CN117131314A - Mixed material temperature monitoring and regulating system for asphalt pavement construction - Google Patents
Mixed material temperature monitoring and regulating system for asphalt pavement construction Download PDFInfo
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Abstract
The invention relates to the technical field of data processing, in particular to a temperature monitoring and regulating system for a mixture for asphalt pavement construction. The system comprises: the system comprises a data acquisition module, a data analysis module and a data monitoring module, wherein the data acquisition module is used for acquiring mixture temperature data and various related data influencing the mixture temperature, obtaining the correlation coefficient of the various related data and the mixture temperature data according to the change degree of the various related data, obtaining the influence degree coefficient of all the related data on the mixture temperature according to the correlation coefficient, obtaining the mixture temperature correction value at each moment by combining the influence degree coefficient of the mixture temperature and the mixture temperature at each moment, detecting the mixture temperature correction value by using an anomaly detection algorithm, obtaining the anomaly factor fraction of the mixture temperature at each moment, and completing the monitoring and regulation of the mixture temperature. Therefore, the accurate monitoring and regulation of the temperature of the mixture are realized, and reliable information is provided for the regulation of the temperature of the mixture.
Description
Technical Field
The invention relates to the field of data processing, in particular to a temperature monitoring and regulating system for a mixture for asphalt pavement construction.
Background
In the process of manufacturing the asphalt pavement construction mixture, other raw materials such as cold materials, water and the like are required to be continuously added, and in the process of stirring, various problems are inevitably caused due to the influence of various factors such as seasons, vibration, high temperature, human factors and the like, and the temperature of the mixture is also changed. The temperature of the asphalt mixture is strictly controlled, the temperature is more required to be controlled within a certain range during the stirring process, and if the temperature exceeds the normal temperature range, the mixture can not be used any more.
The traditional temperature monitoring and regulating method for the mixture for asphalt pavement construction analyzes various data with larger temperature influence, and completes temperature monitoring according to the data change condition of each influence data. However, the method cannot accurately distinguish the influence of the change degree of each related data on the temperature of the mixture, and meanwhile, the method does not deeply combine the correlation of each related data on the temperature data of the mixture, so that the monitoring result is inaccurate, and errors occur.
In summary, the invention provides a mixture temperature monitoring and controlling system for asphalt pavement construction, which is used for analyzing each historical related data to construct the self-adaptive data quantity in a group for each related data, so that the change characteristics of each related data can be effectively used for influencing the mixture temperature data, and the accuracy of the mixture temperature monitoring and controlling is improved.
Disclosure of Invention
In order to solve the technical problems, the invention aims to provide a temperature monitoring and regulating system for a mixture for asphalt pavement construction, which adopts the following technical scheme:
the invention provides a mixture temperature monitoring and regulating system for asphalt pavement construction, which comprises the following components:
the data acquisition module is used for acquiring temperature data of the mixture and various related data;
the data analysis module is used for grouping various related data and obtaining fluctuation coefficients of the data of each group according to the distribution of the data in the group; obtaining the variation degree of each group of data according to the fluctuation coefficient and the discrete degree of each group of data; obtaining intra-group data correction coefficients of each group of data according to the change degree and the difference of each group of data; combining the intra-group data correction coefficients and the number of the intra-group data to obtain the self-adaptive number of the intra-group data of each group of data; the data are regrouped according to the data quantity in the self-adaptive group; calculating correlation coefficients of various kinds of correlation data and mixture temperature data after regrouping; obtaining the influence degree coefficient of the mixture temperature data at each moment according to the correlation coefficient of the regrouped various related data and the mixture temperature data; obtaining corrected mixture temperature values at all times according to the influence degree coefficient of the mixture temperature data at all times and the mixture temperature at the corresponding time;
the data monitoring module is used for carrying out anomaly detection on the corrected mixture temperature value at each moment to obtain an anomaly factor fraction; and (3) monitoring and controlling the temperature of the mixture for asphalt pavement construction according to the abnormal factor fractions at each moment.
Further, the obtaining the fluctuation coefficient of each group of data according to the distribution of the data in the group includes:
and calculating the difference value between each adjacent data according to the data in each group, and summing and averaging the difference values to obtain the fluctuation coefficient of each group of data.
Further, the obtaining the variation degree of each group of data according to the fluctuation coefficient and the discrete degree of each group of data includes:
and calculating the difference value between the maximum value and the minimum value of the data in each group, and taking the product of the difference value and the fluctuation coefficient of each group of data as the change degree of each group of data.
Further, the intra-group data correction coefficient of each group of data is obtained according to the change degree and the difference of each group of data, and the expression is:
in the method, in the process of the invention,intra-group data correction factor representing cold flow data,/->For the number of groups after the cold charge flow data packet, < >>As a linear normalization function>、/>The degree of change of the data in the i-th and j-th groups is shown.
Further, the obtaining the adaptive intra-group data quantity of each group of data by combining the intra-group data correction coefficient and the intra-group data quantity includes:
and rounding the product of the number of the data in each group and the correction coefficient in each group to obtain the self-adaptive number of the data in each group.
Further, the calculation of the correlation coefficient between the regrouped various related data and the mixture temperature data has the expression:
in the method, in the process of the invention,is the correlation coefficient between the cold charge flow data and the mixture temperature data, +.>The number of groups regrouped for the cold flow data,/-for the group>As a pearson correlation coefficient function, +.>For the data sequence of the i-th group in the cold material flow data,>is the data sequence of the i-th group in the temperature data of the mixture.
Further, the influence degree coefficient of the mixture temperature data at each moment is obtained according to the correlation coefficient of the regrouped various related data and the mixture temperature data, and the expression is as follows:
in the method, in the process of the invention,the influence degree coefficient of various related data on the temperature data of the mixture at the time t is shown,as a linear normalization function>、/>、/>The values of the cold material flow data, cold material temperature data and raw material water content data at the time t are respectively represented by +.>、/>、/>Respectively represent cold material flow data and coldCorrelation coefficients between the material temperature data and the raw material water content data and the mixture temperature data respectively.
Further, the obtaining the corrected mixture temperature value at each time according to the influence degree coefficient of the mixture temperature data at each time and the mixture temperature at the corresponding time includes:
and taking the product of the mixture temperature data and the influence degree coefficient as a corrected mixture temperature value at the corresponding moment aiming at the mixture temperature data at each moment.
Further, the abnormality detection of the corrected mixture temperature value at each time to obtain an abnormality factor score includes:
and detecting the corrected mixture temperature value at each moment by using an anomaly detection algorithm, and marking the score obtained by detection as an anomaly factor score.
Further, the method for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to the abnormal factor fractions at each moment comprises the following steps:
and taking the mixture temperature value with the abnormal factor fraction larger than the threshold value as an abnormal mixture temperature value, and regulating and controlling the abnormal mixture temperature value to be within a normal temperature range.
The invention has the following beneficial effects:
according to the invention, through analyzing each historical related data, the self-adaptive data quantity in the group is constructed for each related data, and the influence on the temperature data of the mixture can be effectively performed according to the change characteristics of each related data, so that the influence effect of each related data on the mixture is more valuable to reference;
meanwhile, on the basis of the data quantity in the self-adaptive group of each related data, the data points at each moment in the mixture temperature data are corrected by combining the correlation coefficient between each related data and the mixture temperature data, so that the relation between each related data and the mixture temperature data can be well reflected on the data points at each moment, and the accuracy of an algorithm is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a system for monitoring and controlling temperature of a mixture for asphalt pavement construction according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following is a detailed description of specific implementation, structure, characteristics and effects of the mixture temperature monitoring and controlling system for asphalt pavement construction according to the invention in combination with the accompanying drawings and the preferred embodiment. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a concrete scheme of a temperature monitoring and controlling system for a mixture for asphalt pavement construction, which is specifically described below with reference to the accompanying drawings.
Referring to fig. 1, a block diagram of a system for monitoring and controlling temperature of a mixture for asphalt pavement construction according to an embodiment of the present invention is shown, where the system includes: a data acquisition module 101, a data analysis module 102 and a data monitoring module 103.
The data acquisition module 101 acquires a temperature data sequence of the mixture for asphalt pavement construction and related data thereof, and acquires historical data.
First, regarding the temperature of the mixture for asphalt pavement construction at the time of stirring, there are abnormal conditions that may occur during the process of producing the mixture: the slower the flow rate of the added cold material is, the slower the temperature of the mixture is reduced, and the cold material can fully react with the mixture due to the slower feeding speed of the cold material, so that the temperature of the mixture is relatively higher easily; the temperature of the cold material also determines the change of the temperature of the mixture, namely, the higher the temperature of the cold material is, the heat of the cold material is transferred to the mixture due to the heat conduction effect after the cold material contacts the mixture, so that the temperature of the mixture is increased; the moisture content of the raw materials also has an effect on the temperature change of the mix, and when the mix is added, the moisture therein absorbs heat and evaporates, resulting in a temperature drop of the mix.
Therefore, it is necessary to monitor the flow rate, temperature, water content of raw materials and temperature of the mixture during the stirring process of the mixture, in this embodiment, the flow meter, the temperature sensor and the water content sensor are utilized to monitor the temperature data of the mixture in real time, and convert the data into electrical signals to be output to the control system, so as to obtain real-time flow rate data, temperature data, water content data of raw materials and temperature data of the mixture, and record the flow rate data of the cold material as first related data, the temperature data of the cold material as second related data, the water content data of the raw materials as third related data, and the first related data, the second related data and the third related data are collectively referred to as related data.
Because the data may not be continuous and there are some missing values, the present embodiment uses a mean filling method to fill the missing values, which is a known technique, and this embodiment will not be repeated.
In order to calculate the influence degree of each related data on the temperature change of the mixture, the historical data of each related data needs to be analyzed, so that the historical data of the cold material flow, the cold material temperature, the raw material water content and the mixture temperature need to be acquired to obtain a corresponding related data sequence.
The data analysis module 102 analyzes the historical data of each relevant data, and obtains the mixture temperature data affected by the relevant data by combining the correlation coefficient between each relevant data and the mixture temperature data.
The monitoring and controlling of the mixture temperature is a process of obtaining an abnormal mixture temperature value by analyzing various factors influencing the mixture temperature and adjusting the abnormal temperature value. According to the embodiment, the influence degree coefficient of the mixture temperature data is obtained according to the self-distribution characteristics of various related data and the correlation coefficient of the mixture temperature data, and the corrected mixture temperature value at each moment is obtained by combining the mixture temperature data and the influence degree coefficient, so that the monitoring and the regulation of the mixture temperature are completed. The construction process for correcting the temperature value of the mixture comprises the following steps:
firstly, as any kind of related data change in the process of stirring the mixture can have a certain influence on the temperature of the mixture, the positive correlation relationship that the larger the change is, the larger the influence on the temperature of the mixture is.
Based on that the degree of change is different between the related data, the same degree of change may be abnormal data in one type of related data, but may be normal data in another type of related data, so that the abnormal data points in the related data cannot be identified by the same method, and analysis is required for the degree of change of the related data.
In order to detect the change degree of the related data, each related data sequence is grouped, each group takes N data points, and the related data sequences share the data points at N moments, so that N/N groups are obtained, and the k represents k.
For analysis of one type of related data sequence, the embodiment takes cold material flow data as an example. For the kth group of data in the cold material flow data, acquiring the maximum data point and the minimum data point of the kth group of data, and obtaining the variation degree of the kth group of data according to the maximum data point and the minimum data point, wherein the specific expression is as follows:
in the method, in the process of the invention,represents the degree of change of the kth group data, +.>For the fluctuation coefficient of the kth group of data, +.>、/>Respectively represent maximum value, minimum value, and +.>For the amount of data in the group, +.>、/>The values of the ith and (i+1) th data points in the kth set of data are shown, respectively.
It should be noted that the number of the substrates,the overall change degree condition in the group of data can be represented, and the larger the value is, the larger the change degree of the overall change degree of the group of data is represented; />Representing the calculation of the difference between each data point and adjacent data points in the set of data, wherein the larger the difference is, the more chaotic the distribution change among the data points in detail is; thus, the degree of change in the whole and detail of the group is analyzed separately, and the degree of change in the group is collectively reflected.
In order to reflect the difference of the change degree of the data in each group in the cold material flow data, and in the case of normal data based on history related data, the size of the group is indirectly reacted through the difference. If the variation degree difference of each group is larger, the data dividing range in the group of the data is smaller, and the data quantity in the group needs to be increased, so that the normal condition of the data in the group can be completely identified, and the normal condition is not used as an abnormal condition to cause misjudgment.
And obtaining an intra-group data correction coefficient of the cold material flow data by comparing the variation degree difference of the data in each group, wherein the intra-group data correction coefficient has the following specific expression:
in the method, in the process of the invention,intra-group data correction factor representing cold flow data,/->For the number of groups after the cold charge flow data packet, < >>As a linear normalization function>、/>The degree of change of the data in the i-th and j-th groups is shown.
It should be noted that, by calculating the difference of the variation degree between the data of each group and calculating the average value, if the difference is larger, the data quantity of the cold material flow data packet is smaller, that is, the normal data in the cold material flow data packet cannot be completely represented, and the data quantity in the group needs to be enlarged, so that the data packet of the history normal data is more standard.
Correcting the data quantity in the group according to the data correction coefficient in the group of the cold material flow data to obtain the data quantity in the self-adaptive group, wherein the specific expression of the data quantity in the self-adaptive group is as follows:
in the method, in the process of the invention,adaptive intra-group data amount for each group, < +.>For the number of data in the original group of each group, +.>For rounding function, ++>Correction coefficient for intra-group data of cold charge flow data,/->For the first correction coefficient, an empirical value of 0.5 was taken.
The correction coefficient of the data in the group of the cold material flow data is adjusted by the first correction coefficient, namelyIf the difference between the groups of the cold material flow data is larger than 0.5, the data quantity in the groups needs to be amplified.
And repeating the steps to obtain the data quantity in the self-adaptive group of various related data, and regrouping the various related data according to the data quantity in the self-adaptive group. And analyzing the influence degree of the regrouped various related data and the real-time various related data on the temperature data of the mixture.
Because the cold charge temperature data and the mixture temperature data are in positive correlation, the cold charge flow data, the raw material water content data and the mixture temperature data are in negative correlation, and based on the negative correlation, the correlation coefficients between various correlated data and the mixture temperature data are calculated respectively, wherein the specific expression of the correlation coefficients is as follows:
in the method, in the process of the invention,is the correlation coefficient between the cold charge flow data and the mixture temperature data, +.>The number of groups regrouped for the cold flow data,/-for the group>As a pearson correlation coefficient function, +.>For the data sequence of the i-th group in the cold material flow data,>is the data sequence of the i-th group in the temperature data of the mixture.
The method comprises the steps of calculating the average value of the pearson correlation coefficient between each corresponding group of cold material flow data and mixture temperature data, dividing the data quantity in the groups according to the average value of the data in the self-adaptive group of each group, obtaining the correlation between the two data, and being beneficial to the subsequent judgment of the influence degree of the cold material flow data on the mixture temperature data.
The number of the groups of the mixture temperature data is the same as the number of the groups of the various related data, that is, if the correlation coefficient between the cold material flow data and the mixture temperature data is calculated, the mixture temperature data is grouped according to the same grouping specification as the cold material flow data to obtain the same number of the groups, and if the correlation coefficient between the cold material temperature data and the mixture temperature data is calculated, the mixture temperature data is grouped according to the same grouping specification as the cold material temperature data.
Wherein, the correlation coefficient between the cold charge flow data and the mixture temperature data is thatThe correlation coefficient between other data is the same as the calculation method described above.
For each data point time in various related data, in order to identify the influence degree of the first related data, the second related data and the third related data on the temperature data of the mixture at the corresponding time, for each data point in various related data, the nearest data point is taken as the centerThe data point serves as a neighborhood window for the data point.
And correcting the data points at corresponding moments in the temperature data of the mixture according to the distribution condition of each data point of various related data in the respective self-adaptive group. Analyzing data points at the t moment, and calculating an influence degree coefficient of the temperature data of the mixture, wherein the specific expression of the influence degree coefficient is as follows:
in the method, in the process of the invention,the influence degree coefficient of various related data on the temperature data of the mixture at the time t is shown,as a linear normalization function>、/>、/>The values of the cold material flow data, cold material temperature data and raw material water content data at the time t are respectively represented by +.>、/>、/>Respectively representing the correlation coefficient between the cold material flow data, the cold material temperature data and the water content data of the raw materials and the temperature data of the mixture.
If the data of the cold material flow data at the time t is larger, the cold material flow data is reduced under the influence of the correlation coefficient between the cold material flow data and the mixture temperature data, so that the influence degree of the data value on the mixture temperature data at the time t is reduced.
And correcting each data point of the mixture temperature data according to the influence degree coefficient of each data point of each correlation coefficient on the mixture temperature data to obtain a corrected mixture temperature value, wherein the specific expression is as follows:
in the method, in the process of the invention,for the corrected mixture temperature value at time t, < >>For the value of the mixture temperature data before being influenced by the relevant data at time t,/for>The influence degree coefficient of various related data on the temperature of the mixture at the time t is obtained.
After the data value of the mixture temperature at each time is corrected, more accurate mixture temperature data is obtained, so that the abnormal value in the mixture temperature data is more favorable for judgment.
The data monitoring module 103 acquires an abnormal mixture temperature value by using an LOF abnormality detection algorithm.
And (3) aiming at the corrected mixture temperature value of each moment, LOF anomaly detection is adopted to obtain anomaly factor scores of each moment.
And setting an abnormal threshold value, and marking the mixture temperature value with the abnormal factor score larger than the abnormal threshold value at each moment as an abnormal mixture temperature value. In this embodiment, the anomaly threshold is set to 3, and the implementer can set the anomaly threshold according to the actual situation, which is not limited in this example. For the identified abnormal mixture temperature value, the temperature is regulated to be within the normal temperature range, and the normal temperature range is set asThe operator can set the settings by himself.
So far, the temperature value of each abnormal mixture can be obtained by the method, and the temperature of the mixture for asphalt pavement construction can be monitored and regulated according to the temperature value of each abnormal mixture.
In conclusion, according to the method provided by the embodiment of the invention, the temperature of the mixture can be accurately monitored and regulated, and the problem of unreasonable quality of a construction pavement caused by abnormal temperature of the mixture is avoided. According to the embodiment of the invention, various correlation coefficients are obtained based on the influence degree of various kinds of correlation data on the temperature data of the mixture, the influence degree coefficient of the temperature of the mixture is obtained by integrating various kinds of correlation coefficients, so that a corrected temperature value of the mixture is obtained, the corrected temperature value of the mixture is subjected to anomaly detection to obtain an abnormal temperature of the mixture, and the temperature of the mixture is monitored and regulated. The embodiment of the invention has higher accuracy and efficiency of monitoring the temperature of the mixture.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (10)
1. The utility model provides a mix temperature monitoring and regulation and control system for bituminous paving construction, its characterized in that, the system includes:
the data acquisition module is used for acquiring mixture temperature data and various related data, wherein the various related data comprise cold material flow data, cold material temperature data and raw material water content data;
the data analysis module is used for grouping various related data and obtaining fluctuation coefficients of the data of each group according to the distribution of the data in the group; obtaining the variation degree of each group of data according to the fluctuation coefficient and the discrete degree of each group of data; obtaining intra-group data correction coefficients of each group of data according to the change degree and the difference of each group of data; combining the intra-group data correction coefficients and the number of the intra-group data to obtain the self-adaptive number of the intra-group data of each group of data; the data are regrouped according to the data quantity in the self-adaptive group; calculating correlation coefficients of various kinds of correlation data and mixture temperature data after regrouping; obtaining the influence degree coefficient of the mixture temperature data at each moment according to the correlation coefficient of the regrouped various related data and the mixture temperature data; obtaining corrected mixture temperature values at all times according to the influence degree coefficient of the mixture temperature data at all times and the mixture temperature at the corresponding time;
the data monitoring module is used for carrying out anomaly detection on the corrected mixture temperature value at each moment to obtain an anomaly factor fraction; and (3) monitoring and controlling the temperature of the mixture for asphalt pavement construction according to the abnormal factor fractions at each moment.
2. The system for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to claim 1, wherein the obtaining the fluctuation coefficient of each group of data according to the distribution of the data in the group comprises:
and calculating the difference value between each adjacent data according to the data in each group, and summing and averaging the difference values to obtain the fluctuation coefficient of each group of data.
3. The system for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to claim 1, wherein the step of obtaining the variation degree of each group of data according to the fluctuation coefficient and the dispersion degree of each group of data comprises the steps of:
and calculating the difference value between the maximum value and the minimum value of the data in each group, and taking the product of the difference value and the fluctuation coefficient of each group of data as the change degree of each group of data.
4. The system for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to claim 1, wherein the intra-group data correction coefficients of the data of each group are obtained according to the change degree and the difference of the data of each group, and the expression is:
in the method, in the process of the invention,intra-group data correction factor representing cold flow data,/->For the number of groups after the cold charge flow data packet,as a linear normalization function>、/>The degree of change of the data in the i-th and j-th groups is shown.
5. The system for monitoring and controlling the temperature of a mixture for asphalt pavement construction according to claim 1, wherein the self-adaptive intra-group data quantity of each group of data obtained by combining the intra-group data correction coefficient and the intra-group data quantity comprises:
and rounding the product of the number of the data in each group and the correction coefficient in each group to obtain the self-adaptive number of the data in each group.
6. The system for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to claim 5, wherein the calculation of the correlation coefficient between the regrouped various related data and the temperature data of the mixture is expressed as follows:
in the method, in the process of the invention,is the correlation coefficient between the cold charge flow data and the mixture temperature data, +.>The number of groups regrouped for the cold flow data,/-for the group>As a pearson correlation coefficient function, +.>For the data sequence of the i-th group in the cold material flow data,>is the data sequence of the i-th group in the temperature data of the mixture.
7. The system for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to claim 1, wherein the influence degree coefficient of the temperature data of the mixture at each moment is obtained according to the correlation coefficient of various kinds of correlation data after regrouping and the temperature data of the mixture, and the expression is:
in the method, in the process of the invention,the influence degree coefficient of various related data on the temperature data of the mixture at the time t is represented by +.>As a linear normalization function>、/>、/>The values of the cold material flow data, cold material temperature data and raw material water content data at the time t are respectively represented by +.>、/>、/>Respectively representing the correlation coefficient between the cold material flow data, the cold material temperature data and the water content data of the raw materials and the temperature data of the mixture.
8. The system for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to claim 1, wherein the obtaining the corrected mixture temperature value at each time according to the influence degree coefficient of the mixture temperature data at each time and the mixture temperature at the corresponding time comprises:
and taking the product of the mixture temperature data and the influence degree coefficient as a corrected mixture temperature value at the corresponding moment aiming at the mixture temperature data at each moment.
9. The system for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to claim 1, wherein the abnormal detection of the corrected mixture temperature value at each moment to obtain the abnormal factor score comprises:
and detecting the corrected mixture temperature value at each moment by using an anomaly detection algorithm, and marking the score obtained by detection as an anomaly factor score.
10. The system for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to claim 1, wherein the system for monitoring and controlling the temperature of the mixture for asphalt pavement construction according to the abnormal factor fractions at each moment comprises:
and taking the mixture temperature value with the abnormal factor fraction larger than the threshold value as an abnormal mixture temperature value, and regulating and controlling the abnormal mixture temperature value to be within a normal temperature range.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117632665A (en) * | 2024-01-26 | 2024-03-01 | 东莞市隆慧电子科技有限公司 | Running state real-time monitoring method for radiator |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101290947B1 (en) * | 2012-07-09 | 2013-07-29 | 군산대학교산학협력단 | Method for predicting compaction time of asphalt and recording medium storing program of the same |
| CN108279578A (en) * | 2018-01-30 | 2018-07-13 | 西南交通大学 | Hot-mix asphalt mixture gradation differentials simulation and optimization control system and method |
| CN114687266A (en) * | 2022-05-30 | 2022-07-01 | 江苏南通冠仟新材料科技有限公司 | Intelligent turning and mixing method for recycled asphalt concrete based on program control |
| CN115595853A (en) * | 2022-12-16 | 2023-01-13 | 福建省铁拓机械股份有限公司（Cn） | Aggregate control system and method of continuous asphalt mixture stirring equipment |
| CN116776094A (en) * | 2023-08-22 | 2023-09-19 | 山东盈动智能科技有限公司 | Crystal oscillator temperature test data intelligent analysis storage system |
| CN116839681A (en) * | 2023-08-29 | 2023-10-03 | 泰安岳首拌合站设备有限公司 | Multi-sensor-based asphalt stirring equipment monitoring method and system |
| CN116886737A (en) * | 2023-09-08 | 2023-10-13 | 成都返空汇网络技术有限公司 | Real-time temperature monitoring and early warning system and method for asphalt mixture transportation process |
-
2023
- 2023-10-25 CN CN202311384562.6A patent/CN117131314B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101290947B1 (en) * | 2012-07-09 | 2013-07-29 | 군산대학교산학협력단 | Method for predicting compaction time of asphalt and recording medium storing program of the same |
| CN108279578A (en) * | 2018-01-30 | 2018-07-13 | 西南交通大学 | Hot-mix asphalt mixture gradation differentials simulation and optimization control system and method |
| CN114687266A (en) * | 2022-05-30 | 2022-07-01 | 江苏南通冠仟新材料科技有限公司 | Intelligent turning and mixing method for recycled asphalt concrete based on program control |
| CN115595853A (en) * | 2022-12-16 | 2023-01-13 | 福建省铁拓机械股份有限公司（Cn） | Aggregate control system and method of continuous asphalt mixture stirring equipment |
| CN116776094A (en) * | 2023-08-22 | 2023-09-19 | 山东盈动智能科技有限公司 | Crystal oscillator temperature test data intelligent analysis storage system |
| CN116839681A (en) * | 2023-08-29 | 2023-10-03 | 泰安岳首拌合站设备有限公司 | Multi-sensor-based asphalt stirring equipment monitoring method and system |
| CN116886737A (en) * | 2023-09-08 | 2023-10-13 | 成都返空汇网络技术有限公司 | Real-time temperature monitoring and early warning system and method for asphalt mixture transportation process |
Non-Patent Citations (2)
| Title |
|---|
| BIN GUO; ZHI FANG; AILING REN; QIN GAO; TINGYU ZHU: "The Preparation of Eco-cementing Materials in Utilization of Semidry Desulphurization Ash with Steel Slag and Mineral Materials", 《2009 INTERNATIONAL CONFERENCE ON ENERGY AND ENVIRONMENT TECHNOLOGY》, pages 441 - 444 * |
| 张敏; 程文明: "热拌沥青混合料级配变化仿真与优化控制***及方法", 《中国博士学位论文全文数据库（电子期刊） 信息科技辑》, pages 78 - 94 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117632665A (en) * | 2024-01-26 | 2024-03-01 | 东莞市隆慧电子科技有限公司 | Running state real-time monitoring method for radiator |
| CN117632665B (en) * | 2024-01-26 | 2024-04-05 | 东莞市隆慧电子科技有限公司 | Running state real-time monitoring method for radiator |
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Denomination of invention: A Temperature Monitoring and Control System for Asphalt Pavement Construction Mixtures Granted publication date: 20240109 Pledgee: China Postal Savings Bank Limited by Share Ltd. Wenshang County sub branch Pledgor: Shandong Lichi Municipal Construction Engineering Co.,Ltd. Registration number: Y2024980004188 |