Path planning method, system and medium for smart commercial concrete transport vehicles

Abstract

The present invention relates to the field of commercial concrete transportation path control, and specifically to a path planning method, system, and medium for intelligent commercial concrete transportation vehicles. The method first obtains the traffic volume and average speed of different sections from the commercial concrete prefabrication point to the construction site at different time periods of each day, as well as various road condition information of the sections. Based on the traffic volume and average speed of each section at the same time period on different days, the congestion level of each section in each time period is obtained. When driving to the current intersection, based on the congestion level and road condition information of each direct section in each time period within a preset time domain window at the current moment, combined with the amplitude and round-trip time of the ultrasonic echo signal of the concrete at the current moment, the preferred degree of each direct section is obtained, and then the best section is selected. When driving to the next intersection, the best section is selected again until the construction site is reached. The present invention can effectively balance the relationship between economy and timeliness in commercial concrete transportation and improve transportation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of commercial concrete transportation path control, and in particular to a path planning method, system and medium for smart commercial concrete transportation vehicles. Background Art

[0002] Ready-mixed concrete refers to commercial concrete composed of cement, aggregate, water, admixtures and other components in a certain proportion. It is an important construction material at construction sites. In order to ensure the cost quality of concrete and control construction costs, it is usually necessary to rationally plan the transportation routes of ready-mixed concrete transport vehicles to ensure construction progress and improve construction efficiency.

[0003] In the related art, the complete route from the commercial concrete prefabrication point to the construction point is usually determined in advance, and the transport vehicle is transported along the complete route. However, due to the long distance from the commercial concrete prefabrication point to the construction point, there are many uncertain factors on the road during the actual transportation process, which may affect the transportation timeliness. In addition, the solidification of concrete in the tank of the transport vehicle changes dynamically. When the solidification of concrete is not urgent, the route that prioritizes timeliness will lead to increased fuel consumption, thereby increasing economic costs. When the solidification of concrete is urgent, the route that prioritizes economy will lead to a decrease in concrete performance, resulting in the use of a predetermined fixed route, which makes it difficult to balance the relationship between economy and timeliness of transportation, thereby reducing the efficiency of commercial concrete transportation. Summary of the Invention

[0004] In order to solve the technical problem that it is difficult to balance the economic and time-efficiency aspects of transportation by using a predetermined fixed route, thereby reducing the efficiency of commercial concrete transportation, the purpose of the present invention is to provide a route planning method, system and medium for smart commercial concrete transportation vehicles. The technical solutions adopted are as follows:

[0005] The present invention proposes a path planning method for intelligent commercial concrete transport vehicles, the method comprising:

[0006] Obtain the traffic volume and average speed of different sections of road between the commercial concrete prefabrication site and the construction site at different times of the day, and obtain various road condition information for each section;

[0007] According to the traffic volume and average speed of each road section at the same time on different days, the congestion level of each road section in each time period is obtained;

[0008] The moment when the transport vehicle arrives at the current intersection is used as the current moment, and the direct sections of the current intersection are obtained. Based on the congestion level of each direct section in each time period within a preset time domain window at the current moment and the various road condition information, the predicted travel time and predicted fuel consumption of each direct section are obtained. The ultrasonic echo signal of the concrete in the tank of the transport vehicle at the current moment and its round-trip time are obtained, and the preference level of each direct section is obtained based on the predicted travel time and predicted fuel consumption of each direct section, as well as the amplitude and round-trip time of the ultrasonic echo signal at the current moment.

[0009] Based on the preference degree, the best road section is selected from all direct road sections, the transport vehicle is driven along the best road section to the next intersection and the best road section is selected until the transport vehicle reaches the construction site.

[0010] Furthermore, obtaining the congestion level of each road section in each time period includes:

[0011] Taking any road section as a target road section, performing negative correlation mapping on the average vehicle speed of the target road section in each time period of each day, and obtaining a first congestion performance value of the target road section in each time period of each day;

[0012] Performing negative correlation mapping on the traffic flow of the target road section in each time period of each day to obtain a second congestion performance value of the target road section in each time period of each day;

[0013] The first congestion performance value and the second congestion performance value of the target road section in each time period of each day are integrated and normalized to obtain a comprehensive congestion coefficient of the target road section in each time period of each day;

[0014] The average value of the comprehensive congestion coefficient of the target road section in the same time period of all days is used as the congestion level of the target road section in each time period.

[0015] Furthermore, the starting point of the direct section is the current intersection, and the end point of the direct section is the intersection that the transport vehicle has not passed through.

[0016] Furthermore, obtaining the predicted travel time and predicted fuel consumption of each direct road segment includes:

[0017] Using the historically collected information about the congestion level of a road section in each time period, all road condition information of the road section, and the travel time and fuel consumption of a transport vehicle when passing through the road section, a feature vector and an output label corresponding to the feature vector are constructed, wherein the elements of the feature vector are all road condition information of the road section and the congestion level of the road section in each time period, and the elements of the output label are the travel time and fuel consumption. The feature vectors and corresponding output labels of each road section are used to train a neural network to obtain a trained neural network.

[0018] Taking any direct road section as a target direct road section, and taking the average of the congestion levels of the target direct road section in all time periods within a preset time domain window at the current moment as the local congestion level of the target direct road section at the current moment;

[0019] The feature vector composed of the local congestion level of the target direct section at the current moment and all road condition information of the target direct section is input into the trained neural network, and the predicted travel time and predicted fuel consumption of the target direct section are output.

[0020] Furthermore, obtaining the preference level of each direct road segment includes:

[0021] Obtaining a time priority and a fuel consumption priority of the target direct segment based on a deviation of the predicted travel time of the target direct segment relative to the overall level of the predicted travel time of all direct segments, and a deviation of the predicted fuel consumption of the target direct segment relative to the overall level of the predicted fuel consumption of all direct segments;

[0022] Obtaining the ultrasonic echo signal and round-trip time of the concrete in the transport vehicle tank at the commercial concrete prefabrication point, and obtaining the degree of setting of the concrete at the current moment based on the difference in average amplitude between the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point, as well as the difference in round-trip time between the ultrasonic echo signal at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point;

[0023] The preference degree of the target direct road segment is obtained based on the calculation formula of the preference degree, and the calculation formula of the preference degree is:

[0024]

[0025] in, Indicates the preference of the target direct road segment; Indicates the degree of setting of concrete at the current moment; Indicates the time priority of the target direct road segment; Indicates the fuel consumption priority of the target direct road section.

[0026] Furthermore, obtaining the time priority and fuel consumption priority of the target direct road segment includes:

[0027] The average of the predicted travel times of all direct road segments at the current intersection is used as the overall predicted travel time;

[0028] The predicted travel time of the target direct road section is used as the numerator, the overall predicted travel time is used as the denominator, and the comparison value is normalized by negative correlation to obtain the time priority of the target direct road section;

[0029] taking the average of the predicted fuel consumption of all direct road sections of the current intersection as the overall predicted fuel consumption;

[0030] The predicted fuel consumption of the target direct section is used as the numerator, the overall predicted fuel consumption is used as the denominator, and a negative correlation normalization process is performed on the comparison value to obtain the fuel consumption priority of the target direct section.

[0031] Furthermore, obtaining the degree of coagulation of concrete at the current moment includes:

[0032] The absolute value of the difference between the average amplitude of the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point is used as the first setting performance value of the concrete in the transport vehicle tank at the current moment;

[0033] The absolute value of the difference between the round-trip time of the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point is used as the second setting performance value of the concrete in the transport vehicle tank at the current moment;

[0034] The first coagulation performance value and the second coagulation performance value are integrated and normalized to obtain the coagulation degree of the concrete in the tank of the transport vehicle at the current moment.

[0035] Furthermore, the selecting the best road segment from all direct road segments based on the preference degree includes:

[0036] The direct section corresponding to the maximum value of the preference degree is taken as the optimal section.

[0037] The present invention also proposes a path planning system for intelligent commercial concrete transport vehicles, the system comprising:

[0038] The data acquisition module is used to obtain the traffic volume and average speed of different sections of road between the commercial concrete prefabrication point and the construction site at different times of the day, and obtain various road condition information for each section;

[0039] The road congestion analysis module is used to obtain the congestion level of each road section at each time period based on the traffic volume and average speed of each road section at the same time period on different days;

[0040] a priority analysis module configured to determine the time at which the transport vehicle arrives at the current intersection as the current time, obtain direct road sections at the current intersection, and obtain a predicted travel time and predicted fuel consumption for each direct road section based on the congestion level of each direct road section at each time period within a preset time domain window at the current time and the plurality of road condition information; obtain an ultrasonic echo signal of the concrete within the transport vehicle tank at the current time and its round-trip time, and determine the priority of each direct road section based on the predicted travel time and predicted fuel consumption of each direct road section, as well as the amplitude and round-trip time of the ultrasonic echo signal at the current time;

[0041] The road segment selection module is used to select the best road segment from all direct road segments based on the preference degree, drive the transport vehicle along the best road segment to the next intersection and select the best road segment until the transport vehicle reaches the construction site.

[0042] The present invention also proposes a computer medium, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements any one of the steps of a path planning method for smart commercial concrete transport vehicles.

[0043] The present invention has the following beneficial effects:

[0044] The present invention takes into account that it is difficult to balance the relationship between the economy and timeliness of transportation by adopting a predetermined fixed route, thereby reducing the efficiency of commercial concrete transportation. Therefore, the traffic volume and average speed of different sections between the commercial concrete prefabrication point and the construction point at different time periods of the day are first obtained, and a variety of road condition information of each section is obtained. Considering that the congestion level of the same section changes with time with a daily periodicity, and the congestion level of each section is directly related to the traffic volume and average speed, the road congestion situation of each section at different time periods can be reflected by the congestion level, and the travel time of the transport vehicle on each direct section of the current intersection can be further predicted. and fuel consumption, and taking into account that as the concrete in the tank solidifies, the amplitude and round-trip time of the ultrasonic echo signal will decrease, it is possible to further balance the economic and time-efficiency aspects of commercial concrete transportation based on the amplitude and round-trip time of the ultrasonic echo signal at the current moment, combined with the predicted travel time and predicted fuel consumption of each direct section, thereby obtaining the degree of preference of each direct section, and based on the degree of preference, selecting the best section, driving the transport vehicle along the best section to the next intersection and selecting the best section until the transport vehicle arrives at the construction site, thereby realizing dynamic selection of sections during transportation and improving the final commercial concrete transportation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 A flow chart of a path planning method for a smart commercial concrete transport vehicle provided by one embodiment of the present invention;

[0047] Figure 2 A schematic diagram of the location distribution of multiple road sections from a commercial concrete prefabrication point to a construction site provided by one embodiment of the present invention;

[0048] Figure 3 A block diagram of a path planning system for intelligent commercial concrete transport vehicles provided by one embodiment of the present invention;

[0049] Figure 4 A schematic diagram of computer media distribution provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to further illustrate the technical means and effects adopted by the present invention to achieve the predetermined purpose of the invention, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail a path planning method, system and medium for intelligent commercial concrete transport vehicles proposed in accordance with the present invention, its specific implementation method, structure, features and effects. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics of one or more embodiments may be combined in any suitable form.

[0051] 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.

[0052] The following describes in detail a specific solution of a path planning method, system and medium for smart commercial concrete transport vehicles provided by the present invention in conjunction with the accompanying drawings.

[0053] See also Figure 1 , which shows a flow chart of a path planning method for a smart commercial concrete transport vehicle provided by one embodiment of the present invention, the method comprising:

[0054] Step S1: Obtain the traffic volume and average speed of different sections between the commercial concrete prefabrication point and the construction site at different time periods of each day, and obtain various road condition information for each section.

[0055] Ready-mixed concrete, that is, commercial concrete, is prepared at the prefabrication point and then transported to the construction site by a transport vehicle. Therefore, the prefabrication point can be used as the starting point and the construction site as the end point.

[0056] An embodiment of the present invention first obtains the traffic volume and average speed of different sections between the commercial concrete prefabrication point and the construction point at different time periods of each day within a preset historical time period from the road traffic management big data system, and obtains a variety of road condition information for each section, wherein the road condition information includes information such as the length, width, and slope of each section. The preset historical time period is set to 30 days, and the length of a single period of each day is usually 10 to 20 minutes. In one embodiment of the present invention, the length of a single period is set to 15 minutes. The specific values ​​of the preset historical time period and the length of a single period can also be set by the implementer according to the specific implementation scenario, and are not limited here.

[0057] See also Figure 2 , which shows a schematic diagram of the location distribution of multiple road sections from a commercial concrete prefabrication point to a construction point provided by an embodiment of the present invention, wherein, for example, ab is a road section, fe is a road section, and a, b, f, and e are intersections respectively.

[0058] At the same time, the embodiment of the present invention also requires installing an ultrasonic sensor on the tank surface of the commercial concrete transport vehicle so that when the transport vehicle reaches an intersection during the subsequent transportation process, the ultrasonic sensor can be used to transmit and receive ultrasonic signals to the concrete in the tank.

[0059] It should be noted that when the transport vehicle just departs from the commercial concrete prefabrication plant, it is necessary to use an ultrasonic sensor to transmit an ultrasonic signal into the tank body and receive the ultrasonic echo signal, and at the same time record the time interval from the transmission to the reception of the signal, that is, the round-trip time of the ultrasonic echo signal.

[0060] Step S2: According to the traffic volume and average speed of each road section at the same time period on different days, the congestion level of each road section at each time period is obtained.

[0061] During the transportation process, commercial concrete transport vehicles generally need to consider two main factors. One is the urgency of transportation time, because concrete is prone to solidification when traveling for too long, resulting in a decline in concrete quality; the other is the economy of transportation. Because commercial concrete transport vehicles generally have heavy loads, high fuel consumption, and serious pollution emissions, drivers are more inclined to save transportation costs and reduce environmental pollution by reducing fuel consumption. Therefore, when selecting the optimal road section for transport vehicles, on the one hand, it is necessary to consider the travel time of the transport vehicle caused by the congestion of the road section, and on the other hand, it is also necessary to consider the fuel consumption performance of the transport vehicle when traveling on the road section.

[0062] At the same time, taking into account that the congestion level of the same road section changes with time with a daily periodicity, and the congestion level of each road section is directly related to the traffic flow and average speed, the embodiment of the present invention first analyzes the traffic flow and average speed of each road section at the same time period on different days, and reflects the road congestion level of each road section at different time periods by obtaining the congestion level. Subsequently, based on the congestion level of different road sections at each time period, the travel time and fuel consumption of the transport vehicle on the road section can be accurately predicted.

[0063] Preferably, in one embodiment of the present invention, the method for obtaining the congestion level of each road section in each time period specifically includes:

[0064] First, any road section is taken as the target section. The lower the average speed of vehicles on the target section, the more congested the target section is. Therefore, the average speed of the target section in each time period of the day can be negatively correlated to obtain the first congestion performance value of the target section in each time period of the day.

[0065] The smaller the traffic volume on the target road section, the more congested the target road section is. Therefore, the traffic volume of the target road section in each time period of the day can be negatively correlated to obtain the second congestion performance value of the target road section in each time period of the day.

[0066] Then the first congestion performance value and the second congestion performance value of the target section in each time period of each day are combined and normalized, and the calculation results are limited to range, thereby obtaining the comprehensive congestion coefficient of the target road section in each time period of the day.

[0067] In the embodiment of the present invention, the integration of the first congestion performance value and the second congestion performance value of the target road section in each time period of each day can be achieved by calculating the sum or product of the two values, which is not limited here.

[0068] In one embodiment of the present invention, the normalization processing can be specifically, for example, maximum and minimum value normalization processing, and the normalization in subsequent steps can all adopt maximum and minimum value normalization processing. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of values, or activation functions and hyperbolic tangent functions can be used to implement normalization processing, which will not be repeated or limited.

[0069] Then, the average value of the comprehensive congestion coefficient of the target road section in the same time period of all days is taken as the congestion level of the target road section in each time period.

[0070] As an example, in one embodiment of the present invention, the expression for the congestion level of the target road section in each time period may be specifically, for example, as follows:

[0071]

[0072]

[0073] in, Indicates that the target road segment is The degree of congestion during each time period; Indicates that the target road segment is The first in the sky The comprehensive congestion coefficient of each time period; Indicates the number of days included in the preset historical time period; Indicates that the target road segment is The first in the sky Average vehicle speed during a period of time; Indicates that the target road segment is The first in the sky The first crowding performance value of each period; Indicates that the target road segment is The first in the sky Traffic volume during each period; Indicates that the target road segment is The first in the sky The second crowding performance value of the period; and Respectively represent the preset first adjustment parameter and the preset second adjustment parameter, which are used to prevent the denominator from being 0. and The value range is In one embodiment of the present invention, and are all set to 0.01, and The specific value of can also be set by the implementer according to the specific implementation scenario and is not limited here.

[0074] The congestion level of each road section in each time period can be obtained by the same method as above.

[0075] Step S3: Take the moment when the transport vehicle travels to the current intersection as the current moment, obtain the direct section of the current intersection, and obtain the predicted travel time and predicted fuel consumption of each direct section based on the congestion level of each direct section in each time period within the preset time domain window at the current moment and various road condition information; obtain the ultrasonic echo signal of the concrete in the tank of the transport vehicle at the current moment and its round-trip time, and obtain the preference degree of each direct section based on the predicted travel time and predicted fuel consumption of each direct section, as well as the amplitude and round-trip time of the ultrasonic echo signal at the current moment.

[0076] During the actual transportation process, commercial concrete transport trucks need to choose to travel on different road sections until they reach the construction site. When the transport truck enters another section from one section, it will pass through the intersection between the two sections. Since intersections usually connect multiple sections, the congestion conditions of different sections are different, and the possible travel time and fuel consumption of commercial concrete transport trucks on different sections are also different. Therefore, when the commercial concrete transport truck arrives at the intersection, it is necessary to select the best section to pass through in order to ensure the efficiency of commercial concrete transportation. Therefore, in the actual transportation process, the moment when the transport truck arrives at the current intersection is first taken as the current moment, and the direct section of the current intersection is obtained. Subsequently, the travel time and fuel consumption of each direct section can be predicted, and the best section can be selected for passage.

[0077] Preferably, in one embodiment of the present invention, the direct sections of the current intersection all start from the current intersection. At the same time, in order to avoid the transport vehicle going back, the end point of the selected direct section is the intersection that the transport vehicle has not passed.

[0078] Since the efficiency of commercial concrete transportation mainly lies in two aspects: transportation time and economic cost. The transportation time mainly lies in the travel time of the transport vehicle on each road section, and the economic cost of transportation mainly lies in the fuel consumption during the transportation process. The travel time and fuel consumption of the transport vehicle on the road section are not only related to the congestion level of the road section, but also related to the road condition information of the road section. At the same time, considering that the congestion level of the road section has a daily periodicity and will not change significantly in a short time, the travel time and fuel consumption of the transport vehicle on each direct section of the current intersection are accurately predicted based on the congestion level of each time period within the preset time domain window at the current moment and a variety of road condition information. Subsequently, the preference degree of each direct section can be analyzed based on the predicted travel time and predicted fuel consumption of each direct section.

[0079] Preferably, in one embodiment of the present invention, the method for obtaining the predicted travel time and predicted fuel consumption of each direct road segment specifically includes:

[0080] Using the road traffic management big data system, historical data such as the congestion level of different road sections in each time period, all road condition information of the road section, and the travel time and fuel consumption of transport vehicles when passing through the road section are collected, and feature vectors and output labels corresponding to the feature vectors are constructed as training sets, wherein the elements in the feature vector are all road condition information of the road section and the congestion level of the road section in each time period, and the elements in the output label are the travel time and fuel consumption. The form of the feature vector can be, for example, (congestion level, road section width, road section length, road section slope...), and the form of the output label can be, for example, (travel time, fuel consumption), and the feature vectors and corresponding output labels of each road section are used to train the neural network to obtain a trained neural network, wherein the CNN architecture can be selected as the basic framework of the neural network, and the loss function can be the cross entropy function.

[0081] Take any direct road section as the target direct road section. Since the transport vehicle needs to continue to pass through the direct road section for a period of time, in order to accurately predict the actual congestion situation of the target direct road section, the average congestion level of the target direct road section in all time periods within the preset time domain window at the current moment can be used as the local congestion level of the target direct road section at the current moment. The length of the preset time domain window is set to 3, that is, the preset time domain window includes the time period at the current moment and the two time periods closest to the time period at the current moment. The specific length of the preset time domain window can also be set by the implementer according to the specific implementation scenario, and is not limited here.

[0082] Then, the feature vector composed of the local congestion level of the target direct section at the current moment and all the road condition information of the target direct section can be input into the trained neural network, and the predicted travel time and predicted fuel consumption of the target direct section can be output.

[0083] The same method as above can be used to obtain the predicted travel time and fuel consumption of each direct section of the current intersection.

[0084] After predicting the travel time and fuel consumption of each direct section, it is also necessary to combine the coagulation of concrete in the tank of the transport vehicle to determine whether to focus on the principle of economy and reduce fuel consumption, or to focus on time factors and prevent concrete coagulation. Considering that the degree of coagulation of concrete in the tank of the transport vehicle increases over time, and as the degree of coagulation of concrete increases, the intensity of the ultrasonic signal attenuates more seriously in the concrete, the amplitude is relatively lower, and the propagation speed of the ultrasonic signal in the concrete is faster, and the round-trip time is relatively shorter. Therefore, when the transport vehicle arrives at the current intersection, the ultrasonic sensor is used to transmit an ultrasonic signal to the inside of the tank, and the ultrasonic echo signal is received, and the round-trip time of the ultrasonic echo signal is recorded at the same time. Based on the amplitude and round-trip time of the ultrasonic echo signal at the current moment, and combined with the predicted travel time and predicted fuel consumption of each direct section, the relationship between the economy and timeliness of commercial concrete transportation is effectively balanced, thereby obtaining the preferred degree of each direct section.

[0085] Preferably, in one embodiment of the present invention, the method for obtaining the preference level of each direct road segment specifically includes:

[0086] First, based on the deviation of the predicted travel time of the target direct section relative to the overall level of the predicted travel time of all direct sections, and the deviation of the predicted fuel consumption of the target direct section relative to the overall level of the predicted fuel consumption of all direct sections, the time priority and fuel consumption priority of the target direct section are obtained; the greater the time priority of the target direct section, the shorter the travel time of the transport vehicle on the target direct section relative to other direct sections, and the greater the fuel consumption priority of the target direct section, the less fuel consumption of the transport vehicle on the target direct section relative to other direct sections.

[0087] Preferably, in one embodiment of the present invention, the method for obtaining the time priority and fuel consumption priority of the target direct road segment specifically includes:

[0088] The average of the predicted travel times of all direct sections of the current intersection is used as the overall predicted travel time, and the overall predicted travel time is used to reflect the overall level of the predicted travel times of all direct sections.

[0089] The predicted travel time of the target direct section is used as the numerator, the overall predicted travel time is used as the denominator, and the comparison value is normalized to negative correlation. The calculation result is limited to range, thereby obtaining the time priority of the target direct section.

[0090] The average value of the predicted fuel consumption of all direct sections of the current intersection is used as the overall predicted fuel consumption, and the overall predicted fuel consumption is used to reflect the overall level of the predicted fuel consumption of all direct sections.

[0091] The predicted fuel consumption of the target direct section is used as the numerator, the overall predicted fuel consumption is used as the denominator, the comparison value is normalized for negative correlation, and the calculation result is limited to range, thereby obtaining the fuel consumption priority of the target direct road section.

[0092] In one embodiment of the present invention, the The function form realizes the normalization of negative correlation, where Represents the normalization function.

[0093] In other embodiments of the present invention, the difference between the predicted travel time of the target direct section and the overall predicted travel time can also be normalized with negative correlation to obtain the time priority of the target direct section, and the difference between the predicted fuel consumption of the target direct section and the overall predicted fuel consumption can be normalized with negative correlation to obtain the fuel consumption priority of the target direct section, which is not limited here.

[0094] As an example, in one embodiment of the present invention, the expressions for the time priority and fuel consumption priority of the target direct road segment may be specifically as follows:

[0095]

[0096]

[0097] in, Indicates the time priority of the target direct road segment; Indicates the fuel consumption priority of the target direct road section; Indicates the predicted travel time of the target direct road segment; Indicates the overall predicted travel time; represents the predicted fuel consumption of the target direct road section; Indicates the overall predicted fuel consumption; Represents the normalization function.

[0098] Then, the ultrasonic echo signal and round-trip time of the concrete in the tank of the transport vehicle at the commercial concrete prefabrication point are obtained. Since the concrete gradually solidifies over time, the amplitude and round-trip time of the ultrasonic echo signal at the current moment are smaller than those of the concrete at the commercial concrete prefabrication point, and the difference is greater. Therefore, the degree of solidification of the concrete at the current moment can be obtained based on the difference in average amplitude between the ultrasonic echo signal of the concrete in the tank of the transport vehicle at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point, as well as the difference in round-trip time between the ultrasonic echo signal at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point. It should be noted that the average amplitude of the ultrasonic echo signal can be calculated by integration or by averaging the amplitudes of multiple sampling points on the signal. This is a technical means well known to those skilled in the art and is not limited here.

[0099] Preferably, in one embodiment of the present invention, the method for obtaining the degree of setting of concrete at the current moment specifically includes:

[0100] The absolute value of the difference between the average amplitude of the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point is used as the first setting performance value of the concrete in the transport vehicle tank at the current moment.

[0101] The absolute value of the difference between the round-trip time of the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point is used as the second setting performance value of the concrete in the transport vehicle tank at the current moment.

[0102] After the first coagulation performance value and the second coagulation performance value are integrated and normalized, the calculation result is limited to range, thereby obtaining the degree of coagulation of concrete in the tank of the transport vehicle at the current moment.

[0103] In the embodiment of the present invention, the combination of the first coagulation performance value and the second coagulation performance value may be achieved by calculating the sum or product of the two values, which is not limited herein.

[0104] As an example, in one embodiment of the present invention, the expression for the setting degree of concrete at the current moment may be specifically, for example, as follows:

[0105]

[0106] in, Indicates the degree of setting of concrete at the current moment; Indicates the average amplitude of the ultrasonic echo signal of the concrete in the tank of the transport vehicle at the current moment; It represents the average amplitude of the ultrasonic echo signal when the concrete in the tank of the transport vehicle is at the commercial concrete prefabrication point; Indicates the first setting performance value of the concrete in the tank of the transport vehicle at the current moment; Indicates the round-trip time of the ultrasonic echo signal of the concrete in the tank of the transport vehicle at the current moment; Indicates the round trip time of the ultrasonic echo signal when the concrete in the transport vehicle tank is at the commercial concrete prefabrication point; Indicates the second setting performance value of the concrete in the tank of the transport vehicle at the current moment; Represents the normalization function.

[0107] When the degree of concrete solidification at the current moment is greater, the road section should be selected based on time priority to prevent the concrete from solidifying too heavily and ensure transportation timeliness. When the degree of concrete solidification at the current moment is smaller, the road section should be selected based on fuel consumption priority to prevent high fuel consumption and ensure the economic cost of transportation. Therefore, the preferred degree of the target direct road section can be obtained based on the calculation formula of the preferred degree. The calculation formula of the preferred degree is:

[0108]

[0109] in, Indicates the preference of the target direct road segment; Indicates the degree of setting of concrete at the current moment; Indicates the time priority of the target direct road segment; Indicates the fuel consumption priority of the target direct road section.

[0110] The preference level of each direct road segment at the current intersection can be obtained by the same method as above.

[0111] Step S4: Based on the degree of preference, the best section is selected from all direct sections, and the transport vehicle is driven along the best section to the next intersection and the best section is selected until the transport vehicle reaches the construction site.

[0112] The greater the preference degree of a direct road section, the more the transport vehicles at the current intersection need to give priority to passing through this direct road section. Therefore, based on the preference degree, the best road section can be selected from all direct road sections to improve the efficiency of commercial concrete transportation.

[0113] Preferably, in one embodiment of the present invention, the direct section corresponding to the maximum value of the preference degree can be used as the optimal section, and then the transport vehicle can pass along the optimal section. When it reaches the next intersection, the optimal section can be selected again by the same method mentioned above until the transport vehicle reaches the construction site and completes the commercial concrete transportation.

[0114] One embodiment of the present invention provides a path planning system for intelligent commercial concrete transport vehicles. Figure 3 , which shows a flow chart of a path planning method for smart commercial concrete transport vehicles provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a road congestion analysis module 102, a priority analysis module 103 and a road section selection module 104.

[0115] The data acquisition module is used to obtain the traffic volume and average speed of different sections between the commercial concrete prefabrication point and the construction point at different times of the day, and to obtain various road condition information for each section.

[0116] The road congestion analysis module is used to obtain the congestion level of each road section in each time period based on the traffic volume and average speed of each road section in the same time period on different days.

[0117] The priority analysis module is used to take the moment when the transport vehicle travels to the current intersection as the current moment, obtain the direct sections of the current intersection, and obtain the predicted travel time and predicted fuel consumption of each direct section based on the congestion level of each direct section in each time period within the preset time domain window at the current moment and various road condition information; obtain the ultrasonic echo signal of the concrete in the tank of the transport vehicle at the current moment and its round-trip time, and obtain the priority degree of each direct section based on the predicted travel time and predicted fuel consumption of each direct section, as well as the amplitude and round-trip time of the ultrasonic echo signal at the current moment.

[0118] The road section selection module is used to select the best road section from all direct road sections based on the degree of preference, drive the transport vehicle along the best road section to the next intersection and select the best road section until the transport vehicle arrives at the construction site.

[0119] One embodiment of the present invention provides a computer medium, see Figure 4 , which shows a schematic diagram of the distribution of a computer medium provided by an embodiment of the present invention, wherein the medium includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the method described in steps S1 to S4 can be implemented.

[0120] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0121] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A path planning method for intelligent commercial concrete transport vehicles, characterized in that: The method comprises: Obtain the traffic volume and average speed of different sections of road between the commercial concrete prefabrication site and the construction site at different times of the day, and obtain various road condition information for each section; According to the traffic volume and average speed of each road section at the same time on different days, the congestion level of each road section in each time period is obtained; The moment when the transport vehicle arrives at the current intersection is used as the current moment, and the direct sections of the current intersection are obtained. Based on the congestion level of each direct section in each time period within a preset time domain window at the current moment and the various road condition information, the predicted travel time and predicted fuel consumption of each direct section are obtained. The ultrasonic echo signal of the concrete in the tank of the transport vehicle at the current moment and its round-trip time are obtained, and the preference level of each direct section is obtained based on the predicted travel time and predicted fuel consumption of each direct section, as well as the amplitude and round-trip time of the ultrasonic echo signal at the current moment. Based on the preference degree, selecting the best road segment from all direct road segments, driving the transport vehicle along the best road segment to the next intersection and continuing to select the best road segment until the transport vehicle reaches the construction site; The preference degree of the target direct road segment satisfies the following formula: in, Indicates the preference of the target direct road segment, Indicates the degree of coagulation of concrete at the current moment, Indicates the time priority of the target direct link, Indicates the fuel consumption priority of the target direct section; the degree of coagulation is determined based on the difference in average amplitude between the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point, as well as the difference in round-trip time between the ultrasonic echo signal at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point; the time priority and fuel consumption priority are determined based on the deviation of the predicted travel time of the target direct section relative to the overall level of the predicted travel time of all direct sections, and the deviation of the predicted fuel consumption of the target direct section relative to the overall level of the predicted fuel consumption of all direct sections, respectively.

2. A path planning method for intelligent commercial concrete transport vehicles according to claim 1, characterized in that: Obtaining the congestion level of each road section in each time period includes: Taking any road section as a target road section, performing negative correlation mapping on the average vehicle speed of the target road section in each time period of each day, and obtaining a first congestion performance value of the target road section in each time period of each day; Performing negative correlation mapping on the traffic flow of the target road section in each time period of each day to obtain a second congestion performance value of the target road section in each time period of each day; The first congestion performance value and the second congestion performance value of the target road section in each time period of each day are integrated and normalized to obtain a comprehensive congestion coefficient of the target road section in each time period of each day; The average value of the comprehensive congestion coefficient of the target road section in the same time period of all days is used as the congestion level of the target road section in each time period.

3. A path planning method for intelligent commercial concrete transport vehicles according to claim 1, characterized in that: The starting point of the direct section is the current intersection, and the end point of the direct section is the intersection that the transport vehicle has not passed through.

4. A path planning method for intelligent commercial concrete transport vehicles according to claim 1, characterized in that: The method of obtaining the predicted travel time and fuel consumption of each direct road segment includes: Using the historically collected information about the congestion level of a road section in each time period, all road condition information of the road section, and the travel time and fuel consumption of a transport vehicle when passing through the road section, a feature vector and an output label corresponding to the feature vector are constructed, wherein the elements of the feature vector are all road condition information of the road section and the congestion level of the road section in each time period, and the elements of the output label are the travel time and fuel consumption. The feature vectors and corresponding output labels of each road section are used to train a neural network to obtain a trained neural network. Taking any direct road section as a target direct road section, and taking the average of the congestion levels of the target direct road section in all time periods within a preset time domain window at the current moment as the local congestion level of the target direct road section at the current moment; The feature vector composed of the local congestion level of the target direct section at the current moment and all road condition information of the target direct section is input into the trained neural network, and the predicted travel time and predicted fuel consumption of the target direct section are output.

5. The path planning method for intelligent commercial concrete transport vehicles according to claim 1 is characterized in that: The time priority and fuel consumption priority for obtaining the target direct road segment include: The average of the predicted travel times of all direct road segments at the current intersection is used as the overall predicted travel time; The predicted travel time of the target direct road section is used as the numerator, the overall predicted travel time is used as the denominator, and the comparison value is normalized by negative correlation to obtain the time priority of the target direct road section; taking the average of the predicted fuel consumption of all direct road sections of the current intersection as the overall predicted fuel consumption; The predicted fuel consumption of the target direct section is used as the numerator, the overall predicted fuel consumption is used as the denominator, and a negative correlation normalization process is performed on the comparison value to obtain the fuel consumption priority of the target direct section.

6. A path planning method for intelligent commercial concrete transport vehicles according to claim 1, characterized in that: Obtaining the degree of concrete setting at the current moment includes: The absolute value of the difference between the average amplitude of the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point is used as the first setting performance value of the concrete in the transport vehicle tank at the current moment; The absolute value of the difference between the round-trip time of the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point is used as the second setting performance value of the concrete in the transport vehicle tank at the current moment; The first coagulation performance value and the second coagulation performance value are integrated and normalized to obtain the coagulation degree of the concrete in the tank of the transport vehicle at the current moment.

7. The path planning method for intelligent commercial concrete transport vehicles according to claim 1 is characterized in that: The selecting the best segment from all direct segments based on the preference degree comprises: The direct section corresponding to the maximum value of the preference degree is taken as the optimal section.

8. A path planning system for intelligent commercial concrete transport vehicles, the system comprising: The data acquisition module is used to obtain the traffic volume and average speed of different sections of road between the commercial concrete prefabrication point and the construction site at different times of the day, and obtain various road condition information for each section; The road congestion analysis module is used to obtain the congestion level of each road section at each time period based on the traffic volume and average speed of each road section at the same time period on different days; a priority analysis module configured to determine the time at which the transport vehicle arrives at the current intersection as the current time, obtain direct road sections at the current intersection, and obtain a predicted travel time and predicted fuel consumption for each direct road section based on the congestion level of each direct road section at each time period within a preset time domain window at the current time and the plurality of road condition information; obtain an ultrasonic echo signal of the concrete within the transport vehicle tank at the current time and its round-trip time, and determine the priority of each direct road section based on the predicted travel time and predicted fuel consumption of each direct road section, as well as the amplitude and round-trip time of the ultrasonic echo signal at the current time; a road segment selection module, configured to select an optimal road segment from all direct road segments based on the degree of preference, drive the transport vehicle along the optimal road segment to the next intersection and continue selecting the optimal road segment until the transport vehicle arrives at the construction site; The preference degree of the target direct road segment satisfies the following formula: in, Indicates the preference of the target direct road segment, Indicates the degree of coagulation of concrete at the current moment, Indicates the time priority of the target direct link, Indicates the fuel consumption priority of the target direct section; the degree of coagulation is determined based on the difference in average amplitude between the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point, as well as the difference in round-trip time between the ultrasonic echo signal at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point; the time priority and fuel consumption priority are determined based on the deviation of the predicted travel time of the target direct section relative to the overall level of the predicted travel time of all direct sections, and the deviation of the predicted fuel consumption of the target direct section relative to the overall level of the predicted fuel consumption of all direct sections, respectively.

9. A computer medium comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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