Path planning method and system for intelligent commercial concrete transport vehicle, and medium

Abstract

The invention relates to the field of commercial concrete transportation path control, in particular to a path planning method and system for an intelligent commercial concrete transportation vehicle and a medium. The method comprises the following steps: firstly, acquiring traffic flow and average vehicle speed of different road sections from a commercial concrete prefabricating point to a construction point in different time periods of each day and various road condition information of the road sections, and acquiring the crowding degree of each road section in each time period according to the traffic flow and the average vehicle speed of each road section in the same time period of different days; when the vehicle is driven to the current intersection, the optimal degree of each direct road section is obtained according to the crowding degree and the road condition information of each direct road section in each time period in a preset time domain window of the current moment and in combination with the amplitude and the round-trip duration of an ultrasonic echo signal of concrete at the current moment, and then the optimal road section is selected; and when the vehicle runs to the next intersection, the optimal road section is selected again until the vehicle arrives at the construction point. The method can effectively balance the relation between economy and timeliness of commercial concrete transportation and improve the transportation efficiency.

Description

Technical Field

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

[0002] Commercial concrete refers to commercial concrete composed of components such as cement, aggregates, water, and admixtures in a certain proportion, which 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 reasonably plan the transportation path of commercial concrete transportation vehicles to ensure the construction progress and improve construction efficiency.

[0003] In related technologies, the complete path from the commercial concrete prefabrication point to the construction point is usually determined in advance, and the transport vehicle is transported along this complete path. 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. Moreover, the setting condition of the concrete in the transport vehicle tank is dynamically changing. When the setting of the concrete is not urgent, the path prioritizing timeliness will lead to increased fuel consumption, thus increasing the economic cost. When the setting of the concrete is urgent, the path prioritizing economy will lead to a decline in the performance of the concrete. Therefore, it is difficult to balance the relationship between economy and timeliness in transportation by using the pre-determined fixed path, resulting in a reduction in the transportation efficiency of commercial concrete. Summary of the Invention

[0004] In order to solve the technical problem that it is difficult to balance the relationship between economy and timeliness in transportation by using the pre-determined fixed path, resulting in a reduction in the transportation efficiency of commercial concrete, the purpose of the present invention is to provide a path planning method, system and medium for intelligent commercial concrete transportation vehicles. The specific technical solutions adopted are as follows: The present invention proposes a path planning method for intelligent commercial concrete transportation vehicles, and the method includes: Obtain the traffic flow and average vehicle speed of different road sections between the commercial concrete prefabrication point and the construction point at different times of each day, and obtain various road condition information of each road section; Obtain the congestion degree of each road section at each time period according to the traffic flow and average vehicle speed of each road section at the same time period on different days; Take the moment when the transport vehicle arrives at the current intersection as the current moment, obtain the direct road sections of the current intersection, and based on the congestion levels of each direct road section in each time period within the preset time domain window at the current moment and various traffic condition information, obtain the predicted travel time and predicted fuel consumption of each direct road section; obtain the ultrasonic echo signal and its round-trip duration of the concrete in the transport vehicle tank at the current moment, and based on the predicted travel time and predicted fuel consumption of each direct road section, as well as the amplitude and round-trip duration of the ultrasonic echo signal at the current moment, obtain the preference degree of each direct road section; Based on the preference degree, select the best road section from all the direct road sections, drive the transport vehicle along the best road section to the next intersection and perform the selection of the best road section until the transport vehicle reaches the construction site.

[0005] Further, obtaining the congestion level of each road section in each time period includes: Take any road section as the target road section, perform a negative correlation mapping on the average vehicle speed of the target road section in each time period of each day to obtain the first congestion performance value of the target road section in each time period of each day; Perform a negative correlation mapping on the traffic flow of the target road section in each time period of each day to obtain the second congestion performance value of the target road section in each time period of each day; After comprehensively processing the first congestion performance value and the second congestion performance value of the target road section in each time period of each day and performing normalization processing, obtain the comprehensive congestion coefficient of the target road section in each time period of each day; Take the average value of the comprehensive congestion coefficients of the target road section in the same time periods of all days as the congestion level of the target road section in each time period.

[0006] Further, the starting point of the direct road section is the current intersection, and the end point of the direct road section is an intersection that the transport vehicle has not passed through.

[0007] Further, obtaining the predicted travel time and predicted fuel consumption of each direct road section includes: Use the congestion levels of the road sections in each time period, all traffic condition information of the road sections, the travel time and fuel consumption of the transport vehicle when passing through the road sections collected historically to construct feature vectors and the output labels corresponding to the feature vectors. The elements in the feature vectors are all traffic condition information of the road sections and the congestion levels of the road sections in each time period, and the elements in the output labels are travel time and fuel consumption. Then use the feature vectors and corresponding output labels of each road section to train the neural network to obtain a trained neural network; Take any direct road segment as the target direct road segment, and use the average value of the congestion levels of all time periods within the preset time domain window of the target direct road segment at the current moment as the local congestion level of the target direct road segment at the current moment; Input the feature vector composed of the local congestion level of the target direct road segment at the current moment and all road condition information of the target direct road segment into the trained neural network, and output the predicted travel time and predicted fuel consumption of the target direct road segment.

[0008] Further, the obtaining of the preference degree of each direct road segment includes: Obtain the time priority and fuel consumption priority of the target direct road segment according to the deviation of the predicted travel time of the target direct road segment from the overall level of the predicted travel times of all direct road segments, and the deviation of the predicted fuel consumption of the target direct road segment from the overall level of the predicted fuel consumptions of all direct road segments; Obtain the ultrasonic echo signal and its round-trip duration of the concrete in the transport vehicle tank at the commercial concrete prefabrication point. According to the difference in the 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, and the difference in the round-trip duration between the ultrasonic echo signal at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point, obtain the setting degree of the concrete at the current moment; Based on the calculation formula of the preference degree, obtain the preference degree of the target direct road segment. The calculation formula of the preference degree is: Wherein, represents the preference degree of the target direct road segment; represents the setting degree of the concrete at the current moment; represents the time priority of the target direct road segment; represents the fuel consumption priority of the target direct road segment.

[0009] Further, the obtaining of the time priority and fuel consumption priority of the target direct road segment includes: Take the average value of the predicted travel times of all direct road segments at the current intersection as the overall predicted travel time; Use the predicted travel time of the target direct road segment as the numerator and the overall predicted travel time as the denominator, and perform negative correlation normalization on the ratio value to obtain the time priority of the target direct road segment; Take the average value of the predicted fuel consumptions of all direct road segments at the current intersection as the overall predicted fuel consumption; Taking the predicted fuel consumption of the target direct road section as the numerator and the overall predicted fuel consumption as the denominator, perform negative correlation normalization on the ratio value to obtain the fuel consumption priority of the target direct road section.

[0010] Further, the obtaining the degree of setting of the concrete at the current moment includes: Taking the absolute value of the difference in the 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 the first setting performance value of the concrete in the transport vehicle tank at the current moment; Taking the absolute value of the difference in the round-trip duration 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 the second setting performance value of the concrete in the transport vehicle tank at the current moment; After comprehensively processing the first setting performance value and the second setting performance value and performing normalization, obtain the degree of setting of the concrete in the transport vehicle tank at the current moment.

[0011] Further, the selecting the best road section from all direct road sections based on the preference degree includes: Taking the direct road section corresponding to the maximum value of the preference degree as the best road section.

[0012] The present invention also proposes a path planning system for intelligent commercial concrete transport vehicles, and the system includes: A data acquisition module, configured to obtain the traffic flow and average vehicle speed of different road sections between the commercial concrete prefabrication point and the construction point at different time periods of each day, and obtain various road condition information of each road section; A road section congestion analysis module, configured to obtain the congestion degree of each road section at each time period according to the traffic flow and average vehicle speed of each road section at the same time period on different days; A priority analysis module, configured to take the moment when the transport vehicle reaches the current intersection as the current moment, obtain the direct road sections of the current intersection, and obtain the predicted passing duration and predicted fuel consumption of each direct road section according to the congestion degree and various road condition information of each time period within the preset time domain window of each direct road section at the current moment; obtain the ultrasonic echo signal and its round-trip duration of the concrete in the transport vehicle tank at the current moment, and obtain the preference degree of each direct road section according to the predicted passing duration and predicted fuel consumption of each direct road section, and the amplitude and round-trip duration of the ultrasonic echo signal at the current moment; A road section selection module, configured to select the best road section from all direct road sections based on the preference degree, drive the transport vehicle along the best road section to the next intersection and perform the selection of the best road section until the transport vehicle reaches the construction point.

[0013] The present invention also provides a computer medium, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of any one of the path planning methods for intelligent commercial concrete transport vehicles are implemented.

[0014] The present invention has the following beneficial effects: Considering that it is difficult to balance the relationship between economy and timeliness in transportation by using a pre-determined fixed path, which reduces the efficiency of commercial concrete transportation. Therefore, firstly, the traffic flow and average vehicle speed of different road sections between the commercial concrete prefabrication point and the construction point at different times of each day are obtained, and various road condition information of each road section is obtained. Considering that the congestion degree of the same road section has a daily periodicity with time changes, and the congestion degree of each road section is directly related to the traffic flow and average vehicle speed. Therefore, the road congestion situation of each road section at different times can be firstly reflected by the congestion degree, and the passing duration and fuel consumption of the transport vehicle driving on each direct road section at the current intersection can be further predicted. At the same time, considering that as the concrete in the tank solidifies, both the amplitude and round-trip duration of the ultrasonic echo signal will decrease. Therefore, based on the amplitude and round-trip duration of the ultrasonic echo signal at the current moment, combined with the predicted passing duration and predicted fuel consumption of each direct road section, the relationship between economy and timeliness in commercial concrete transportation can be effectively balanced, so as to obtain the preference degree of each direct road section, and based on the preference degree, the best road section is selected. 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 point, so as to realize the dynamic selection of road sections during transportation and improve the final commercial concrete transportation efficiency. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a flowchart of a path planning method for an intelligent commercial concrete transport vehicle provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of the position distribution of multiple road sections between the commercial concrete prefabrication point and the construction point provided by an embodiment of the present invention; Figure 3 It is a block diagram of a path planning system for an intelligent commercial concrete transport vehicle provided by an embodiment of the present invention; Figure 4 It is a schematic diagram of the distribution of a computer medium provided by an embodiment of the present invention. Detailed implementation manners

[0017] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following will, in combination with the accompanying drawings and preferred embodiments, elaborate in detail on a path planning method, system, and medium for intelligent commercial concrete transport vehicles proposed according to the present invention, including its specific implementation manners, structures, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs.

[0019] The following will specifically describe the specific solutions of a path planning method, system, and medium for intelligent commercial concrete transport vehicles provided by the present invention with reference to the accompanying drawings.

[0020] Please refer to Figure 1 , which shows a flowchart of a path planning method for intelligent commercial concrete transport vehicles provided by an embodiment of the present invention. The method includes: Step S1: Obtain the traffic flow and average vehicle speed of different road sections between the commercial concrete prefabrication point and the construction point at different time periods of each day, and obtain various road condition information for each road section.

[0021] Commercial concrete, that is, commercial concrete, after being prepared at the commercial concrete prefabrication point, is transported from the prefabrication point to the construction point by a transport vehicle. Therefore, the prefabrication point can be used as the starting point and the construction point as the ending point.

[0022] In an embodiment of the present invention, first, obtain the traffic flow and average vehicle speed of different road 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 obtain various road condition information for each road section. The road condition information includes information such as the length, width, and slope of each road section. Among them, the preset historical time period is set to 30 days, and the length of a single time period in a day is usually 10 - 20 minutes. In an embodiment of the present invention, the length of a single time period is set to 15 minutes. The specific values of the preset historical time period and the length of a single time period can also be set by the implementer according to the specific implementation scenario and are not limited herein.

[0023] Please refer to Figure 2 , which shows a schematic diagram of the position distribution of multiple road sections between the commercial concrete prefabrication point and the construction point provided by an embodiment of the present invention. Among them, for example, ab is a road section, fe is a road section, and a, b, f, and e are intersection points respectively.

[0024] Meanwhile, in the embodiment of the present invention, it is also necessary to install an ultrasonic sensor on the surface of the concrete mixer truck tank, so that during subsequent transportation, when the truck travels to an intersection, the ultrasonic sensor is used to emit and receive ultrasonic signals to the concrete in the tank.

[0025] It should be noted that when the truck just departs from the commercial concrete prefabrication factory, it is necessary to use the ultrasonic sensor to emit an ultrasonic signal into the tank once, and receive the ultrasonic echo signal, and at the same time record the time interval from the emission to the reception of the signal this time, that is, the round-trip duration of the ultrasonic echo signal.

[0026] Step S2: Obtain the congestion degree of each road section at each time period according to the traffic flow and average vehicle speed of each road section at the same time period on different days.

[0027] During the transportation process of the concrete mixer truck, generally two main factors need to be considered. One is the urgency of transportation time, because when the concrete travels for too long, it is easy to solidify, resulting in a decline in the quality of the concrete; the other is the economy of transportation, because the commercial concrete transport vehicle generally has a large load, high fuel consumption, and serious pollution emissions, so the driver is more inclined to save transportation costs and reduce environmental pollution by reducing fuel consumption. Therefore, when selecting the optimal road section for the transport vehicle, on the one hand, it is necessary to consider the passing 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 driving on the road section.

[0028] At the same time, considering that the congestion degree of the same road section changes with time in a daily cycle, and the congestion situation of each road section is directly related to the traffic flow and average vehicle speed, therefore, in the embodiment of the present invention, first, the traffic flow and average vehicle speed of each road section at the same time period on different days are analyzed, and the congestion degree of each road section at different time periods is reflected by the obtained congestion degree. Subsequently, based on the congestion degree of different road sections at each time period, the passing time and fuel consumption of the transport vehicle on the road section can be accurately predicted.

[0029] Preferably, in an embodiment of the present invention, the method for obtaining the congestion degree of each road section at each time period specifically includes: First, take any road section as the target road section. The smaller the average vehicle speed of the vehicles on the target road section, the more congested the target road section is. Therefore, a negative correlation mapping can be performed on the average vehicle speed of the target road section at each time period of each day to obtain the first congestion performance value of the target road section at each time period of each day.

[0030] The smaller the traffic flow on the target road section, the more congested the target road section is. Therefore, a negative correlation mapping can be performed on the traffic flow of the target road section at each time period of each day to obtain the second congestion performance value of the target road section at each time period of each day.

[0031] Then, the first congestion performance value and the second congestion performance value of the target road section in each time period of each day are combined and then normalized, and the calculation result is limited to within the range, so as to obtain the comprehensive congestion coefficient of the target road section in each time period of each day.

[0032] In the embodiment of the present invention, the combination of the two can be realized by calculating the sum value or the product value of the first congestion performance value and the second congestion performance value of the target road section in each time period of each day, and no limitation is made here.

[0033] In an embodiment of the present invention, the normalization process can be specifically, for example, the maximum-minimum normalization process, and the normalization in subsequent steps can all adopt the maximum-minimum normalization process. In other embodiments of the present invention, other normalization methods can be selected according to the specific range of values, or the activation function and the hyperbolic tangent function can be used to realize the normalization process, which will not be elaborated and limited herein.

[0034] Furthermore, the average value of the comprehensive congestion coefficients of the target road section in the same time period of all days is used as the congestion degree of the target road section in each time period.

[0035] As an example, in an embodiment of the present invention, the expression of the congestion degree of the target road section in each time period can be specifically, for example: Among them, represents the congestion degree of the target road section in the th time period; represents the comprehensive congestion coefficient of the target road section in the th day and the th time period; represents the number of days included in the preset historical time period; represents the average vehicle speed of the target road section in the th day and the th time period; represents the first congestion performance value of the target road section in the th day and the th time period; represents the traffic flow of the target road section in the th day and the th time period; represents the second congestion performance value of the target road section in the th day and the th time period; and respectively represent a preset first adjustment parameter and a preset second adjustment parameter, which are used to prevent the denominator from being 0, and The value range of , in an embodiment of the present invention, and are both set to 0.01. and The specific values of

[0036] The congestion level of each road section at each time period can be obtained by the same method described above.

[0037] Step S3: Take the moment when the transport vehicle arrives at the current intersection as the current moment, obtain the direct road sections of the current intersection, and obtain the predicted passing time and predicted fuel consumption of each direct road section according to the congestion levels of each time period within the preset time domain window at the current moment and various road conditions; obtain the ultrasonic echo signal and its round-trip duration of the concrete in the transport vehicle tank at the current moment, and obtain the preference level of each direct road section according to the predicted passing time and predicted fuel consumption of each direct road section, as well as the amplitude and round-trip duration of the ultrasonic echo signal at the current moment.

[0038] During the actual transportation process of the commercial concrete transport vehicle, it is necessary to select different road sections to drive until it reaches the construction site. When the transport vehicle enters another road section from one road section, it will pass through the intersection between these two road sections. Since the intersection usually connects multiple road sections, there are differences in the congestion conditions of different road sections, and there are also differences in the possible passing time and fuel consumption of the commercial concrete transport vehicle on different road sections. Therefore, when the commercial concrete transport vehicle arrives at the intersection, it is necessary to select an optimal road section to pass through to ensure the efficiency of commercial concrete transportation. Therefore, during the actual transportation process, first take the moment when the transport vehicle arrives at the current intersection as the current moment, and obtain the direct road sections of the current intersection. Subsequently, the passing time and fuel consumption of each direct road section can be predicted, and the optimal road section can be selected to pass through.

[0039] Preferably, in an embodiment of the present invention, the direct road sections of the current intersection all start from the current intersection. At the same time, in order to prevent the transport vehicle from taking a detour, the end point of the selected direct road section is an intersection that the transport vehicle has not passed through yet.

[0040] Since the efficiency of commercial concrete transportation mainly lies in two aspects: transportation timeliness and economic cost. The transportation timeliness mainly depends on the passing 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 passing time of the transport vehicle on the road section and the fuel consumption are not only related to the congestion degree of the road section, but also related to the road condition information of the road section. At the same time, considering that the congestion degree of the road section has a daily periodicity and will not change significantly in a short time, therefore, according to the congestion degree of each direct road section at each time period within the preset time domain window at the current moment and various road condition information, accurately predict the passing duration and fuel consumption of the transport vehicle when driving on each direct road section at the current intersection. Subsequently, based on the predicted passing duration and predicted fuel consumption of each direct road section, analyze the preference degree of each direct road section.

[0041] Preferably, in an embodiment of the present invention, the method for obtaining the predicted passing duration and predicted fuel consumption of each direct road section specifically includes: Using the road traffic management big data system, collect historical data such as the congestion degree of different road sections at each time period, all road condition information of the road section, the passing time and fuel consumption of the transport vehicle when passing through the road section, and construct a feature vector and the output label corresponding to the feature vector as the training set. Among them, the elements in the feature vector are all road condition information of the road section and the congestion degree of the road section at each time period, and the elements in the output label are the passing time and fuel consumption. Among them, the form of the feature vector can be, for example, (congestion degree, road width, road length, road slope...), and the form of the output label can be, for example, (passing time, fuel consumption), and use the feature vector of each road section and the corresponding output label to train the neural network to obtain a trained neural network. Among them, the CNN architecture can be selected as the basic framework of the neural network, and the loss function can choose the cross-entropy function.

[0042] Take any one direct road section as the target direct road section. Since the transport vehicle needs to take a certain period of time to pass through the direct road section, in order to accurately predict the actual congestion situation of the target direct road section, the average value of the congestion degree of all time periods within the preset time domain window of the target direct road section at the current moment can be used as the local congestion degree of the target direct road section at the current moment. Among them, the length of the preset time domain window is set to 3, that is, the preset time domain window includes the time period where the current moment is located and the two time periods closest to the time period where the current moment is located. 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.

[0043] Furthermore, the feature vector composed of the local congestion degree of the target direct road section at the current moment and all road condition information of the target direct road section can be input into the trained neural network, and the predicted passing duration and predicted fuel consumption of the target direct road section are output.

[0044] By the same method as described above, the predicted travel time and predicted fuel consumption of each direct road section at the current intersection can be obtained.

[0045] After predicting the travel time and fuel consumption of each direct road section, it is also necessary to combine the setting condition of the concrete in the transport vehicle tank to determine whether to give priority to the economic principle to reduce fuel consumption or to give priority to the time factor to prevent the concrete from setting. Considering that the setting degree of the concrete in the transport vehicle tank increases with the passage of time, and as the setting degree of the concrete increases, the attenuation of the ultrasonic signal intensity in the concrete is more serious, 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, an ultrasonic sensor is used to emit an ultrasonic signal into the tank body and receive the ultrasonic echo signal, and at the same time, the round-trip time of the ultrasonic echo signal is recorded. Then, 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 road section, the relationship between the economy and timeliness of commercial concrete transportation is effectively balanced, so as to obtain the preference degree of each direct road section.

[0046] Preferably, in an embodiment of the present invention, the method for obtaining the preference degree of each direct road section specifically includes: First, according to the deviation of the predicted travel time of the target direct road section from the overall level of the predicted travel times of all direct road sections, and the deviation of the predicted fuel consumption of the target direct road section from the overall level of the predicted fuel consumptions of all direct road sections, the time priority and fuel consumption priority of the target direct road section are obtained; the greater the time priority of the target direct road section, it indicates that the travel time of the transport vehicle on the target direct road section is shorter than that of other direct road sections, and the greater the fuel consumption priority of the target direct road section, it indicates that the fuel consumption of the transport vehicle passing through the target direct road section is less than that of other direct road sections.

[0047] Preferably, in an embodiment of the present invention, the method for obtaining the time priority and fuel consumption priority of the target direct road section specifically includes: Taking the average value of the predicted travel times of all direct road sections at the current intersection 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 road sections.

[0048] Taking the predicted travel time of the target direct road section as the numerator and the overall predicted travel time as the denominator, performing a negative correlation normalization process on the ratio value, and limiting the calculation result within the range, so as to obtain the time priority of the target direct road section.

[0049] The average predicted fuel consumption of all direct road segments at the current intersection is used as the overall predicted fuel consumption, which is used to reflect the overall level of the predicted fuel consumption of all direct road segments.

[0050] Taking the predicted fuel consumption of the target direct road segment as the numerator and the overall predicted fuel consumption as the denominator, the ratio is subjected to negative correlation normalization processing, and the calculation result is limited to within the range to obtain the fuel priority of the target direct road segment.

[0051] In an embodiment of the present invention, the negative correlation normalization processing can be implemented using the function form, where represents the normalization function.

[0052] In other embodiments of the present invention, it is also possible to perform negative correlation normalization processing on the difference between the predicted travel time of the target direct road segment and the overall predicted travel time to obtain the time priority of the target direct road segment, and perform negative correlation normalization processing on the difference between the predicted fuel consumption of the target direct road segment and the overall predicted fuel consumption to obtain the fuel priority of the target direct road segment, which is not limited herein.

[0053] As an example, in an embodiment of the present invention, the expressions of the time priority and fuel priority of the target direct road segment can be specifically, for example: where, represents the time priority of the target direct road segment; represents the fuel priority of the target direct road segment; represents the predicted travel time of the target direct road segment; represents the overall predicted travel time; represents the predicted fuel consumption of the target direct road segment; represents the overall predicted fuel consumption; represents the normalization function.

[0054] Then, obtain the ultrasonic echo signal and its round-trip duration of the concrete in the transport vehicle tank at the commercial concrete prefabrication point. Since the concrete gradually sets over time, the amplitude and round-trip duration of the ultrasonic echo signal at the current moment are smaller and the difference is greater compared to the concrete at the commercial concrete prefabrication point. Therefore, based on the difference in the 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, and the difference in the round-trip duration between the ultrasonic echo signal at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point, the setting degree of the concrete at the current moment can be obtained. It should be noted that the average amplitude of the ultrasonic echo signal can be calculated by integral or by averaging the amplitudes of multiple sampling points on the signal, which is a well-known technical means to those skilled in the art and will not be limited herein.

[0055] Preferably, in an embodiment of the present invention, the method for obtaining the setting degree of the concrete at the current moment specifically includes: Take the absolute value of the difference in the 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 the first setting performance value of the concrete in the transport vehicle tank at the current moment.

[0056] Take the absolute value of the difference in the round-trip duration 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 the second setting performance value of the concrete in the transport vehicle tank at the current moment.

[0057] After comprehensively processing the first setting performance value and the second setting performance value and performing normalization processing, limit the calculation result within the range, so as to obtain the setting degree of the concrete in the transport vehicle tank at the current moment.

[0058] In the embodiment of the present invention, the comprehensive processing of the two can be achieved by calculating the sum value or product value of the first setting performance value and the second setting performance value, which will not be limited herein.

[0059] As an example, in an embodiment of the present invention, the expression of the setting degree of the concrete at the current moment can be specifically, for example: Wherein, represents the setting degree of the concrete at the current moment; represents the average amplitude of the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment; represents the average amplitude of the ultrasonic echo signal of the concrete in the transport vehicle tank at the commercial concrete prefabrication point; represents the first setting performance value of the concrete in the transport vehicle tank at the current moment; It represents the round-trip duration of the ultrasonic echo signal of the concrete in the transport vehicle tank at the current moment; It represents the round-trip duration of the ultrasonic echo signal of the concrete in the transport vehicle tank at the precast point of commercial concrete; It represents the second setting performance value of the concrete in the transport vehicle tank at the current moment; It represents the normalization function.

[0060] When the degree of setting of the concrete at the current moment is greater, at this time, the section should be selected mainly based on time priority to prevent the concrete from setting too heavily and ensure the transportation timeliness. When the degree of setting of the concrete at the current moment is smaller, at this time, the section should be selected mainly based on fuel consumption priority to prevent high fuel consumption and ensure the economic cost of transportation. Therefore, based on the calculation formula of the preference degree, the preference degree of the target direct section can be obtained. The calculation formula of the preference degree is: Among them, It represents the preference degree of the target direct section; It represents the degree of setting of the concrete at the current moment; It represents the time priority of the target direct section; It represents the fuel consumption priority of the target direct section.

[0061] Through the above same method, the preference degree of each direct section of the current intersection can be obtained.

[0062] Step S4: Based on the preference degree, select the best section from all direct sections, drive the transport vehicle along the best section to the next intersection and select the best section until the transport vehicle reaches the construction site.

[0063] The greater the preference degree of a certain direct section, it indicates that the transport vehicle at the current intersection needs to preferentially select this direct section to pass. Therefore, based on the preference degree, the best section can be selected from all direct sections, thereby improving the commercial concrete transportation efficiency.

[0064] Preferably, in an embodiment of the present invention, the direct section corresponding to the maximum value of the preference degree can be used as the best section. Then the transport vehicle can pass along this best section. When passing through to the next intersection, the best section can be selected again through the above same method until the transport vehicle reaches the construction site and completes the current commercial concrete transportation.

[0065] An embodiment of the present invention provides a path planning system for intelligent commercial concrete transport vehicles. Please refer to Figure 3, which shows a flowchart of a path planning method for intelligent commercial concrete transport vehicles provided by an embodiment of the present invention. The system includes: a data acquisition module 101, a road section congestion analysis module 102, a priority analysis module 103, and a road section selection module 104.

[0066] The data acquisition module is used to obtain the traffic flow and average vehicle speed of different road sections between the commercial concrete prefabrication point and the construction point at different times of each day, and obtain various road conditions information of each road section.

[0067] The road section congestion analysis module is used to obtain the congestion degree of each road section at each time period according to the traffic flow and average vehicle speed of each road section at the same time period on different days.

[0068] The priority analysis module is used to take the moment when the transport vehicle arrives at the current intersection as the current moment, obtain the direct road sections of the current intersection, and obtain the predicted passing duration and predicted fuel consumption of each direct road section according to the congestion degree of each direct road section at each time period within the preset time domain window at the current moment and various road conditions information; obtain the ultrasonic echo signal and its round-trip duration of the concrete in the transport vehicle tank at the current moment, and obtain the preference degree of each direct road section according to the predicted passing duration and predicted fuel consumption of each direct road section, and the amplitude and round-trip duration of the ultrasonic echo signal at the current moment.

[0069] The road section selection module is used to select the best road section from all direct road sections based on the preference degree, 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 point.

[0070] An embodiment of the present invention provides a computer medium. Please refer to Figure 4 , which shows a schematic diagram of the distribution of a computer medium provided by an embodiment of the present invention. The medium includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it can implement the method described in steps S1 to S4.

[0071] It should be noted that: the above sequence of embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0072] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are the differences from other embodiments.

Claims

1. A path planning method for intelligent commercial concrete transport vehicles, characterized in that, The method includes: Obtaining the traffic flow and average vehicle speed of different road sections between the commercial concrete prefabrication point and the construction point at different time periods of each day, and obtaining various road condition information of each road section; Obtaining the congestion degree of each road section at each time period according to the traffic flow and average vehicle speed of each road section at the same time period on different days; Taking the moment when the transport vehicle reaches the current intersection as the current moment, obtaining the direct road sections of the current intersection, and obtaining the predicted passing duration and predicted fuel consumption of each direct road section according to the congestion degree and various road condition information of each time period within the preset time domain window of each direct road section at the current moment; Obtaining the ultrasonic echo signal and its round-trip duration of the concrete in the transport vehicle tank at the current moment, and obtaining the preference degree of each direct road section according to the predicted passing duration and predicted fuel consumption of each direct road section, as well as the amplitude and round-trip duration of the ultrasonic echo signal at the current moment; Based on the preference degree, selecting the best road section from all direct road sections, driving the transport vehicle along the best road section to the next intersection and making a selection of the best road section until the transport vehicle reaches the construction point.

2. The path planning method for an intelligent commercial concrete transport vehicle according to claim 1, wherein The obtaining the congestion degree of each road section at each time period includes: Taking any road section as the target road section, performing a negative correlation mapping on the average vehicle speed of the target road section at each time period of each day to obtain the first congestion performance value of the target road section at each time period of each day; Performing a negative correlation mapping on the traffic flow of the target road section at each time period of each day to obtain the second congestion performance value of the target road section at each time period of each day; After comprehensively processing the first congestion performance value and the second congestion performance value of the target road section at each time period of each day and performing normalization processing, obtaining the comprehensive congestion coefficient of the target road section at each time period of each day; Taking the average value of the comprehensive congestion coefficients of the target road section at the same time period on all days as the congestion degree of the target road section at 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 road section is the current intersection, and the end point of the direct road section is an 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 obtaining the predicted passing duration and predicted fuel consumption of each direct road section includes: Using the congestion degree of the road section at each time period, all road condition information of the road section, the passing time and fuel consumption of the transport vehicle when passing through the road section collected historically, constructing a feature vector and the output label corresponding to the feature vector, the elements in the feature vector are all road condition information of the road section and the congestion degree of the road section at each time period, the elements in the output label are the passing time and fuel consumption, and using the feature vectors and corresponding output labels of each road section to train the neural network to obtain the trained neural network; Taking any direct road section as the target direct road section, and taking the average value of the congestion degrees of all time periods within the preset time domain window of the target direct road section at the current moment as the local congestion degree of the target direct road section at the current moment; Input the feature vector composed of the local congestion degree of the target direct road section at the current moment and all road condition information of the target direct road section into the trained neural network, and output the predicted travel time and predicted fuel consumption of the target direct road section.

5. A path planning method for intelligent commercial concrete transport vehicles according to claim 4, characterized in that, The obtaining of the preference degree of each direct road section includes: Obtain the time priority and fuel consumption priority of the target direct road section according to the deviation of the predicted travel time of the target direct road section from the overall level of the predicted travel time of all direct road sections, and the deviation of the predicted fuel consumption of the target direct road section from the overall level of the predicted fuel consumption of all direct road sections; Obtain the ultrasonic echo signal and its round-trip duration of the concrete in the transport vehicle tank at the commercial concrete prefabrication point, and obtain the setting degree of the concrete at the current moment according to the difference in the 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, and the difference in the round-trip duration between the ultrasonic echo signal at the current moment and the ultrasonic echo signal at the commercial concrete prefabrication point; Based on the calculation formula of the preference degree, obtain the preference degree of the target direct road section, and the calculation formula of the preference degree is: Among them, represents the preference degree of the target direct road section; represents the setting degree of concrete at the current moment; represents the time priority of the target direct road section; represents the fuel consumption priority of the target direct road section.

6. The path planning method for an intelligent commercial concrete transport vehicle according to claim 5, wherein, The obtaining of the time priority and fuel consumption priority of the target direct road section includes: Take the average value of the predicted travel times of all direct road sections at the current intersection as the overall predicted travel time; Take the predicted travel time of the target direct road section as the numerator, take the overall predicted travel time as the denominator, and perform a negative correlation normalization process on the ratio value to obtain the time priority of the target direct road section; Take the average value of the predicted fuel consumptions of all direct road sections at the current intersection as the overall predicted fuel consumption; Take the predicted fuel consumption of the target direct road section as the numerator, take the overall predicted fuel consumption as the denominator, and perform a negative correlation normalization process on the ratio value to obtain the fuel consumption priority of the target direct road section.

7. A path planning method for intelligent commercial concrete transport vehicles according to claim 5, characterized in that, The obtaining of the setting degree of the concrete at the current moment includes: Take the absolute value of the difference in the 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 the first setting performance value of the concrete in the transport vehicle tank at the current moment; Take the absolute value of the difference in the round-trip duration 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 the second setting performance value of the concrete in the transport vehicle tank at the current moment; After comprehensively processing the first setting performance value and the second setting performance value and performing a normalization process, obtain the setting degree of the concrete in the transport vehicle tank at the current moment.

8. A path planning method for intelligent commercial concrete transport vehicles according to claim 1, characterized in that, Based on the preference degree, the selection of the best road section from all direct road sections includes: Take the direct road section corresponding to the maximum value of the preference degree as the best road section.

9. A path planning system for intelligent commercial concrete transport vehicles, the system includes: A data acquisition module, configured to obtain the traffic flow and average vehicle speed of different road sections between the commercial concrete prefabrication point and the construction point at different times of each day, and obtain various road condition information of each road section; A road section congestion analysis module, configured to obtain the congestion level of each road section at each time period according to the traffic flow and average vehicle speed of each road section at the same time period on different days; A priority analysis module, configured to use the time when the transport vehicle arrives at the current intersection as the current time, obtain the direct road sections of the current intersection, and obtain the predicted travel duration and predicted fuel consumption of each direct road section according to the congestion levels of each time period within the preset time domain window of each direct road section at the current time and various traffic conditions; obtain the ultrasonic echo signal and its round-trip duration of the concrete in the transport vehicle tank at the current time, and obtain the preference level of each direct road section according to the predicted travel duration and predicted fuel consumption of each direct road section, as well as the amplitude and round-trip duration of the ultrasonic echo signal at the current time; A road section selection module, configured to select the best road section from all direct road sections based on the preference level, drive the transport vehicle along the best road section to the next intersection and perform the selection of the best road section until the transport vehicle reaches the construction site.

10. A computer medium, the medium comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

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