Disclosure of Invention
The invention aims to provide a logistics vehicle overload alarm management method based on load, which can quickly capture the immediate change of the load of a vehicle through instant load judgment, and the long-term load trend of the vehicle is reflected through calculating the average load in a period of time through sliding window judgment, so that the accuracy of overload judgment can be obviously improved through combining the two, the threshold value is enabled to be more in accordance with the actual running condition through dynamic adjustment, the adaptability is improved, the inertia force is compensated through adding an additional load compensation coefficient on a downhill road section, the overload risk caused by the superposition of the self weight of the vehicle and the weight of the goods during downhill is prevented, the running safety is enhanced, and the problems in the prior art can be solved.
In order to achieve the above purpose, the present invention provides the following technical solutions:
The logistics vehicle overload alarm management method based on the load comprises the following steps:
Monitoring load information of the vehicle in real time, and transmitting the monitored load information of the vehicle to a data processing center for data processing, wherein the data processing comprises data preprocessing and calibration;
Setting a dynamic threshold value of the processed load data, carrying out overload judgment after setting, carrying out early warning according to an overload judgment result, and finally generating a statistical report from the early warning result;
The dynamic threshold setting process comprises the following steps:
Firstly, retrieving vehicle identification information, and inquiring a vehicle characteristic database according to the vehicle identification information, wherein rated load parameters of different vehicle types are prestored in the database, and reference load data of the vehicle are obtained according to the vehicle characteristic database;
Monitoring the current running speed of the vehicle in real time through a vehicle-mounted GPS, and detecting the road gradient of a running road section of the vehicle by using the GPS and road map data;
a dynamic safe load threshold is calculated based on the reference load data, the travel speed, and the road grade of the vehicle.
Preferably, the real-time monitoring of load information of the vehicle includes:
Acquiring load information of a vehicle;
The load information comprises the total weight of the vehicle, axle load, wheel load, cargo weight and basic information of the vehicle;
The total weight of the vehicle is the self weight of the vehicle and the weight of the loaded goods, real-time data acquisition is carried out through a vehicle-mounted sensor, the axle load is the load of each axle, data acquisition is carried out through a pressure sensor on each axle, the wheel load is the weight born by each wheel, data acquisition is carried out through a pressure sensor on each wheel, the weight of the goods is the weight of the loaded goods, and the weight of a carriage or a tray is monitored in real time through a vehicle-mounted electronic scale;
and finally obtaining the real-time monitored vehicle load information.
Preferably, transmitting the monitored vehicle load information to a data processing center for data processing, including:
data integration is carried out on the collected vehicle load information, and data formatting is carried out after the data integration;
carrying out data transmission on the vehicle loading information formatted by the data by adopting a wireless communication technology;
The data processing center receives the vehicle load information and performs data verification after receiving the vehicle load information;
The data verification includes checking of data packets, time stamps and repeated data;
And storing the qualified data in a database or a cloud platform.
Preferably, the monitored load information is transmitted to a data processing center for data processing, and the method further comprises the steps of:
carrying out data preprocessing on data in a database or a cloud platform;
the data preprocessing comprises data denoising, missing value processing, abnormal value detection and data standardization;
Data calibration is carried out after data preprocessing, and the data calibration comprises sensor error correction, sensor calibration, data fusion and multi-sensor calibration;
and after the data calibration is finished, consistency and integrity verification are carried out, and load data after the verification is finished are obtained.
Preferably, the dynamic threshold setting of the processed load data further includes:
When the vehicle is on a downhill road section, an additional load compensation coefficient is added, and the lifting range of the load compensation coefficient is between 8% and 15% of the compensation inertia force;
And finally obtaining a dynamic threshold value of the vehicle, wherein the dynamic threshold value is a reference threshold value for judging whether the vehicle is overloaded.
Preferably, in calculating the dynamic safe load threshold value, the method further comprises:
When the parallel lane empty margin of the vehicle is smaller than the set empty threshold, the front traffic flow compensation coefficient and the rear traffic flow compensation coefficient are increased, specifically:
according to the running conditions of adjacent vehicles within a set monitoring range, which are monitored in real time by the vehicle-mounted monitoring equipment, determining the parallel lane empty quantity of each vehicle according to the running conditions, and determining a first running vehicle in front of the current vehicle and a second running vehicle behind the current vehicle;
acquiring a first distance between each first running vehicle and the current vehicle, and determining a safety distance level between each first running vehicle and the current vehicle by comparing the first distance with a first preset distance threshold range;
collecting first running vehicles with the same safety distance level to obtain a first safety distance-vehicle combination;
determining a first compensation coefficient from a preset vehicle-distance compensation mapping table according to the safety distance level of the first safety distance-vehicle combination and the number of vehicles correspondingly contained;
summarizing first compensation coefficients of all first safety distance-vehicle combinations to obtain a front traffic flow compensation coefficient;
acquiring a second distance between each second driving vehicle and the current vehicle, and determining a safety distance level between each second driving vehicle and the current vehicle by comparing the second distance with a second preset distance threshold range;
Collecting second running vehicles with the same safety distance level to obtain a second safety distance-vehicle combination;
Determining a second compensation coefficient from a preset vehicle-distance compensation mapping table according to the safety distance level of the second safety distance-vehicle combination and the number of vehicles correspondingly contained;
and summarizing the second compensation coefficients of all the second distance-vehicle combinations to obtain the rear traffic flow compensation coefficient.
Preferably, the overload judgment is performed after the setting is completed, and the early warning processing is performed according to the overload judgment result, including:
Carrying out overload judgment on the vehicle according to the set dynamic threshold value;
The overload judgment is a double judgment mechanism, and the double judgment mechanism combines the instantaneous load and the average load of the sliding window;
judging that the instantaneous load is from a database or a cloud platform of a data processing center every 0.5 seconds, and calling the real-time collected data;
And comparing the fetched real-time acquisition data with the dynamic threshold value, and triggering overload judgment in the period if the instantaneous load is greater than the dynamic threshold value. If the instantaneous load is less than or equal to the dynamic threshold, the period does not trigger overload judgment;
the sliding window is judged to adopt a 60-second sliding window as one period, and then the average value of the load in 60 seconds is calculated in each period;
and comparing the load average value with the dynamic threshold value, judging that the load is overloaded if the load average value is larger than the dynamic threshold value, and not triggering the overload judgment if the load average value is smaller than the dynamic threshold value.
Preferably, the overload judgment is performed after the setting is completed, and the early warning processing is performed according to the overload judgment result, and the method further comprises the following steps:
the results of the sliding window judgment and the instantaneous load judgment are overload judgment if the dynamic threshold value is exceeded in three continuous periods;
Finally obtaining an overloaded vehicle and an uninverted vehicle;
Calculating the difference value of an overload threshold value of the overloaded vehicle, and judging the overload level according to the calculation result of the difference value;
wherein, the larger the threshold value of the difference value calculation result is, the higher the overload level is;
Overload levels are classified into slight overload, moderate overload and heavy overload;
Early warning treatment of different intensities is carried out according to different overload levels;
the method comprises the steps of carrying out early warning treatment of slight overload, starting an audible and visual alarm and locking the speed limit of a vehicle when the vehicle is out of limit, and automatically sending a encrypted alarm message to a supervision platform after the early warning treatment of severe overload lasts for 5 minutes, synchronously cutting off power output and generating an illegal event log.
Preferably, the overload level determination according to the difference calculation result includes:
If the instantaneous loads of three continuous periods exceed the dynamic threshold according to the instantaneous load judging result of the current overload vehicle, calculating a reference overload score by utilizing the instantaneous loads of the current three periods and the dynamic threshold;
if the average load value of three continuous periods exceeds the dynamic threshold according to the sliding window judging result of the current overload vehicle, calculating the reference overload score by utilizing the average load value and the dynamic threshold of the current three periods;
and taking the obtained reference overload score as a difference value calculation result, and judging the overload level.
Preferably, the method further comprises:
the method comprises the steps of acquiring and analyzing historical load information of an overloaded vehicle in a set time period, and acquiring the total historical overload times, overload levels of each historical overload and historical overload time if a historical overload record exists;
Determining the time interval between the historical overload time and the current time of each historical overload, and marking the time interval as a reference time interval;
Based on the reference time interval with the current moment, corresponding time attenuation weights are distributed to each historical overload;
if the historical overload total times of the overloaded vehicle do not exceed the preset overload times, the corresponding reference overload score is not adjusted;
If the total historical overload times of the overloaded vehicle exceeds the preset overload times, dividing the historical overload of different overload levels according to the comparison result of the reference time interval and the set time length constraint to obtain corresponding first historical overload combinations and second historical overload combinations;
acquiring reference attenuation weights of corresponding first historical overload combinations and reference attenuation weights of second historical overload combinations of different overload levels;
inputting all the historical overload corresponding to each overload level contained in the current overload vehicle into a pre-established pattern recognition model at the moment of the historical overload to obtain a pattern recognition result corresponding to the overload level;
According to the pattern recognition result, distributing a pattern adjustment weight to each overload level contained in the current overload vehicle;
combining mode adjustment weights of different overload levels contained in the current overload vehicle with reference attenuation weights corresponding to the first historical overload combination and reference attenuation weights of the second historical overload combination to obtain a target adjustment value;
And adjusting the reference overload score of the current overload vehicle by using the acquired target adjustment value.
Preferably, the generating the statistics report of the early warning result finally includes:
integrating overload information, overload grade information and early warning processing information of the vehicle with basic information of the vehicle;
After the data integration is completed, a vehicle statistical report is generated, wherein the generated statistical report comprises basic information, overload event, overload processing result and safety risk relation of each vehicle;
the vehicle statistical report is presented in the form of a chart, a table, an image and a text report;
And automatically sending the generated vehicle statistical report to corresponding staff in a mail, short message and system notification mode.
Compared with the prior art, the invention has the following beneficial effects:
1. The logistics vehicle overload alarm management method based on the load, provided by the invention, has the advantages that the steps of data integration, formatting, verification, preprocessing and the like ensure the accuracy and the reliability of data, reduce false alarm or missing alarm conditions caused by human factors or equipment errors, and monitor the load of the vehicle in real time is beneficial to preventing overload phenomenon and reduce traffic accident risks caused by overload. For vehicles for transporting dangerous goods, the real-time monitoring of the load can ensure that the vehicles run in a safety range, so that potential safety hazards are reduced.
2. According to the logistics vehicle overload warning management method based on the load, the threshold value is dynamically adjusted to be more in accordance with the actual running condition, the adaptability is improved, and the inertia force is compensated by adding the additional load compensation coefficient on the downhill road section, so that overload risks caused by the superposition of the vehicle dead weight and the cargo weight during downhill are prevented, and the running safety is enhanced.
3. According to the logistics vehicle overload alarm management method based on the load, the instantaneous load judgment can rapidly capture the immediate change of the load of the vehicle, the sliding window judgment reflects the long-term load trend of the vehicle by calculating the average load in a period of time, and the combination of the two can significantly improve the accuracy of overload judgment.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the problem that in the prior art, after the load data of the vehicle is acquired, no further data processing and calibration are performed, so that the original data is inaccurate, referring to fig. 1 and 2, the present embodiment provides the following technical scheme:
The logistics vehicle overload alarm management method based on the load comprises the following steps:
Monitoring load information of the vehicle in real time, and transmitting the monitored load information of the vehicle to a data processing center for data processing, wherein the data processing comprises data preprocessing and calibration;
and setting a dynamic threshold value of the processed load data, carrying out overload judgment after setting, carrying out early warning processing according to an overload judgment result, and finally generating a statistical report from the early warning result.
Specifically, through real-time monitoring of the load information of the vehicle, the overload behavior can be found and early-warned in time, and omission in the traditional spot check mode is effectively avoided. The system has the advantages that the response speed is high, the data acquisition, analysis and early warning can be completed in a short time, the treatment efficiency is improved, the accuracy and the reliability of the monitored data are ensured by adopting an advanced weighing sensor and a data processing technology, noise and abnormal values in the data can be removed in a data preprocessing step, the risk of model overfitting is reduced, the generalization capability of the model is improved, the reliability and the accuracy of the data are improved, and the threshold value is allowed to be dynamically adjusted according to actual requirements so as to adapt to different application scenes and regulation requirements. The flexibility enables the management method to be more accurately suitable for various conditions, improves early warning accuracy, greatly reduces the burden of manual management through an automatic data processing and early warning mechanism, and improves management efficiency. Meanwhile, the overload behavior is early-warned and processed in time, so that road damage and traffic accidents caused by overload are reduced, the maintenance cost and the accident handling cost are reduced, and finally the generated early-warning statistical report can provide powerful decision support for the management department. Through analysis and report of the data, the management department can know the road transportation condition more accurately, and provide basis for formulating more scientific traffic management strategies.
Monitoring load information of a vehicle in real time, comprising:
Acquiring load information of a vehicle;
The load information comprises the total weight of the vehicle, axle load, wheel load, cargo weight and basic information of the vehicle;
The total weight of the vehicle is the self weight of the vehicle and the weight of the loaded goods, real-time data acquisition is carried out through a vehicle-mounted sensor, the axle load is the load of each axle, data acquisition is carried out through a pressure sensor on each axle, the wheel load is the weight born by each wheel, data acquisition is carried out through a pressure sensor on each wheel, the weight of the goods is the weight of the loaded goods, and the weight of a carriage or a tray is monitored in real time through a vehicle-mounted electronic scale;
and finally obtaining the real-time monitored vehicle load information.
Specifically, the real-time property of the vehicle load information is ensured by collecting data in real time through the vehicle-mounted sensor and the vehicle-mounted electronic scale. Whether the total weight of the vehicle, the axle load, the wheel load or the cargo weight can obtain the latest data in a short time, and the high-precision sensor and the electronic scale are used for data acquisition, so that the accuracy of the data is improved, errors caused by manual measurement or estimation are avoided, various aspects of the load of the vehicle are covered, including the total weight of the vehicle, the axle load, the wheel load, the cargo weight and the basic information of the vehicle, and comprehensive load information is provided. The addition of the basic information (such as the type of the vehicle, the vehicle identification information and the time stamp) of the vehicle ensures that the data is more complete, is convenient for subsequent analysis and management, and the real-time monitoring of the load of the vehicle is beneficial to preventing overload phenomenon and reducing the risk of traffic accidents caused by overload. For vehicles for transporting dangerous goods, the real-time monitoring load can ensure that the vehicles run in a safety range, potential safety hazards are reduced, manual intervention is reduced in an automatic data acquisition and processing flow, and working efficiency is improved. The real-time monitoring data can be fed back to the manager or the driver in real time, so that the manager or the driver can make adjustment in time, the transportation plan is optimized, and the real-time collected data can be stored in a database, so that the follow-up data analysis and mining are facilitated. Through data analysis, the load distribution condition, the transportation efficiency and the like of the vehicle can be known, and powerful support is provided for optimizing the transportation strategy.
Transmitting the monitored vehicle load information to a data processing center for data processing, comprising:
data integration is carried out on the collected vehicle load information, and data formatting is carried out after the data integration;
carrying out data transmission on the vehicle loading information formatted by the data by adopting a wireless communication technology;
The data processing center receives the vehicle load information and performs data verification after receiving the vehicle load information;
The data verification includes checking of data packets, time stamps and repeated data;
And storing the qualified data in a database or a cloud platform.
Carrying out data preprocessing on data in a database or a cloud platform;
the data preprocessing comprises data denoising, missing value processing, abnormal value detection and data standardization;
Data calibration is carried out after data preprocessing, and the data calibration comprises sensor error correction, sensor calibration, data fusion and multi-sensor calibration;
and after the data calibration is finished, consistency and integrity verification are carried out, and load data after the verification is finished are obtained.
Specifically, the real-time monitoring and transmission of the vehicle load information can be realized through an automatic data acquisition and wireless communication technology, the detection efficiency is greatly improved, the manual intervention and waiting time are reduced, the real-time monitoring function can effectively prevent the passing of overrun overload vehicles, the safety and smoothness of road traffic are ensured, the steps of data integration, formatting, verification, preprocessing and the like are ensured, the accuracy and reliability of data are ensured, the false alarm or missing report condition caused by human factors or equipment errors is reduced, the data denoising, missing value processing, abnormal value detection and data standardization in the data preprocessing step are further improved, the quality of data is further improved, and an intelligent data processing flow is adopted, the method comprises the steps of data integration, formatting, transmission, verification, preprocessing, calibration and the like, automatic processing of data is realized, sensor error correction, sensor calibration, data fusion and multi-sensor calibration in the data calibration step are improved, the accuracy and consistency of the data are improved, the processed load data can provide powerful decision support for traffic management departments, the traffic management departments can be better helped to know road traffic conditions, corresponding management measures are formulated, the rules and characteristics of traffic illegal behaviors can be found through data analysis, scientific basis is provided for law enforcement, the safety and compliance of road traffic are guaranteed through real-time monitoring and management of the load conditions of vehicles, and traffic accidents and illegal behaviors caused by overrun overload are reduced.
In order to solve the problem that in the prior art, no targeted dynamic adjustment is performed according to the actual situation of the vehicle, so that the load factor of the vehicle is not compensated when the vehicle goes up and down a slope, and the load threshold is deviated, referring to fig. 1 and 2, the present embodiment provides the following technical scheme:
Setting a dynamic threshold of the processed load data, including:
The dynamic threshold setting process comprises the following steps:
Firstly, retrieving vehicle identification information, and inquiring a vehicle characteristic database according to the vehicle identification information, wherein rated load parameters of different vehicle types are prestored in the database, and reference load data of the vehicle are obtained according to the vehicle characteristic database;
Monitoring the current running speed of the vehicle in real time through a vehicle-mounted GPS, and detecting the road gradient of a running road section of the vehicle by using the GPS and road map data;
Calculating a dynamic safety load threshold according to the reference load data, the running speed and the road gradient of the vehicle;
When the vehicle is on a downhill road section, an additional load compensation coefficient is added, and the lifting range of the load compensation coefficient is between 8% and 15% of the compensation inertia force;
And finally obtaining a dynamic threshold value of the vehicle, wherein the dynamic threshold value is a reference threshold value for judging whether the vehicle is overloaded.
Specifically, by retrieving the vehicle identification information and querying the vehicle feature database, the scheme can obtain specific reference load data for each vehicle. The personalized processing is more accurate than the unified standard, the real load capacity of different vehicle types can be better adapted, the vehicle running speed is monitored in real time by utilizing the vehicle-mounted GPS, the road gradient is detected by combining the road map data, and the scheme can adjust the safety load threshold in real time. The dynamic adjustment enables the threshold value to be more in line with the actual running condition, the adaptability is improved, and particularly in a downhill road section, the scheme compensates the inertia force by adding an additional load compensation coefficient, which is helpful for preventing overload risks caused by superposition of the self weight of the vehicle and the weight of goods in the downhill road section, thereby enhancing the running safety, combining various technical means such as vehicle identification, GPS monitoring, map data and the like, and realizing intelligent management of load data. The method not only improves management efficiency, but also reduces the possibility of human intervention and erroneous judgment, and the scheme is helpful for preventing overload behaviors in advance by setting a dynamic threshold value as a reference for judging whether overload exists. This not only protects the road infrastructure, but also reduces the risk of traffic accidents.
In order to solve the problem that in the prior art, no targeted dynamic adjustment is performed according to the influence of the surrounding traffic conditions of the vehicle on the load safety, resulting in deviation of the load threshold, referring to fig. 1 and 2, the present embodiment provides the following technical scheme:
in calculating the dynamic safe load threshold, further comprising:
When the parallel lane empty margin of the vehicle is smaller than the set empty threshold, the front traffic flow compensation coefficient and the rear traffic flow compensation coefficient are increased, specifically:
according to the running conditions of adjacent vehicles within a set monitoring range, which are monitored in real time by the vehicle-mounted monitoring equipment, determining the parallel lane empty quantity of each vehicle according to the running conditions, and determining a first running vehicle in front of the current vehicle and a second running vehicle behind the current vehicle;
acquiring a first distance between each first running vehicle and the current vehicle, and determining a safety distance level between each first running vehicle and the current vehicle by comparing the first distance with a first preset distance threshold range;
collecting first running vehicles with the same safety distance level to obtain a first safety distance-vehicle combination;
determining a first compensation coefficient from a preset vehicle-distance compensation mapping table according to the safety distance level of the first safety distance-vehicle combination and the number of vehicles correspondingly contained;
summarizing first compensation coefficients of all first safety distance-vehicle combinations to obtain a front traffic flow compensation coefficient;
acquiring a second distance between each second driving vehicle and the current vehicle, and determining a safety distance level between each second driving vehicle and the current vehicle by comparing the second distance with a second preset distance threshold range;
Collecting second running vehicles with the same safety distance level to obtain a second safety distance-vehicle combination;
Determining a second compensation coefficient from a preset vehicle-distance compensation mapping table according to the safety distance level of the second safety distance-vehicle combination and the number of vehicles correspondingly contained;
and summarizing the second compensation coefficients of all the second distance-vehicle combinations to obtain the rear traffic flow compensation coefficient.
In this embodiment, the vehicle-mounted monitoring device is a monitoring device mounted on a vehicle and used for capturing images of the front, rear, left and right directions of the vehicle in real time, and generally refers to a high-definition camera, the set monitoring range is a predetermined area range in which the vehicle-mounted monitoring device effectively monitors, the running condition of adjacent vehicles is the relative positions and the number of other vehicles around the current vehicle, the parallel lane space is the space between the lane where the current vehicle is located and the adjacent lanes, namely, the space where the current vehicle can move transversely and does not collide with other vehicles, and the set space threshold is a predetermined value for judging whether the space of the parallel lane is sufficient or not, and is generally set to be the width of the vehicle body of the current vehicle.
In the embodiment, the first traveling vehicle refers to a vehicle traveling in front of a current vehicle, the second traveling vehicle refers to a vehicle traveling behind the current vehicle, the first distance refers to a distance between the current vehicle and the first traveling vehicle, the first preset distance threshold range is a preset distance range used for dividing the distance between the first traveling vehicle and the current vehicle into different grades, and the safe distance grades comprise three grades of short distance, medium distance and long distance.
In this embodiment, for example, there is a first preset distance threshold range ofM is expressed as a unit in which the first distances of the first traveling vehicles a1, a2 and a3 from the current vehicle are 1.3m, 2.3m and 4.1m, respectively, and at this time, the safety distance level of the first traveling vehicle a1 from the current vehicle is a near distance level, the safety distance level of the first traveling vehicle a2 from the current vehicle is a middle distance level, and the safety distance level of the first traveling vehicle a3 from the current vehicle is a far distance level.
In this embodiment, the first safe distance-vehicle combination refers to a combination obtained by integrating first traveling vehicles of the same safe distance level, and the preset vehicle-distance compensation mapping table is a preset table, which is composed of different safe distance levels, vehicle number ranges and corresponding compensation coefficients (the value range is (0, 1)), wherein, for each safe distance level, different vehicle number ranges are listed in the preset vehicle-distance compensation mapping table, the compensation coefficients corresponding to the different vehicle number ranges are different, for example, in a close range level, the vehicle number ranges are divided into 1-2, 3-5 and 6 and above, and the corresponding compensation coefficients are 0.5, 0.55 and 0.7, respectively.
In the embodiment, the first compensation coefficient is a compensation coefficient which is determined by searching a preset vehicle-distance compensation mapping table according to the distance level of the first safe distance-vehicle combination and the number of the included vehicles, the front traffic flow compensation coefficient is obtained by summing the corresponding first compensation coefficients of all the acquired first safe distance-vehicle combinations and is used for adjusting the load threshold of the current vehicle based on the consideration of the current traffic flow situation in front of the vehicle, wherein, for example, the front traffic flow compensation coefficient of the existing vehicle 1 is c1, the adjusted load threshold is obtained, wherein,E is a base number of natural logarithm, and the value is 2.7;
Where, for example, when c1=0.5,Indicating that the front traffic is dense, the load threshold is reduced by 37%.
In this embodiment, for example, there is a dynamic threshold adjustment flow as follows:
Input of adjusting the front load thresholdTon, downhill slope 5 degrees, compensation coefficient +10% > outputTon of water;
Input forward traffic flow c1=0.5→output dynamic thresholdTons.
In this embodiment, the second distance refers to a distance between the current vehicle and the second traveling vehicle, the second preset distance threshold range is a preset distance range, and is used to divide the distance between the second traveling vehicle and the current vehicle into different levels, and the second safe distance-vehicle combination refers to a combination obtained by integrating second traveling vehicles with the same safe distance level.
In the embodiment, the second compensation coefficient is a compensation coefficient which is determined by searching a preset vehicle-distance compensation mapping table according to the safety distance level of the second safety distance-vehicle combination and the number of the included vehicles, the rear traffic flow compensation coefficient is obtained by summing the corresponding second compensation coefficients of all the acquired second safety distance-vehicle combinations and is used for adjusting the load threshold of the current vehicle based on the consideration of the current traffic flow situation behind the current vehicle, wherein, for example, the rear traffic flow compensation coefficient of the existing vehicle 2 is c2, the adjusted load threshold is obtained, wherein,The load threshold before the adjustment of the vehicle 2 is represented, and e is represented as a base number of natural logarithm and the value is 2.7.
In this embodiment, the construction of the forward/backward traffic flow compensation formula refers to the idea of a following model, specifically, the vehicle-mounted monitoring device monitors the running condition of the adjacent vehicle in real time, and determines the parallel lane empty allowance of the vehicle according to the running condition, and this process is actually to simulate the perception and response of the rear vehicle to the running state of the front vehicle in the following model;
Depending on the safe distance level of the front vehicle from the current vehicle, if the front traffic is denser, the current vehicle may face greater driving pressure and potential risk, and therefore, the front traffic compensation coefficient is appropriately increased to reflect the potential limitation of the front traffic on the current vehicle load carrying capacity. Similarly, according to the safety distance level between the rear vehicle and the current vehicle, if the rear traffic is denser, the rear traffic compensation coefficient is appropriately increased so as to consider the influence of the rear traffic on the current vehicle running state.
In the embodiment, the adjustment formula of the front/rear traffic flow compensation coefficient is based on normal distribution transformation, and the change of the load threshold value follows the transformed normal distribution through the combination of an exponential function and a root number, so that the load change condition in the actual traffic flow can be better described, wherein the exponential function is used for smoothly showing the change of the load threshold value along with the front/rear traffic flow compensation coefficient, the root number is used for performing the evolution operation on the result of the exponential function, further, the change of the load threshold value is smoother and better accords with the characteristic of the normal distribution, accords with the gradual change characteristic in the actual traffic flow, and the load change condition in the actual traffic flow can be better described.
The technical scheme comprises the following working principles of firstly determining the parallel lane empty allowance of a current vehicle, identifying the front and rear traveling vehicles, then respectively calculating the distances between the front and rear vehicles and the current vehicle when the parallel lane empty allowance is smaller than a set threshold value, dividing the vehicles into different safety distance grades according to a preset distance threshold value range, carrying out aggregation treatment on the vehicles with the same safety distance grade to form a safety distance-vehicle combination, then searching and determining corresponding compensation coefficients according to the safety distance grades and the number of the vehicles in a preset vehicle-distance compensation mapping table, and finally summarizing the compensation coefficients of all the safety distance-vehicle combinations to respectively obtain a front traffic flow compensation coefficient and a rear traffic flow compensation coefficient so as to adjust the load threshold value of the current vehicle.
The technical scheme has the beneficial effects that the running conditions around the vehicle are monitored in real time, and the influence of the front and rear traffic flows on the load safety is considered, so that the running environment of the current vehicle can be estimated more accurately, the load threshold is dynamically adjusted, the vehicle can keep safe running in a complex traffic environment, and meanwhile, the adaptability of the vehicle is enhanced and the traffic accident is effectively prevented.
In order to solve the problem that in the prior art, when overload judgment is performed on a vehicle, a more accurate judgment mode is not adopted, so that the overload judgment is inaccurate, referring to fig. 1 and 2, the present embodiment provides the following technical scheme:
After the setting is completed, the overload judgment is carried out, early warning processing is carried out according to the overload judgment result, comprising the following steps:
Carrying out overload judgment on the vehicle according to the set dynamic threshold value;
The overload judgment is a double judgment mechanism, and the double judgment mechanism combines the instantaneous load and the average load of the sliding window;
judging that the instantaneous load is from a database or a cloud platform of a data processing center every 0.5 seconds, and calling the real-time collected data;
And comparing the fetched real-time acquisition data with the dynamic threshold value, and triggering overload judgment in the period if the instantaneous load is greater than the dynamic threshold value. If the instantaneous load is less than or equal to the dynamic threshold, the period does not trigger overload judgment;
the sliding window is judged to adopt a 60-second sliding window as one period, and then the average value of the load in 60 seconds is calculated in each period;
and comparing the load average value with the dynamic threshold value, judging that the load is overloaded if the load average value is larger than the dynamic threshold value, and not triggering the overload judgment if the load average value is smaller than the dynamic threshold value.
The results of the sliding window judgment and the instantaneous load judgment are overload judgment if the dynamic threshold value is exceeded in three continuous periods;
Finally obtaining an overloaded vehicle and an uninverted vehicle;
Calculating the difference value of an overload threshold value of the overloaded vehicle, and judging the overload level according to the calculation result of the difference value;
wherein, the larger the threshold value of the difference value calculation result is, the higher the overload level is;
Overload levels are classified into slight overload, moderate overload and heavy overload;
Early warning treatment of different intensities is carried out according to different overload levels;
the method comprises the steps of carrying out early warning treatment of slight overload, starting an audible and visual alarm and locking the speed limit of a vehicle when the vehicle is out of limit, and automatically sending a encrypted alarm message to a supervision platform after the early warning treatment of severe overload lasts for 5 minutes, synchronously cutting off power output and generating an illegal event log.
In this embodiment, the initial reference value of the dynamic threshold value refers to the maximum safe load of the vehicle factory calibration.
Specifically, a dual mechanism of combining instantaneous load determination and sliding window determination is adopted, which can more comprehensively and accurately evaluate the load condition of the vehicle. The instantaneous load judgment can rapidly capture the immediate change of the load of the vehicle, the sliding window judgment reflects the long-term load trend of the vehicle by calculating the average load in a period of time, the combination of the instantaneous load judgment and the sliding window judgment can remarkably improve the accuracy of the overload judgment, and the dynamic threshold is used for carrying out the overload judgment, so that the threshold can be adjusted according to the actual situation so as to adapt to different roads, different vehicle types or different transportation demands. The flexibility enables the scheme to be widely applied to various scenes, the practicability is improved, the early warning processing with different intensities is carried out according to the overload level, and measures can be timely taken when the vehicle is overloaded, so that potential safety hazards are prevented. Meanwhile, the hierarchical early warning processing can take appropriate measures according to different conditions, so that excessive reaction or neglect problems are avoided, and the database or the cloud platform of the data processing center is utilized for real-time data acquisition and analysis, so that advanced technical means are embodied. By acquiring and processing the data in real time, the scheme can realize the real-time monitoring and judging of the load of the vehicle, improve the working efficiency and accuracy, and take measures of cutting off the power output and generating the illegal event log under the condition of heavy overload, so that the safety of the vehicle under the extreme condition can be ensured. Meanwhile, the encrypted message alarm message is automatically sent to the supervision platform, and related departments can be timely informed to process, so that the safety of the scheme is further improved.
And according to the result of the difference value calculation, making overload level decision, comprising the following steps:
If the instantaneous loads of three continuous periods exceed the dynamic threshold according to the instantaneous load judging result of the current overload vehicle, calculating a reference overload score by utilizing the instantaneous loads of the current three periods and the dynamic threshold;
if the average load value of three continuous periods exceeds the dynamic threshold according to the sliding window judging result of the current overload vehicle, calculating the reference overload score by utilizing the average load value and the dynamic threshold of the current three periods;
and taking the obtained reference overload score as a difference value calculation result, and judging the overload level.
In this embodiment, the calculation formula of the reference overload score determined according to the instantaneous load determination result or the sliding window determination result is as follows:
;
In the formula,The reference overload score is determined according to the instantaneous load judging result of the current overload vehicle or the sliding window judging result; The instantaneous load of the ith period of the current overload vehicle is determined according to the instantaneous load result, or the average value of the load of the ith period of the current overload vehicle is determined according to the sliding window judging result, wherein i=1, 2 and 3; E represents the base number of natural logarithm, and the value is 2.7;
among these, for example, there are the following examples of calculation of the reference overload score:
Input: Output → output。
In this embodiment, calculating the reference overload score according to the instantaneous load determination result or the sliding window determination result is based on the principle of load difference accumulation of the average load value and the dynamic threshold value of three consecutive periods, and the reference overload score is converted into a value between 0 and 1 by using an exponential model, wherein the exponential model is used for smoothly showing the change of the reference overload score along with the load difference accumulation.
The technical scheme has the advantages that the overload state of the vehicle can be accurately captured and quantified no matter the instantaneous high load or the continuous high load by determining the reference overload score according to the instantaneous load judging result or the sliding window judging result, and finer measurement standard is provided for overload judgment by introducing the reference overload score, so that the overload grade is more scientifically and reasonably divided, and powerful data support is provided for subsequent early warning processing.
In order to solve the problem that in the prior art, when the overload level is classified for the vehicle, the reference overload score determined based on the calculation result of the difference value of the overload threshold value of the overload vehicle is inaccurate, so that the overload level is not classified scientifically, referring to fig. 1 and 2, the embodiment provides the following technical scheme:
the method comprises the steps of acquiring and analyzing historical load information of an overloaded vehicle in a set time period, and acquiring the total historical overload times, overload levels of each historical overload and historical overload time if a historical overload record exists;
Determining the time interval between the historical overload time and the current time of each historical overload, and marking the time interval as a reference time interval;
Based on the reference time interval with the current moment, corresponding time attenuation weights are distributed to each historical overload;
if the historical overload total times of the overloaded vehicle do not exceed the preset overload times, the corresponding reference overload score is not adjusted;
If the total historical overload times of the overloaded vehicle exceeds the preset overload times, dividing the historical overload of different overload levels according to the comparison result of the reference time interval and the set time length constraint to obtain corresponding first historical overload combinations and second historical overload combinations;
acquiring reference attenuation weights of corresponding first historical overload combinations and reference attenuation weights of second historical overload combinations of different overload levels;
inputting all the historical overload corresponding to each overload level contained in the current overload vehicle into a pre-established pattern recognition model at the moment of the historical overload to obtain a pattern recognition result corresponding to the overload level;
According to the pattern recognition result, distributing a pattern adjustment weight to each overload level contained in the current overload vehicle;
combining mode adjustment weights of different overload levels contained in the current overload vehicle with reference attenuation weights corresponding to the first historical overload combination and reference attenuation weights of the second historical overload combination to obtain a target adjustment value;
And adjusting the reference overload score of the current overload vehicle by using the acquired target adjustment value.
In this embodiment, the set time period refers to a specific time period preset for acquiring historical load information for evaluating overload behavior, and the historical load information refers to a data record of each load of the overloaded vehicle in a past period, including the weight, load time, load place, and the like of each load.
In this embodiment, the historical overload record refers to the overload record of the vehicle in the historical load information, including the time of overload, the weight of overload, the level of overload, etc., the total number of historical overload refers to the total number of all overload records of the vehicle in the past period of time, the historical overload moment refers to the specific time point when each overload occurs, and the historical overload refers to the past overload event.
In the present embodiment, the reference time interval refers to the time difference between each time of historical overload time and the current time, and the time decay weight is expressed asWherein e is expressed as a constant and has a value of 2.7; Expressed as an attenuation coefficient, controls the weight-down speed; The preset number of overloads is a preset threshold value, such as 5 times, for judging whether the vehicle is frequently overloaded.
In this embodiment, the historical overload of each overload level, in which the reference time interval does not exceed the set time length constraint, is divided into a first historical overload combination, and the historical overload of each overload level, in which the reference time interval exceeds the set time length constraint, is divided into a second historical overload combination, where the set time length constraint refers to a preset limit on the time difference between the occurrence time point of the historical load behavior and the current time, for example, 1 month.
In this embodiment, the reference decay weight is obtained by averaging the time decay weight average value with the average value of the time decay weight maximum value and the time decay weight minimum value within the current historical overload combination.
In the embodiment, the pattern recognition model is a model for recognizing an overload pattern by training a neural network by utilizing a large amount of collected historical load data, and in terms of the neural network structure, a deep neural network comprising a plurality of hidden layers is designed, wherein an input layer is responsible for receiving the historical load data, the dimension of the input data is determined according to specific load records and comprises a plurality of characteristics such as time stamps, load capacity and the like, the input data is sent to the hidden layer for characteristic extraction and conversion after being preprocessed, the hidden layer is composed of a plurality of fully connected layers, each layer comprises a certain number of neurons, a nonlinear characteristic is introduced by using an activation function, the neural network can gradually learn a complex pattern in the load data by extracting and converting the characteristics layer by layer, an output layer uses a Softmax activation function to convert the output of the neural network into probability distribution, so that the input data can be judged to which overload pattern the input data belongs, the output dimension corresponds to the category number of the overload pattern, and the output dimension corresponds to 3, and the low-frequency mode, the high-frequency mode and the low-frequency mixed mode respectively.
In this embodiment, the pattern recognition results, that is, the overload pattern includes a low frequency pattern, a high frequency pattern and a high and low frequency mixed pattern, the low frequency pattern means frequent overload in a plurality of consecutive periods (such as 3 times of overload in a week), the high frequency pattern means overload at a longer interval (such as 1 time per month but lasting half a year), the high and low frequency mixed pattern means that the overload behavior shows characteristics of both high frequency and low frequency in time, such as that there is a possibility that the vehicle is frequently overloaded in a certain period (such as 2-3 times per month), but the overload behavior is reduced in the next several months, only occasionally (such as 1 time every two, three months).
In this embodiment, the mode adjustment weight refers to an adjustment weight assigned to different overload levels of the vehicle according to the mode identification result, wherein weights respectively assigned to the low frequency mode, the high frequency mode, and the high-low frequency mixed modeAndThe range of the values of (a) is all,AndIs obtained by solving a matrix constructed by performing pairwise comparison and scoring by adopting an analytic hierarchy process, andThe target adjustment value is used for adjusting the determined reference overload score based on the calculation result of the difference value of the overload threshold value of the overload vehicle.
In this embodiment, for example, in a set period of time when there is an overloaded vehicle 1, there is only a historical overload with two overload levels, i.e. a slight overload and a moderate overload, where after the historical overload is divided, the slight overload includes a first historical overload combinationAndThe corresponding reference attenuation weights are respectivelyAndThe mode of slight overload adjusts the weight toThe medium overload includes a first historical overload combinationAnd a second historical overload combinationThe corresponding reference attenuation weights are respectivelyAndThe mode adjustment weight of the medium overload is as follows;
At this time, the target adjustment value of the overloaded vehicle 1。
The technical scheme comprises the following steps of firstly, obtaining historical load information of an overload vehicle, including the total number of historical overload, overload levels of each overload and overload time, calculating time intervals between each historical overload and the current time, namely reference time intervals, then, distributing time attenuation weights to the historical overload of different overload levels based on the reference time intervals so as to consider the influence of time factors on overload behaviors, then, dividing the historical overload of different overload levels into a first historical overload combination and a second historical overload combination according to comparison of the reference time intervals and set time length when the total number of the historical overload exceeds a preset threshold, then, analyzing the historical overload time of the overload levels by utilizing a pre-established pattern recognition model, recognizing a low-frequency, high-frequency or high-low-frequency mixed pattern, distributing pattern adjustment weights to each overload level according to pattern recognition results, and finally, determining a target adjustment value by combining the pattern adjustment weights and the reference attenuation weights, and adjusting corresponding reference overload scores.
The technical scheme has the advantages that through deep analysis of the historical overload records of the overload vehicle, time attenuation weights are distributed to the historical overload, reference attenuation weights of a first historical overload combination and a second historical overload combination which are obtained by corresponding division of different overload levels are determined, the historical overload modes of the different overload levels are identified by using a mode identification model, finally, mode adjustment weights given to the different overload levels according to an identification result output by the model are combined with the reference attenuation weights, and reference overload scores are adjusted, so that the overload level division is more accurate and flexible, and further, the follow-up early warning processing is accurate.
Finally, generating a statistical report from the early warning result, including:
integrating overload information, overload grade information and early warning processing information of the vehicle with basic information of the vehicle;
After the data integration is completed, a vehicle statistical report is generated, wherein the generated statistical report comprises basic information, overload event, overload processing result and safety risk relation of each vehicle;
the vehicle statistical report is presented in the form of a chart, a table, an image and a text report;
And automatically sending the generated vehicle statistical report to corresponding staff in a mail, short message and system notification mode.
Specifically, the overload information, the overload grade information and the early warning processing information of the vehicle are integrated with the basic information of the vehicle, so that the comprehensiveness and relevance of the data are ensured. This integration allows the staff to more easily understand the context and handling of overload events, and the generated statistical reports include basic information, overload events, overload handling results and security risk relationships for each vehicle, providing an overall overview of the overload conditions of the vehicle. The method is favorable for deep analysis and effective management of the overload problem of the vehicles by workers, and the presentation mode of the vehicle statistics report comprises charts, tables, images and text reports, so that the information receiving preference of different workers is met. The diversity enables the report to be easier to understand and use, improves the information transmission efficiency, automatically sends the generated vehicle statistics report to the corresponding staff through mails, short messages and system notification modes, reduces the complexity of manual operation and improves the working efficiency. The automatic sending also ensures the timeliness and accuracy of the report, avoids information delay or omission, provides abundant data and information support for staff, and is helpful for the staff to make more scientific and reasonable decisions. Through statistics and analysis of overload events, workers can identify trends and rules of overload problems, so that more effective management measures are formulated, and through timely early warning and processing of the overload events, the scheme is beneficial to reducing safety risks caused by overload of vehicles and improving safety of road traffic. The method has important significance for protecting the life and property safety of people.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made hereto without departing from the spirit and principles of the present invention.