalt is extremely small, and the decrease speed is too fast and is not representative, the surface temperature of the asphalt mixture is not representative enough for direct measurement, and a measuring point in the asphalt mixture is required to measure the temperature; the heat of the asphalt mixture is radially dissipated outwards from the inside to the periphery, so that the temperature attenuation condition of the asphalt mixture is approximately symmetrical distribution by taking the upper and lower bisection planes of the asphalt mixture as a reference, and a contact temperature probe needs to be inserted into one-half depth of the asphalt mixture; four measuring points are distributed on the contact temperature probe of the embodiment, so that the uppermost measuring point of the contact temperature probe is ensured to be positioned on the surface of the asphalt mixture, and the lowermost measuring point of the contact temperature probe is ensured to be positioned at the half depth of the asphalt mixture as much as possible.
The data acquisition instrument 2 synchronously records and stores the temperature acquired by the non-contact infrared temperature detector 3 and the temperature acquired by all the contact temperature probes 4 in real time at a sampling frequency of 10 s/time; the collection frequency of the wind speed sensor 5 and the thermometer 6 is 0.5 h/time; 2400 groups of data are collected together, each group of data comprises four parameters of air temperature, air speed, the outward radiation temperature of the asphalt mixture and the actual temperature of the asphalt mixture at any one temperature measuring point A, and all the collected data are transmitted to an upper computer.
Step two, constructing a temperature monitoring model;
(1) and (3) data standardization treatment: standardizing the data acquired in the first step, and standardizing all the data to 0-1; the normalized data was expressed in groups as 4: 1, dividing a training set and a test set in proportion;
(2) designing a BP neural network model: the temperature monitoring model is constructed by using a BP neural network, the number of input layer nodes of the BP neural network is four, and the input layer nodes are respectively the outward radiation temperature, the air speed of the asphalt mixture and the presence or absence of a heat insulation material, namely the characteristic values of the BP neural network; the heat insulation material is a heat insulation film covered on the side wall of the car hopper, the heat insulation material is input in a 0/1 mode, the heat insulation material is input in a 1 mode, and the heat insulation material is input in a 0 mode; the number of output layer nodes of the BP neural network is one, namely the actual temperature of the asphalt mixture;
the BP neural network has a simple structure, so that only one hidden layer is arranged, and the number of nodes of the hidden layer is determined according to the empirical formula of the formula (1);
in the formula (1), m is the number of nodes of an implicit layer, n is the number of nodes of an input layer, l is the number of nodes of an output layer, and alpha is a constant between 1 and 10;
calculating according to the formula (1) to obtain the number of hidden layer nodes of 3-12, and preliminarily selecting 10 in the embodiment to obtain a BP neural network model;
(3) training a BP neural network model;
1) setting initial parameters: assigning random numbers to the weight matrix W, V, setting a sample pattern counter p and a training frequency counter q as 1, setting an error E as 0, and setting a learning rate eta as a random number between 0 and 1;
2) inputting the training set into the BP neural network model obtained in the step (2) for training to obtain a temperature predicted value; calculating an error E by using the formula (2);
in the formula (2), the reaction mixture is,
the mean value of the target values of all samples in the training set; a is the number of samples of the training set; y is i The real value of the ith sample; z is a radical of i Is the predicted value of the ith sample;
setting an error limit Emin1, stopping training when E is less than Emin1, indicating that the model training error meets the requirement and the training effect is better; if E is more than or equal to Emin1, training is continued until E is less than Emin 1.
(4) Testing the BP neural network model;
inputting the test set into the BP neural network model trained in the step (2) for testing, and setting an error limit value Emin2, wherein Emin1 is more than or equal to Emin2 is more than or equal to 1.1Emin 1; calculating an error E by using a formula (2), wherein if E is less than Emin2, the learning effect of the BP neural network model obtained in the step (2) is good, and the trained BP neural network model obtained in the step 2) is a temperature monitoring model; and (3) if the E is more than or equal to Emin2, indicating that the model is over-fitted, manually adjusting the number of nodes of the hidden layer in the step (2) and other super-parameters, repeating the step (2) to retrain the network model, and terminating the model training until the E is less than Emin2 to obtain the temperature monitoring model.
Increasing the number of hidden layers and the number of nodes of the hidden layers can improve the accuracy of the model, but excessive number of hidden layers can easily result in overfitting (too many characteristics are learned in the training set), that is, the model has good performance in the training set, and the test set has poor performance, so that a model with better effect can be obtained by properly increasing the number of nodes of the hidden layers under the condition of less number of hidden layers in order to prevent overfitting.
The obtained temperature monitoring model is the temperature monitoring model at the temperature measuring point A, the actual temperature of the asphalt mixture in the 2400 groups of data in the first step is replaced by the actual temperature of the asphalt mixture collected at any temperature measuring point, and the second step is repeated to construct the temperature monitoring model at each temperature measuring point;
and establishing a relationship between the temperature collected by the non-contact infrared temperature detector and the temperature collected by the contact temperature probe by using a temperature monitoring model, namely establishing a relationship between the temperature radiated outside by the asphalt mixture and the actual temperature.
Step three, storing the temperature monitoring model obtained in the step two in intelligent processing equipment for monitoring the temperature of the asphalt mixture in the transportation process; in the transportation process of the asphalt mixture, the non-contact infrared temperature detector collects the temperature of the outward radiation of the asphalt mixture and is used as the input of the temperature monitoring model, and the temperature monitoring model outputs the actual temperature of the asphalt mixture at the temperature measuring point, so that the real-time monitoring in the transportation process is realized.
In the embodiment, 16 temperature monitoring models are built in total, and four temperature monitoring models in the depth direction of the asphalt mixture are built in each area. In practical application, the temperature monitoring models at 16 temperature measuring points can be called to monitor the temperature of each temperature measuring point, so that the temperature distribution conditions at the 16 temperature measuring points are obtained, and the integral monitoring of the asphalt mixture in the hopper is realized; or calling temperature monitoring models at all temperature measuring points in a certain area to realize key monitoring of the area; or calling a temperature monitoring model at a certain temperature measuring point to realize key monitoring at the temperature measuring point.
Nothing in this specification is said to apply to the prior art.
Claims (7)
1. A temperature monitoring method in an asphalt conveying process is characterized by comprising the following steps:
acquiring multiple groups of data, wherein each group of data comprises four parameters of wind speed, air temperature, external radiation temperature of the asphalt mixture and actual temperature of the asphalt mixture at each temperature measurement point; collecting the temperature of the external radiation of the asphalt mixture by using a non-contact infrared temperature detector, collecting the wind speed by using a wind speed sensor, and collecting the temperature by using a thermometer; dividing a material transporting car hopper into a plurality of areas, wherein each area is provided with at least one contact type temperature probe for acquiring temperature; vertically inserting a contact temperature probe into one-half depth of the asphalt mixture, and acquiring temperatures at different depths, namely actual temperature of the asphalt mixture by using a plurality of temperature measuring points on the contact temperature probe; the data acquisition instrument synchronously records and stores the external radiation temperature of the asphalt mixture and the actual temperature of the asphalt mixture in real time at a fixed sampling frequency;
step two, constructing a temperature monitoring model;
(1) and (3) data standardization treatment: standardizing the data acquired in the first step, and dividing the standardized data into a training set and a test set by taking a group as a unit;
(2) designing a BP neural network model: the number of the BP neural network input layer nodes is four, and the BP neural network input layer nodes respectively represent the outward radiation temperature, the air speed and the presence or absence of a heat insulation material of the asphalt mixture; the number of output layer nodes of the BP neural network is one, namely the actual temperature of the asphalt mixture; the BP neural network comprises an implicit layer;
(3) training a BP neural network model;
(4) testing the BP neural network model;
thirdly, monitoring the temperature of the asphalt mixture in the transportation process by using the temperature monitoring model obtained in the second step;
a plurality of temperature measuring points are distributed in the hopper of the material transporting vehicle, a temperature monitoring model is built at each temperature measuring point, and the temperature monitoring models at all the temperature measuring points are called during practical application to monitor the temperature of each temperature measuring point to obtain the overall temperature distribution condition, so that the overall monitoring of the asphalt mixture in the hopper is realized, and a basis is provided for scheduling of the material transporting vehicle, research on the heat preservation scheme of the transportation process of the asphalt mixture and quality tracing; or the key monitoring of a certain area is realized by calling temperature monitoring models at all temperature measuring points in the area; or the key monitoring of a certain temperature measuring point is realized by calling a temperature monitoring model at the temperature measuring point.
2. The method for monitoring the temperature in the asphalt transportation process according to claim 1, wherein in the second step, the heat insulating material is input in the form of 0/1, 1 is input if the heat insulating material is present, and 0 is input if the heat insulating material is absent.
3. A temperature monitoring system in an asphalt conveying process is characterized by comprising intelligent processing equipment, a data acquisition instrument, a non-contact infrared temperature detector, a contact temperature probe, a wind speed sensor and a thermometer;
the data acquisition instrument is communicated with the intelligent processing equipment, and the non-contact infrared temperature detector and the contact temperature probe are both connected with the data acquisition instrument; a non-contact infrared temperature detector collects the temperature of the outward radiation of the asphalt mixture; the contact temperature probe is doped into the asphalt mixture and used for acquiring the actual temperature of the asphalt mixture, and the data acquisition instrument transmits the acquired data to the intelligent processing equipment; the wind speed sensor and the thermometer are used for acquiring wind speed, weather and temperature and transmitting the acquired data to the intelligent processing equipment;
the contact type temperature probe comprises a stainless steel pipe and a data transmission line, a plurality of digital temperature sensor chips are arranged in the data transmission line at equal intervals from top to bottom, heat insulation materials are filled between the data transmission line and the stainless steel pipe, the plurality of digital temperature sensor chips are all arranged in the data transmission line and used for measuring the temperature of the asphalt mixture, the position of each digital temperature sensor chip is a temperature measuring point, and therefore a plurality of temperature measuring points are distributed on the contact type temperature probe.
4. The asphalt conveying process temperature monitoring system according to claim 3, wherein a probe of the contact temperature probe is designed to be a pointed head, a high-temperature-resistant hand-held handle is arranged at the tail of the probe, the hand-held handle is fixed with the upper end of the stainless steel pipe through a bolt, reinforcement treatment is carried out simultaneously, and the hot melt adhesive is sealed and fixed.
5. The asphalt transportation process temperature monitoring system according to claim 3, wherein the intelligent processing device is a cloud server or an upper computer, and the intelligent processing device is in communication with the data acquisition instrument, the wind speed sensor and the thermometer through a wireless transmission module.
6. The asphalt transportation process temperature monitoring system according to claim 3, wherein the wind speed sensor is a three-cup wind speed sensor and the thermometer is an electronic thermometer.
7. An asphalt transportation process temperature monitoring system according to any one of claims 3 to 6, further comprising a receiving device; the receiving device is a tablet computer, a mobile phone or a vehicle-mounted terminal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110137412.XA CN112985608B (en) | 2021-02-01 | 2021-02-01 | Method and system for monitoring temperature in asphalt conveying process |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110137412.XA CN112985608B (en) | 2021-02-01 | 2021-02-01 | Method and system for monitoring temperature in asphalt conveying process |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112985608A CN112985608A (en) | 2021-06-18 |
| CN112985608B true CN112985608B (en) | 2022-08-02 |
Family
ID=76346142
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110137412.XA Active CN112985608B (en) | 2021-02-01 | 2021-02-01 | Method and system for monitoring temperature in asphalt conveying process |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112985608B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112985608B (en) * | 2021-02-01 | 2022-08-02 | 河北工业大学 | Method and system for monitoring temperature in asphalt conveying process |
| CN113340438B (en) * | 2021-07-08 | 2022-05-03 | 电子科技大学 | Method for calibrating distance error of hot end component of aero-engine in non-contact temperature field |
| CN113624343A (en) * | 2021-08-10 | 2021-11-09 | 哈尔滨工业大学 | Non-contact type high-precision continuous detection device for construction temperature of asphalt pavement |
| CN114675691B (en) * | 2022-04-13 | 2023-02-03 | 中铁三局集团第五工程有限公司 | Construction process temperature control method and device for asphalt mixture and storable medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103336499A (en) * | 2013-06-19 | 2013-10-02 | 中山市拓维电子科技有限公司 | System and method of dynamic management for construction quality of asphalt concrete pavement |
| CN104165699A (en) * | 2014-08-28 | 2014-11-26 | 哈尔滨工业大学 | Bituminous concrete construction temperature detection method of bituminous pavement double-layer paving technology |
| CN105675145A (en) * | 2014-11-18 | 2016-06-15 | 西安志越机电科技有限公司 | Non-contact temperature monitoring system for asphalt mixing bunker |
| CN108595833A (en) * | 2018-04-23 | 2018-09-28 | 长安大学 | Life prediction method is avenged in storage salt asphalt ice-melt based on BP neural network |
| CN108871586A (en) * | 2018-07-26 | 2018-11-23 | 成都信息工程大学 | A kind of inversion method of ground infrared remote sensing surface temperature |
| CN108896193A (en) * | 2018-07-12 | 2018-11-27 | 黑龙江科技大学 | A kind of the temperature monitoring early warning system and method for high-voltage switch cabinet of transformer substation |
| CN109284863A (en) * | 2018-09-04 | 2019-01-29 | 南京理工大学 | A kind of power equipment temperature predicting method based on deep neural network |
| CN110531797A (en) * | 2019-05-31 | 2019-12-03 | 华电国际电力股份有限公司技术服务分公司 | Extra-supercritical unit high temperature superheater wall temperature prediction technique neural network based |
| CN110656554A (en) * | 2018-06-29 | 2020-01-07 | 卡特彼勒路面机械公司 | System and method for controlling paving machine speed |
| CN210014841U (en) * | 2019-05-28 | 2020-02-04 | 广东逸华交通工程检测有限公司 | Device for monitoring temperature of asphalt concrete construction site based on unmanned aerial vehicle |
| CN111105079A (en) * | 2019-11-29 | 2020-05-05 | 江苏信息职业技术学院 | Internet of things water body dissolved oxygen prediction method and water quality monitoring system |
| CN112985608A (en) * | 2021-02-01 | 2021-06-18 | 河北工业大学 | Method and system for monitoring temperature in asphalt conveying process |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112252292B (en) * | 2020-10-15 | 2021-05-28 | 河北工业大学 | Real-time highway compactness monitoring method based on artificial neural network |
-
2021
- 2021-02-01 CN CN202110137412.XA patent/CN112985608B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103336499A (en) * | 2013-06-19 | 2013-10-02 | 中山市拓维电子科技有限公司 | System and method of dynamic management for construction quality of asphalt concrete pavement |
| CN104165699A (en) * | 2014-08-28 | 2014-11-26 | 哈尔滨工业大学 | Bituminous concrete construction temperature detection method of bituminous pavement double-layer paving technology |
| CN105675145A (en) * | 2014-11-18 | 2016-06-15 | 西安志越机电科技有限公司 | Non-contact temperature monitoring system for asphalt mixing bunker |
| CN108595833A (en) * | 2018-04-23 | 2018-09-28 | 长安大学 | Life prediction method is avenged in storage salt asphalt ice-melt based on BP neural network |
| CN110656554A (en) * | 2018-06-29 | 2020-01-07 | 卡特彼勒路面机械公司 | System and method for controlling paving machine speed |
| CN108896193A (en) * | 2018-07-12 | 2018-11-27 | 黑龙江科技大学 | A kind of the temperature monitoring early warning system and method for high-voltage switch cabinet of transformer substation |
| CN108871586A (en) * | 2018-07-26 | 2018-11-23 | 成都信息工程大学 | A kind of inversion method of ground infrared remote sensing surface temperature |
| CN109284863A (en) * | 2018-09-04 | 2019-01-29 | 南京理工大学 | A kind of power equipment temperature predicting method based on deep neural network |
| CN210014841U (en) * | 2019-05-28 | 2020-02-04 | 广东逸华交通工程检测有限公司 | Device for monitoring temperature of asphalt concrete construction site based on unmanned aerial vehicle |
| CN110531797A (en) * | 2019-05-31 | 2019-12-03 | 华电国际电力股份有限公司技术服务分公司 | Extra-supercritical unit high temperature superheater wall temperature prediction technique neural network based |
| CN111105079A (en) * | 2019-11-29 | 2020-05-05 | 江苏信息职业技术学院 | Internet of things water body dissolved oxygen prediction method and water quality monitoring system |
| CN112985608A (en) * | 2021-02-01 | 2021-06-18 | 河北工业大学 | Method and system for monitoring temperature in asphalt conveying process |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112985608A (en) | 2021-06-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112985608B (en) | Method and system for monitoring temperature in asphalt conveying process | |
| CN201740777U (en) | Rapid measurement system for measuring slope soil erosion | |
| CN101793545B (en) | Automatic metering system and method for biofuel | |
| CN201378152Y (en) | Concrete heat-insulation temperature-rise measuring device | |
| CN108803312B (en) | Novel road damage detecting system | |
| CN112965077B (en) | Road inspection system and method based on vehicle-mounted laser radar | |
| CN102680812A (en) | Method and device for evaluating life of product | |
| CA2798525A1 (en) | Load-measuring, fleet asset tracking and data management system for load-lifting vehicles | |
| CN111532313B (en) | Remote automatic train dispatching and commanding system and method for loading station | |
| CN104330038B (en) | Size measurement method | |
| CN208815383U (en) | A kind of high road pavement construction Quality Remote Monitoring System | |
| CN103969415A (en) | Movable type water pollution data acquisition system and movable type water pollution data acquisition method | |
| CN108646681B (en) | Intelligent management and system for asphalt concrete construction quality | |
| CN112575656A (en) | Multifunctional sensor for detecting compactness of asphalt pavement and arrangement method | |
| CN205486358U (en) | Monitoring system of crowd's density and flow direction | |
| CN105389983A (en) | Road traffic information collection method and system based on vehicular unit and roadside units | |
| CN107806118A (en) | A kind of broken stone pile machine digitizes construction system | |
| CN115375202A (en) | Railway tunnel engineering safety detection method and system | |
| CN113821861A (en) | Roadbed and pavement compaction quality measurement method and system based on multiple regression | |
| CN114095801A (en) | Automatic inspection system for 3D material state of coal bunker based on 5G technology | |
| CN108303138A (en) | A kind of road and bridge concrete structure monitoring device | |
| CN207424889U (en) | Build data acquisition equipment | |
| CN107219531A (en) | A kind of pilot system for measurement sample mass and size during Frozen-thawed cycled | |
| CN109707452B (en) | Remote operation system for intelligently judging surrounding rock level of tunnel face | |
| CN116553119A (en) | Coal bunker monitoring system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |