Implementation method of an intelligent driving system

Abstract

The present invention provides a method for implementing a smart driving system, belonging to the technical field of smart guidance, including: when a target vehicle enters the initial area of a waste incineration power plant, parsing the vehicle permissions of qualified vehicles based on the Internet of Things management platform and establishing a communication connection with the waste incineration power plant; sending a guidance list to the qualified vehicles, dynamically counting the first guidance units involved by the qualified vehicles within the time period T1 and the transportation progress based on the last unit among all the first guidance units; estimating the waiting probability of the qualified vehicles in the subsequent guidance units based on the current positions of each transport vehicle in the waste incineration power plant and the vehicle density of each guidance unit obtained through the Internet of Things platform; and issuing guidance for the traveling route of the qualified vehicle based on the last unit according to the transportation progress of the last unit and the waiting probability of the subsequent guidance units, so as to send a qualified route to the qualified vehicle and effectively improve the transportation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent guidance, and particularly relates to a method for implementing an intelligent driving system. Background Art

[0002] Waste incineration has become an important part of the circular economy. In addition, in the process of recycling and processing waste resources and waste materials, not only the problem of resource shortage is solved, but also the waste discharge is reduced, which can be described as "killing two birds with one stone".

[0003] During the process of transporting waste to the incineration power plant, since there are multiple waste storage bins in the incineration power plant, the daily waste storage and incineration volume are large, and the types and trips of vehicles are numerous. Due to the different heat released by the incineration of waste transported from different regions into the plant and the operating status of the incineration line in the plant area, it is necessary to balance the calorific value of multiple waste storage bins and dynamically control the storage volume of waste in the tank body. However, currently, since the driver cannot clearly understand the current status of each waste storage bin, there will be a situation of blindly advancing during the transportation process, and due to the lack of knowledge of the possible congestion situations during the transportation process, the transportation efficiency is low.

[0004] Therefore, the present invention proposes a method for implementing an intelligent driving system. Summary of the Invention

[0005] The present invention provides a method for implementing an intelligent driving system, which is used to communicate with the vehicle by timely obtaining and updating the current status of each waste storage bin, providing an effective basis for subsequent transportation, and then capturing the vehicle conditions under each guiding unit to issue a qualified route to the qualified vehicles, effectively improving the transportation efficiency.

[0006] The present invention provides a method for implementing an intelligent driving system, including:

[0007] Step 1: When a target vehicle enters the initial area of the waste incineration power plant, analyze the vehicle authority of the qualified vehicle based on the Internet of Things management platform and establish a communication connection with the waste incineration power plant;

[0008] Step 2: After the communication connection is successful, issue a guiding list to the qualified vehicle, and when the qualified vehicle starts transportation based on the guiding list, dynamically count the first guiding units involved by the qualified vehicle within the T1 time period and the transportation progress based on the last unit among all the first guiding units;

[0009] Step 3: Based on the Internet of Things platform, obtain the current position of each transport vehicle in the waste incineration power plant and the vehicle density based on each guiding unit, and estimate the waiting probability of the qualified vehicle in the subsequent guiding units;

[0010] Step 4: According to the transportation progress of the last unit and the waiting probability of subsequent guiding units, issue guidance to qualified vehicles based on the travel route of the last unit.

[0011] Preferably, before parsing the vehicle permissions of qualified vehicles based on the Internet of Things management platform, it includes:

[0012] Send infrared light to the initial area based on the infrared sensing devices distributed around the initial area, and construct the object contour of the target vehicle entering the initial area;

[0013] Match the object contour with the contour database to determine whether there is a first contour with a similarity greater than the preset degree. If so, determine that the target vehicle entering the initial area is a qualified vehicle;

[0014] If not, capture the initial signal set Cc = {cy i1 , i1 = 1, 2, 3,..., n1} of the inductive loop coils distributed in an array in the initial area, where cy i1 is the initial signal value of the i1th coil unit;

[0015] Obtain the historical output voltage set of each coil unit based on the same transportation weight from the historical database to draw a historical voltage curve, and set a reset point in the historical voltage curve to obtain the normal working coefficient of the corresponding coil unit;

[0016]

[0017]

[0018]

[0019] Among them, Zg i1 represents the normal working coefficient of the i1th coil unit; n2 represents the number of curve segments of the corresponding historical voltage curve, and n2 - 1 is the number of reset points set in the corresponding historical voltage curve; dy j1 represents the average voltage value of the j1th curve segment in the corresponding historical voltage curve; L0 represents the total length of the corresponding historical voltage curve; L1 j1 represents the length of the j1th curve segment of the corresponding historical voltage curve; represents the voltage variance of the j1th curve segment in the corresponding historical voltage curve; ΔTb j1-1 represents the influence factor of the output voltage at the (j1 - 1)th reset point on the j1th curve segment; represents the variance based on all ; represents all the maximum value in; D0 GRepresents the reference voltage corresponding to the transport weight G; D1 i1 Represents the calculated voltage corresponding to the j1-th curve segment in the historical voltage curve; U0 represents the first voltage value in the j1-th curve segment; n2 j1 Represents the number of values corresponding to the j1-th curve segment; n3 j1 Represents the number of measured values under the remaining transport weights other than the number of values corresponding to the j1-th curve segment involved between the (j1 - 1)-th reset point and the j1-th reset point; ΔU N Represents the attenuation voltage obtained from the attenuation mapping table based on the (j1 - 1)-th reset point;

[0020] Based on the initial signal set and all normal working coefficients, obtain the effective signal set Sc = {sy i1 , i1 = 1, 2, 3,..., n1} of the arrayed distributed inductive loop, where sy i1 is the effective signal value of the i1-th coil unit, and sy i1 = cy i1 × Zg i1 ;

[0021] Analyze the effective signal set to determine whether it satisfies the signal diffusion law;

[0022] If it is satisfied, determine that the target vehicle entering the initial area is a qualified vehicle;

[0023] Otherwise, determine that the target vehicle entering the initial area is an unqualified vehicle and give a reminder to drive out.

[0024] Preferably, establish a communication connection with the waste incineration power plant, including:

[0025] Identify the license plate of the qualified vehicle and match it with the license plate - permission mapping table to obtain the vehicle permission;

[0026] Obtain the communication method and the waste bunker matching the vehicle permission from the permission - communication mapping table and establish a communication connection with the waste bunker.

[0027] Preferably, send a guidance list to the qualified vehicle, including:

[0028] Obtain the guidance unit of the qualified vehicle from the permission - transport unit according to the vehicle permission;

[0029] According to the bunker position of the waste bunker and the current position of the qualified vehicle, screen the required units from all guidance units to construct a guidance list.

[0030] Preferably, dynamically count the transportation progress of the qualified vehicle in the last unit of all the first guidance units, including:

[0031] Determine the distance progress according to the actual transportation distance and the standard transportation distance of the qualified vehicle in the last unit;

[0032] Based on the distance progress and combined with the actual transportation time in the last unit, obtain the transportation progress based on the last unit.

[0033] Preferably, step 3 includes:

[0034] Respectively count the actual transportation time of the first guiding unit involved by the qualified vehicle within the time period T1 and the corresponding route segments under each first guiding unit, and combine with the standard transportation time of the corresponding route segments under each first guiding unit to construct a transportation sequence where Ts j2 represents the actual transportation time of the corresponding route segment under the j2th first guiding unit; T0 j2 represents the standard transportation time of the corresponding route segment under the j2th first guiding unit; Ny represents the number of first guiding units;

[0035] Determine the first estimated density of the corresponding route segment under each first guiding unit according to the transportation sequence. At the same time, determine the second estimated density based on the corresponding route segment of the last unit according to the transportation progress;

[0036]

[0037]

[0038] where D1m j2 represents the first estimated density of the corresponding route segment under the j2th first guiding unit; a1 j2 represents the set full load density of the corresponding route segment under the j2th first guiding unit; Δ1 j2 represents the unit guiding density set based on the process attribute of the guiding unit under the j2th first guiding unit; D2m represents the second estimated density based on the corresponding route segment of the last unit; YJ represents the transportation progress based on the corresponding route segment of the last unit; UJ represents the standard progress based on the corresponding route segment of the last unit under the corresponding actual transportation time; Δyw represents the unit guiding density based on the last unit;

[0039] Determine the first sharing density of the corresponding route segment under each subsequent guiding unit according to all the first estimated densities and based on the vehicle density of the corresponding route segments under all the first guiding units determined by the Internet of Things platform;

[0040]

[0041] where F1 j3It represents the first sharing density corresponding to the route segment under the j3-th subsequent guiding unit; Xn j2 It represents the unit serial number of the j2-th first guiding unit; XN represents the unit serial number of the j3-th subsequent guiding unit; FN1 represents the number of fork points between the j2-th first guiding unit and the j3-th subsequent guiding unit; Cm j2 It represents the vehicle density corresponding to the route segment under the j2-th first guiding unit; Cm2 represents the vehicle density corresponding to the route segment under the last unit;

[0042] Meanwhile, according to the transportation progress corresponding to the last unit, and in combination with the vehicle density corresponding to the route segment under the last unit and the vehicle density corresponding to the route segment under each subsequent guiding unit, determine the second sharing density corresponding to the route segment under each subsequent guiding unit;

[0043]

[0044] Among them, FN2 represents the number of turning points between the last unit and the j3-th subsequent guiding unit; YJ0 represents the set standard progress of the corresponding overall route segment under the last unit; Cm3 j3 It represents the vehicle density corresponding to the route segment under the j3-th subsequent guiding unit; F2 j3 It represents the second sharing density corresponding to the route segment under the j3-th subsequent guiding unit;

[0045] According to the first sharing density and the second sharing density, and in combination with the vehicle density corresponding to the route segment under each subsequent guiding unit, estimate the waiting probability of qualified vehicles corresponding to the route segment under each subsequent guiding unit.

[0046] Preferably, step 4 includes:

[0047] Input the transportation progress of the last unit and the waiting probability of the subsequent guiding unit into the route analysis model in sequence, and output the travel route;

[0048] Send the travel route to the in-vehicle terminal of the qualified vehicle for display.

[0049] Preferably, estimating the waiting probability of qualified vehicles corresponding to the route segment under each subsequent guiding unit includes:

[0050]

[0051] Among them, a2 j3 It represents the set full-load density of the corresponding overall route segment of the j3-th subsequent guiding unit.

[0052] Compared with the prior art, the beneficial effects of this application are as follows:

[0053] Communicate and connect to the vehicle by obtaining and updating the current status of each waste bin in a timely manner, providing an effective basis for subsequent transportation. Subsequently, capture the vehicle situation under each guiding unit and issue qualified routes to qualified vehicles to effectively improve transportation efficiency.

[0054] Other features and advantages of the present invention will be described in the following specification, and partly will be obvious from the specification, or will be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained by the structures specifically pointed out in the written specification and the drawings.

[0055] The technical solution of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0056] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings:

[0057] Figure 1 is a flowchart of an implementation method of an intelligent driving system in an embodiment of the present invention;

[0058] Figure 2 is a route guiding diagram in an embodiment of the present invention;

[0059] Figure 3 is a T-junction guiding diagram in an embodiment of the present invention. Detailed Embodiments

[0060] The following describes the preferred embodiments of the present invention with reference to the drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0061] The present invention provides an implementation method of an intelligent driving system, as Figure 1 shown, including:

[0062] Step 1: When a target vehicle enters the initial area of the waste incineration power plant, parse the vehicle permissions of qualified vehicles based on the Internet of Things management platform and establish a communication connection with the waste incineration power plant;

[0063] Step 2: After the communication connection is successful, issue a guiding list to the qualified vehicles, and when the qualified vehicles start transportation based on the guiding list, dynamically count the first guiding units involved by the qualified vehicles within the T1 time period and the transportation progress based on the last unit among all the first guiding units;

[0064] Step 3: Based on the Internet of Things platform, obtain the current location of each transport vehicle in the waste incineration power plant and estimate the waiting probability of qualified vehicles at subsequent guiding units based on the vehicle density of each guiding unit;

[0065] Step 4: According to the transportation progress of the last unit and the waiting probability of subsequent guiding units, issue guidance to the qualified vehicles based on the travel route of the last unit.

[0066] In this embodiment, the initial area refers to the entrance where vehicles enter the waste incineration power plant.

[0067] In this embodiment, the Internet of Things management platform is constructed based on the waste incineration plant.

[0068] In this embodiment, vehicle authority refers to which waste bin the corresponding qualified vehicle needs to transport the waste to.

[0069] In this embodiment, the target vehicle refers to the vehicle that enters the initial area. However, not all vehicles entering the initial area are qualified, so it is necessary to analyze whether the target vehicle is qualified to determine whether a communication connection with the power plant can be established.

[0070] In this embodiment, the guiding list includes: vehicle entry into the plant, weighing at the weighbridge, waiting at the waste bin ramp entrance, entering the waste bin for unloading, exiting the waste bin, weighing at the weighbridge, and vehicle departure within the unit.

[0071] In this embodiment, the T1 time period is randomly set and is less than one-tenth of the standard time when there is no congestion in the complete routes corresponding to all guiding units in the guiding list.

[0072] In this embodiment, the guiding units included in the guiding list of each qualified vehicle may be different. It should be noted that the unit locations of each guiding unit may be different, and thus the travel routes sent to the corresponding vehicles will also be different.

[0073] In this embodiment, the communication connection refers to the internal network connection between the vehicle and the waste incineration power plant. For example, vehicle 1 is connected to waste bin 01 using communication method 1, vehicle 2 is connected to waste bin 02 using communication method 2, and so on.

[0074] In this embodiment, for example, the first guiding units involved by vehicle 1 within the T1 time period are: vehicle entry into the plant unit, weighing at the weighbridge unit, and the last unit involved is: waiting at the waste bin ramp entrance unit.

[0075] In this embodiment, the transportation progress refers to the driving progress of the corresponding vehicle at the last unit.

[0076] In this embodiment, the vehicle density can be obtained based on vehicle positioning via the network after communication connection through the Internet of Things management platform.

[0077] In this embodiment, the waiting probability refers to the possibility that a vehicle needs to wait.

[0078] In this embodiment, the driving route is obtained based on a route analysis model.

[0079] The beneficial effects of the above technical solution are as follows: By timely obtaining and updating the current status of each garbage bin to establish a communication connection with the vehicle, an effective basis is provided for subsequent transportation. And subsequently, by capturing the vehicle situation under each guiding unit, a qualified route is issued to the qualified vehicles, effectively improving the transportation efficiency.

[0080] The present invention provides a method for implementing an intelligent driving system. Before parsing the vehicle permissions of qualified vehicles based on the Internet of Things management platform, it includes:

[0081] Based on infrared sensing devices distributed around the initial area, infrared light is sent to the initial area to construct the object contour of the target vehicle entering the initial area;

[0082] The object contour is matched with the contour database to determine whether there is a first contour with a similarity greater than a preset degree. If so, it is determined that the target vehicle entering the initial area is a qualified vehicle;

[0083] If not, capture the initial signal set Cc = {cy i1 , i1 = 1, 2, 3,..., n1} of the inductive loop coils distributed in an array in the initial area, where cy i1 is the initial signal value of the i1-th coil unit;

[0084] Obtain the historical output voltage set of each coil unit based on the same transportation weight from the historical database to draw a historical voltage curve, and set a reset point in the historical voltage curve to obtain the normal working coefficient of the corresponding coil unit;

[0085]

[0086]

[0087]

[0088] Among them, Zg i1 represents the normal working coefficient of the i1-th coil unit; n2 represents the number of curve segments of the corresponding historical voltage curve, and n2 - 1 is the number of reset points set in the corresponding historical voltage curve; dy j1represents the average voltage value of the j1-th curve segment in the corresponding historical voltage curve; L0 represents the total length of the corresponding historical voltage curve; L1 j1 represents the length of the j1-th curve segment of the corresponding historical voltage curve; represents the voltage variance of the j1-th curve segment in the corresponding historical voltage curve; ΔTb j1-1 represents the influence factor of the output voltage at the (j1 - 1)-th reset point on the j1-th curve segment; represents based on all variances; represents all the maximum value in; D0 G represents the reference voltage corresponding to the transport weight G; D1 i1 represents the calculated voltage of the j1-th curve segment in the corresponding historical voltage curve; U0 represents the first voltage value in the j1-th curve segment; n2 j1 represents the number of values corresponding to the j1-th curve segment; n3 j1 represents the number of measured values of the remaining transport weights involved between the (j1 - 1)-th reset point and the j1-th reset point except for the number of values corresponding to the j1-th curve segment; ΔU N represents the attenuation voltage obtained from the attenuation mapping table based on the (j1 - 1)-th reset point;

[0089] Based on the initial signal set and all normal working coefficients, obtain the effective signal set Sc = {sy i1 , i1 = 1, 2, 3,..., n1} of the arrayed distributed inductive loop, where sy i1 is the effective signal value of the i1-th coil unit, and sy i1 = cy i1 ×Zg i1 ;

[0090] Analyze the effective signal set to determine whether it satisfies the signal diffusion law;

[0091] If it is satisfied, determine that the target vehicle entering the initial area is a qualified vehicle;

[0092] Otherwise, determine that the target vehicle entering the initial area is an unqualified vehicle and remind it to drive out.

[0093] In this embodiment, the object contour refers to the vehicle contour.

[0094] In this embodiment, the contour database includes the contours of different vehicle models, and which vehicle models are required for garbage transportation in this waste incineration power plant are determined in advance.

[0095] In this embodiment, the preset degree is generally 0.8, and the similarity is obtained by performing similarity analysis on the object contour and the standard contour in the contour database based on a similarity function.

[0096] In this embodiment, for the inductive loop in an array distribution, when the vehicle reaches a specified position, there are n1 outputs. During the signal output process, due to the sensitivity change of the inductive loop coil, it is necessary to adjust the collected signal to restore the effective signal of the coil operation to the greatest extent, and then re-determine whether the vehicle is qualified to avoid the garbage being returned for re-transportation.

[0097] In this embodiment, for example, the signals in the effective signal set are successively: {1v, 1.2v, 1.4v, 1.6v, 1.8v, 2v, 1.8v, 1.6v}. Then, a curve is plotted based on the effective signal set. If the linear coefficients in the regions on both sides of 2v tend to be consistent and are within the set coefficient range (-1, 1), it is determined that the signal diffusion law is satisfied.

[0098] The beneficial effects of the above technical solution are as follows: First, the vehicle contour is obtained based on infrared light for preliminary analysis and judgment. When there is no similar situation, secondly, the distributed weight of the vehicle is obtained based on the inductive loop. In order to avoid the sensitivity change of the inductive loop, the signal is adjusted to obtain an effective signal. Finally, based on the comparative analysis of the signal diffusion law, it is determined whether the vehicle is qualified, providing a reliable basis for the subsequent route distribution and guidance.

[0099] The present invention provides a method for implementing an intelligent driving system, which establishes a communication connection with a waste incineration power plant, including:

[0100] Identify the license plate of the qualified vehicle and match it with the license plate-authority mapping table to obtain the vehicle authority;

[0101] Obtain the communication method and the waste bin matching the vehicle authority from the authority-communication mapping table, and establish a communication connection with the waste bin.

[0102] Among them, the license plate-authority mapping table is updated in real time. Because the status of the waste bin is different every day, and thus the required calorific value and waste situation will also be different. Therefore, after the vehicle enters the incineration plant, it is necessary to match the waste bin with the vehicle in a timely manner to determine the subsequent guiding unit of the vehicle and ensure its effective transportation.

[0103] In this embodiment, the license plate is recorded before the vehicle enters the incineration plant, the authority is updated every day according to the status of the waste bin, and the purpose of the authority is to determine which waste bin the vehicle with the corresponding license plate needs to transport the waste to.

[0104] For example: Date 1: License plate f1 -- waste bin 01, license plate f2 -- waste bin 02, license plate f3 -- waste bin 03;

[0105] Date 2: License plate f2 -- waste bin 01, license plate f1 -- waste bin 03, license plate f3 -- waste bin 02.

[0106] In this embodiment, the permission - communication mapping table includes different vehicle permissions and their corresponding communication methods, and the communication is displayed on the internal network of the waste incineration plant. Therefore, the corresponding communication method can be used for connection to obtain the result.

[0107] The beneficial effect of the above technical solution is that by identifying the license plate to obtain the vehicle permission, and then obtaining the communication method from the mapping table, effective communication is achieved, providing a basis for subsequent route distribution and route density analysis.

[0108] The present invention provides a method for implementing a smart driving system, which issues a guidance list to qualified vehicles, including:

[0109] Obtaining the guidance unit of the qualified vehicle from the permission - transportation unit mapping table according to the vehicle permission;

[0110] Filtering the required units from all the guidance units according to the bin location of the waste bin and the current location of the qualified vehicle, and constructing a guidance list.

[0111] In this embodiment, the permission - transportation unit mapping table includes the guidance units corresponding to different permissions. For example, the guidance units corresponding to the vehicle with permission u1 include unit 1, unit 3, unit 4, and unit 5. At this time, the units located between the bin location and the current location include unit 1, unit 3, and unit 4, which are regarded as the required units. Finally, the constructed guidance list is: unit 1, unit 3, and unit 4. It should be noted that unit 1, unit 3, and unit 4 are the corresponding locations.

[0112] The beneficial effect of the above technical solution is that by obtaining the guidance unit from the mapping table, and combining the bin location and the current location to obtain the required units, and then constructing a guidance list to facilitate subsequent route analysis.

[0113] The present invention provides a method for implementing a smart driving system, which dynamically counts the transportation progress of the last unit of the qualified vehicle in all the first guidance units, including:

[0114] Determining the distance progress according to the actual transportation distance and the standard transportation distance of the qualified vehicle in the last unit;

[0115] Based on the distance progress and combined with the actual transportation time in the last unit, obtaining the transportation progress based on the last unit.

[0116] In this embodiment, the distance progress = actual transportation distance / standard transportation distance.

[0117] In this embodiment, the transportation progress = the mean square of the product of the distance progress and the actual transportation efficiency at the actual transportation time.

[0118] The beneficial effects of the above technical solution are: determining the distance progress according to the actual and standard distances, and effectively obtaining the transportation progress in combination with the actual transportation time.

[0119] The present invention provides a method for implementing an intelligent driving system. Step 3 includes:

[0120] Statistically analyze the first guiding units involved by the qualified vehicles within the time period T1 and the actual transportation time of the corresponding route segments based on each first guiding unit, and construct a transportation sequence in combination with the standard transportation time of the corresponding route segments under each first guiding unit. where Ts j2 represents the actual transportation time of the corresponding route segment under the j2-th first guiding unit; T0 j2 represents the standard transportation time of the corresponding route segment under the j2-th first guiding unit; Ny represents the number of first guiding units;

[0121] Determine the first estimated density of the corresponding route segment under each first guiding unit according to the transportation sequence. At the same time, determine the second estimated density based on the corresponding route segment of the last unit according to the transportation progress.

[0122]

[0123]

[0124] where D1m j2 represents the first estimated density of the corresponding route segment under the j2-th first guiding unit; a1 j2 represents the set full-load density of the corresponding route segment under the j2-th first guiding unit; Δ1 j2 is the unit guiding density set based on the process attribute of the guiding unit under the j2-th first guiding unit; D2m represents the second estimated density based on the corresponding route segment of the last unit; YJ represents the transportation progress based on the corresponding route segment of the last unit; UJ represents the standard progress based on the corresponding route segment of the last unit at the corresponding actual transportation time; Δyw represents the unit guiding density based on the last unit;

[0125] Determine the first sharing density of the corresponding route segment under each subsequent guiding unit according to all the first estimated densities and the vehicle density of the corresponding route segments under all the first guiding units determined based on the Internet of Things platform.

[0126]

[0127] Among them, F1 j3 represents the first sharing density corresponding to the route segment under the j3 - th subsequent guiding unit; Xn j2 represents the unit number of the j2 - th first guiding unit; XN represents the unit number of the j3 - th subsequent guiding unit; FN1 represents the number of fork points between the j2 - th first guiding unit and the j3 - th subsequent guiding unit; Cm j2 represents the vehicle density of the route segment corresponding to the j2 - th first guiding unit; Cm2 represents the vehicle density of the route segment corresponding to the last unit;

[0128] Meanwhile, according to the transportation progress corresponding to the last unit, and in combination with the vehicle density of the route segment corresponding to the last unit and the vehicle density of the route segment corresponding to each subsequent guiding unit, determine the second sharing density of the route segment corresponding to each subsequent guiding unit;

[0129]

[0130] Among them, FN2 represents the number of turning points between the last unit and the j3 - th subsequent guiding unit; YJ0 represents the set standard progress of the overall route segment corresponding to the last unit; Cm3 j3 represents the vehicle density of the route segment corresponding to the j3 - th subsequent guiding unit; F2 j3 represents the second sharing density of the route segment corresponding to the j3 - th subsequent guiding unit;

[0131] According to the first sharing density and the second sharing density, and in combination with the vehicle density of the route segment corresponding to each subsequent guiding unit, estimate the waiting probability of qualified vehicles on the route segment corresponding to each subsequent guiding unit.

[0132] Preferably, estimating the waiting probability of qualified vehicles on the route segment corresponding to each subsequent guiding unit includes:

[0133]

[0134] Among them, a2 j3 represents the set full - load density of the overall route segment corresponding to the j3 - th subsequent guiding unit.

[0135] In this embodiment, the route segment corresponding to the j2 - th first guiding unit is the route segment between the j2 - th first guiding unit and the j2 + 1 - th first guiding unit.

[0136] In this embodiment, the standard distances and standard transportation times under different route segments are all set in advance.

[0137] In this embodiment, the unit guiding density is set in advance to relieve the traffic pressure on subsequent guiding units.

[0138] The beneficial effects of the above technical solution are as follows: Based on constructing the transportation sequence, it is convenient to determine the first estimated density, and based on obtaining the transportation progress, it is convenient to determine the second estimated density. Subsequently, by counting the vehicle density of different route segments and combining the estimated density, two sharing densities under different route segments can be effectively obtained, and then the waiting probability can be obtained, providing a data basis for subsequent route distribution, thereby further effectively improving the transportation efficiency.

[0139] The present invention provides a method for implementing an intelligent driving system. Step 4 includes:

[0140] Input the transportation progress of the last unit and the waiting probability of subsequent guiding units into the route analysis model in sequence, and output the traveling route;

[0141] Send the traveling route to the in-vehicle terminals of qualified vehicles for display.

[0142] In this embodiment, the route analysis model is based on different combinations of waiting probabilities and transportation progress, and the expert uses the given routes under the guiding list as samples to train the audit network model, which is convenient to obtain the traveling route. For example, a specific route guidance map, as Figure 2 shown.

[0143] Regarding Figure 2 the specific elaboration is as follows:

[0144] The approach and departure directions of the weighbridge area are double-lane weighbridges, and 4 license plate recognition cameras and 1 guiding screen are set at the weighbridge entrances and exits. The garbage trucks entering and leaving the factory area must enter the weighbridge area for weighing. When the vehicle is weighed, the intelligent license plate recognition weighing system of the weighbridge will record important data such as the license plate, fleet, and driver information of the garbage truck. When the vehicle leaves the park, it is weighed again. Here, the camera only recognizes the license plate and does not participate in the guidance. Only the cargo weight, license plate number, etc. information are extracted from the weighbridge system for data analysis and record filing.

[0145] After the vehicle enters the factory through the logistics entrance and exit of Gate 1 or Gate 2, it enters the weighbridge area for weighing. The vehicle guiding management system needs to extract the data of the weighbridge system. After analysis and processing, the guiding screen will display the vehicle license plate information and the name of the garbage storage to be entered.

[0146] Whether the A and B warehouse ramp barriers are open is determined by the total number of garbage unloading ports opened in the A and B warehouses on the same day. Based on the total number of remaining vehicles in the warehouse, the number of vehicles to be released is judged. When both the A and B warehouses are open, the guiding system guides the garbage trucks to queue up outside the A and B warehouse ramps at intervals. The license plate recognition collection terminal equipped with the guiding screen along the way can collect vehicle passing information, and at a position 10-15 meters away from the vehicle, it can prompt the vehicle's unloading location and driving direction (going straight, turning, or making a U-turn).

[0147] (1) Calculation of guiding time limit

[0148] The guiding system needs to distinguish between fleet groups (Fleet 1, Fleet 2... Fleet X). Each fleet group is calculated in a cycle of 10 vehicles, and the calculation period is in days (from 0:00 to 24:00, the cycle count before 0:00 is cleared. If the garbage bin accommodation ratio has not been changed, the calculation continues according to the original ratio).

[0149] (2) Proportion allocation algorithm

[0150] Each fleet group follows the principle of proportion allocation. For example, 3:7 (A garbage bin: B garbage bin), the guiding rule is: the first vehicle preferentially enters the B garbage bin with a larger proportion, the second vehicle enters A, the third vehicle enters B, the fourth vehicle enters A, the fifth vehicle enters B, the sixth vehicle enters A, and the seventh to tenth vehicles enter B. All proportions are calculated according to this principle.

[0151] Proportion customization: The proportion can be adjusted immediately and take effect immediately; or the effective time of the proportion adjustment can be reserved.

[0152] (3) Guiding function for special vehicles

[0153] Slag trucks, fly ash trucks, production material trucks (slaked lime, activated carbon, liquid caustic soda, chemical agents, diesel, chelating agents), and temporary material transport vehicles are designated to be guided to Warehouse A or Warehouse B.

[0154] (4) Docking interface for the weighbridge system

[0155] The weighbridge system will pre-enter important data such as the license plate number, fleet, and driver information of the garbage truck. The yard guiding management system needs to extract the data in the weighbridge system database.

[0156] (5) Report statistics and query

[0157] The amount of garbage entering the A and B garbage bins on the same day, month, and year can be queried according to the A and B warehouse intake, material name, date, and time period. The license plate number, the time of entering and leaving the park, the gross weight, tare weight, and net weight of the vehicle and goods, as well as the set proportion of the system when the vehicle enters the A and B garbage bins, are reflected in the report fields. The report can be exported and saved.

[0158] T-junction guiding, as Figure 3 shown

[0159] A guide screen is set up at the T-junction where the two garbage bins fork. The guide screen displays the following information: ① serial number, ② license plate, ③ unloading location, ④ driving direction (left turn or straight ahead). The above information is displayed in a scrolling manner (scrolling speed parameters can be set). When a vehicle leaves the weighbridge, it is recognized by the license plate recognition camera next to the weighbridge, and the license plate number and the name of the garbage bin it should enter are added to the scrolling display queue. When the vehicle enters garbage bins A and B, the license plate number is removed from the scrolling display queue of the guide screen.

[0160] Ramp Guidance System:

[0161] A set of license plate recognition cameras and traffic light equipment are installed at the entrance and exit of the garbage bin ramp to guide vehicles in and out of the garbage bin.

[0162] The number of vehicles that can stay in the garbage bin can be set manually. Take 6 vehicles as an example: the total number of vehicles on each unloading platform at the same time is controlled to be no more than 6. When the system detects that the number of vehicles entering the site is 6 and the unloading platform is fully loaded, vehicles in the waiting area at the ramp entrance will no longer be allowed to pass. When the number of vehicles on the unloading platform is less than 6, traffic will be resumed.

[0163] Red and green traffic lights and remaining space display screen: prompt one car per pole and the number of vacancies. If the number of vacancies is greater than 0, the light will turn green and the pole will be lifted to allow the vehicle to pass; if the number of vacancies is equal to 0, the light will turn red and the pole will not be lifted to allow the vehicle to pass (the exit direction recognition camera detects the vehicle leaving, the number of vacancies in the field increases by 1, and the pole is lifted again).

[0164] The beneficial effect of the above technical solution is: the travel route is obtained through model analysis and sent to the vehicle terminal for display, providing a basis for effective transportation.

[0165] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for implementing a smart driving system, characterized in that: include: Step 1: When a target vehicle enters the initial area of ​​the waste incineration power plant, the vehicle permissions of the qualified vehicle are parsed based on the IoT management platform, and a communication connection with the waste incineration power plant is established; Step 2: After the communication connection is successful, a guide list is issued to the qualified vehicle, and when the qualified vehicle starts transportation based on the guide list, the first guide unit involved in the qualified vehicle within the T1 time period and the transportation progress based on the last unit among all the first guide units are dynamically counted; Step 3: Based on the Internet of Things platform, the current position of each transport vehicle in the waste incineration power plant and the vehicle density of each guide unit are obtained, and the waiting probability of qualified vehicles in subsequent guide units is estimated; Step 4: According to the transportation progress of the last unit and the waiting probability of the subsequent guiding unit, the qualified vehicle is guided based on the route of the last unit; Wherein, step 3 includes: The first guidance units involved in the qualified vehicles in the T1 time period and the actual transportation time of the corresponding route segments under each first guidance unit are counted respectively, and the standard transportation time of the corresponding route segments under each first guidance unit is combined to construct a transportation sequence Among them, Ts j2 T0 represents the actual transportation time of the corresponding route segment under the j2th first guide unit; j2 represents the standard transportation time of the corresponding route segment under the j2th first guiding unit; Ny represents the number of first guiding units; Determine a first estimated density of a route segment corresponding to each first guide unit according to the transport sequence, and determine a second estimated density of a route segment corresponding to the last unit according to the transport progress; Among them, D1m j2 a1 represents the first estimated density of the corresponding route segment under the j2th first guiding unit; j2 represents the set full load density of the corresponding route segment under the j2th first guide unit; Δ1 j2 The unit guidance density set based on the process attributes of the guidance unit under the j2th first guidance unit; D2m represents the second estimated density based on the route segment corresponding to the last unit; YJ represents the transportation progress based on the route segment corresponding to the last unit; UJ represents the standard progress based on the route segment corresponding to the last unit at the corresponding actual transportation time; Δyw represents the unit guidance density based on the last unit; Determine a first shared density for a corresponding route segment under each subsequent guiding unit according to all first estimated densities and based on vehicle densities of corresponding route segments under all first guiding units determined by the Internet of Things platform; Among them, F1 j3 represents the first shared density of the corresponding route segment under the j3th subsequent guiding unit; Xn j2 represents the unit number of the j2th first guide unit; XN represents the unit number of the j3th subsequent guide unit; FN1 represents the number of forks between the j2th first guide unit and the j3th subsequent guide unit; Cm j2 represents the vehicle density of the corresponding route segment under the j2th first guide unit; Cm2 represents the vehicle density of the corresponding route segment under the last unit; At the same time, according to the transportation progress corresponding to the last unit, and in combination with the vehicle density of the corresponding route segment under the last unit and the vehicle density of the corresponding route segment under each subsequent guiding unit, a second shared density for the corresponding route segment under each subsequent guiding unit is determined; Among them, FN2 represents the number of turning points between the last unit and the j3th subsequent guiding unit; YJ0 represents the set standard progress of the corresponding overall route segment under the last unit; Cm3 j3 F2 represents the vehicle density of the corresponding route segment under the j3th subsequent guidance unit; j3 represents the second shared density of the corresponding route segment under the j3th subsequent guiding unit; According to the first sharing density and the second sharing density, and in combination with the vehicle density of the corresponding route segment under each subsequent guiding unit, the waiting probability of the qualified vehicle in the corresponding route segment under each subsequent guiding unit is estimated; Wherein, step 4 includes: The transportation progress of the last unit and the waiting probability of the subsequent leading unit are sequentially input into the route analysis model, and the travel route is output; The travel route is sent to the vehicle-mounted terminal of the qualified vehicle for display.

2. The method for implementing the intelligent driving system according to claim 1, characterized in that: Before parsing the vehicle permissions of qualified vehicles based on the IoT management platform, it includes: Infrared sensing devices distributed around the initial area send infrared light to the initial area to construct an object profile of a target vehicle entering the initial area; Matching the object contour with the contour database to determine whether there is a first contour with a similarity greater than a preset degree, and if so, determining that the target vehicle entering the initial area is a qualified vehicle; If not, capture the initial signal set Cc=cy of the array-distributed ground sensing coils in the initial area i1 ,i1=1,2,3,...,n1}, where cy i1 is the initial signal value of the i1th coil unit; Obtaining a historical output voltage set of each coil unit based on the same transport weight from a historical database to draw a historical voltage curve, and setting a reset point in the historical voltage curve to obtain a normal working coefficient of the corresponding coil unit; Among them, Zg i1 represents the normal working coefficient of the i1th coil unit; n2 represents the curve segment corresponding to the historical voltage curve, and n2-1 is the number of reset points set in the corresponding historical voltage curve; dy j1 represents the average voltage value of the j1th curve segment in the corresponding historical voltage curve; L0 represents the total length of the corresponding historical voltage curve; L1 j1 Indicates the length of the j1th curve segment corresponding to the historical voltage curve; Indicates the voltage variance of the j1th curve segment in the corresponding historical voltage curve; ΔTb j1-1 represents the influence factor of the output voltage at the j1-1th reset point on the j1th curve segment; Indicates that based on all The variance of Indicates all The maximum value among them; D0 G Indicates the reference voltage corresponding to the transport weight G; D1 i1 Indicates the calculated voltage corresponding to the j1th curve segment in the historical voltage curve; U0 indicates the first voltage value in the j1th curve segment; n2 j1 Indicates the number of values ​​corresponding to the j1th curve segment; n3 j1 Indicates the number of values ​​measured under the remaining transport weights between the j1-1th reset point and the j1th reset point, except for the number of values ​​corresponding to the j1th curve segment; ΔU N Indicates that the attenuation voltage based on the j1-1th reset point is obtained from the attenuation mapping table; Based on the initial signal set and all normal working coefficients, the effective signal set Sc of the array-distributed ground sensing coil is obtained. i1 ,i1=1,2,3,...,n1}, where sy i1 is the effective signal value of the i1th coil unit, and sy i1 =cy i1 ×Z i1 ; Analyzing the valid signal set to determine whether it satisfies the signal diffusion law; If the conditions are met, the target vehicle entering the initial area is determined to be a qualified vehicle; Otherwise, the target vehicle entering the initial area is determined to be an unqualified vehicle and is reminded to leave.

3. The method for implementing the intelligent driving system according to claim 1, characterized in that: Establishing communication connection with waste incineration power plant, including: Identify the license plate of the qualified vehicle and match it with the license plate-authority mapping table to obtain vehicle authority; A communication method and a garbage bin matching the vehicle authority are obtained from the authority-communication mapping table, and a communication connection with the garbage bin is established.

4. The method for implementing the intelligent driving system according to claim 3, characterized in that: A guidance list is issued to qualified vehicles, including: Obtaining a guide unit of the qualified vehicle from an authority-transportation unit according to vehicle authority; According to the bin position of the garbage bin and the current position of the qualified vehicle, required units are screened from all guide units to construct a guide list.

5. The method for implementing the intelligent driving system according to claim 1, characterized in that: Dynamically count the transportation progress of the last unit of all first-guided units of qualified vehicles, including: Determine the distance progress according to the actual transportation distance of the qualified vehicle in the last unit and the standard transportation distance; Based on the distance schedule and in combination with the actual transport time of the last unit, a transport schedule based on the last unit is obtained.

6. The method for implementing the intelligent driving system according to claim 1, characterized in that: Estimate the waiting probability of qualified vehicles in the corresponding route segment under each subsequent guidance unit, including: Among them, a2 j3 It represents the set full load density of the j3th subsequent guide unit corresponding to the overall route segment.

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