Intelligent navigation method based on traffic control platform and related device

Abstract

The invention discloses an intelligent navigation method based on a traffic control platform and a related device, and the method comprises the steps: obtaining the estimated navigation duration of each navigation route in m navigation routes, and obtaining m estimated navigation durations; obtaining the predicted cost of each navigation route in the m navigation routes to obtain m predicted costs; acquiring a traffic jam index of each navigation route in the m navigation routes in a preset time period between the current time to obtain m traffic jam index sets; fitting is carried out according to the m traffic jam index sets, and m fitting results are obtained; optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations; determining evaluation parameters of each navigation route in the m navigation routes according to the m predicted costs and the m target predicted navigation durations to obtain m evaluation parameters; and selecting the maximum value in the m evaluation parameters, and pushing the navigation route corresponding to the maximum value to the target vehicle.

Description

Technical Field

[0001] The present application relates to the field of smart transportation technology, big data technology, or computer technology, and in particular to an intelligent navigation method and related devices based on a traffic control platform. Background Art

[0002] With the rapid development of new energy vehicles, cars are becoming more and more popular and more and more intelligent. At present, navigation is also widely used in new energy vehicles. Navigation can not only generate navigation routes, but also estimate navigation time, which brings a lot of convenience to users. However, at present, navigation is not intelligent enough. Therefore, the problem of how to improve the intelligence of navigation needs to be solved urgently. Summary of the invention

[0003] The embodiments of the present application provide an intelligent navigation method and related devices based on a traffic control platform, which can improve navigation intelligence.

[0004] In a first aspect, an embodiment of the present application provides an intelligent navigation method based on a traffic control platform, which is applied to the traffic control platform. The method includes:

[0005] Receiving a navigation request from a target vehicle, the navigation request carrying a current position and a target position of the target vehicle;

[0006] Generate m navigation routes according to the current location and the target location, where m is an integer greater than 1;

[0007] Obtaining an estimated navigation duration for each of the m navigation routes to obtain m estimated navigation durations;

[0008] Obtaining an estimated cost of each of the m navigation routes to obtain m estimated costs;

[0009] Obtaining a traffic congestion index of each of the m navigation routes in a preset time period between the current time, to obtain m traffic congestion index sets; each of the m traffic congestion index sets includes a plurality of traffic congestion indexes, and each traffic congestion index corresponds to a sampling time;

[0010] Perform fitting according to the m traffic congestion index sets to obtain m fitting results;

[0011] Optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations;

[0012] Determining an evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation times, to obtain m evaluation parameters;

[0013] A maximum value among the m evaluation parameters is selected, and a navigation route corresponding to the maximum value is pushed to the target vehicle.

[0014] In a second aspect, an embodiment of the present application provides an intelligent navigation device based on a traffic control platform, which is applied to the traffic control platform. The device includes: a receiving unit, a generating unit, an acquiring unit, a fitting unit, an optimizing unit, a determining unit and a selecting unit, wherein:

[0015] The receiving unit is used to receive a navigation request of a target vehicle, wherein the navigation request carries a current position and a target position of the target vehicle;

[0016] The generating unit is used to generate m navigation routes according to the current position and the target position, where m is an integer greater than 1;

[0017] The acquisition unit is used to acquire the estimated navigation duration of each of the m navigation routes to obtain m estimated navigation durations; acquire the estimated cost of each of the m navigation routes to obtain m estimated costs; acquire the traffic congestion index of each of the m navigation routes in a preset time period between the current time to obtain m traffic congestion index sets; each of the m traffic congestion index sets includes a plurality of traffic congestion indexes, and each traffic congestion index corresponds to a sampling time;

[0018] The fitting unit is used to perform fitting according to the m traffic congestion index sets to obtain m fitting results;

[0019] The optimization unit is used to optimize the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations;

[0020] The determining unit is used to determine the evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation durations, so as to obtain m evaluation parameters;

[0021] The selection unit is used to select a maximum value among the m evaluation parameters and push the navigation route corresponding to the maximum value to the target vehicle.

[0022] In a third aspect, an embodiment of the present application provides a traffic control platform, which includes a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing some or all of the steps described in the method described in the first aspect of the embodiment of the present application.

[0023] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, wherein the computer program is executed by a processor to implement part or all of the steps described in the method described in the first aspect of the embodiment of the present application.

[0024] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the method described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0025] Implementing the embodiments of the present application has the following beneficial effects:

[0026] It can be seen that the intelligent navigation method based on the traffic control platform and the related device described in the embodiment of the present application are applied to the traffic control platform, receive a navigation request of a target vehicle, the navigation request carries the current position and the target position of the target vehicle, generate m navigation routes according to the current position and the target position, m is an integer greater than 1, obtain the estimated navigation time of each of the m navigation routes, obtain m estimated navigation times, obtain the estimated cost of each of the m navigation routes, obtain m estimated costs, obtain the traffic congestion index of each of the m navigation routes in a preset time period between the current time, and obtain m traffic congestion index sets; each of the m traffic congestion index sets includes multiple traffic congestion indexes, each traffic congestion index corresponds to a sampling time, and according to the m traffic congestion index sets Fitting is performed to obtain m fitting results, m estimated navigation times are optimized according to the m fitting results, m target estimated navigation times are obtained, evaluation parameters of each of the m navigation routes are determined according to the m estimated costs and the m target estimated navigation times, m evaluation parameters are obtained, the maximum value of the m evaluation parameters is selected, and the navigation route corresponding to the maximum value is pushed to the target vehicle, so that, firstly, each traffic congestion index set reflects the traffic change trend of each navigation route to a certain extent, and the traffic change trend is used to make the estimated navigation time more in line with future traffic changes, making the navigation time more accurate, and secondly, each navigation route can be accurately evaluated in combination with the cost and time that users care about, which is helpful to realize the navigation route push in an accurate, in-depth and humanized manner, and helps to improve the user experience, so that the navigation intelligence can be improved.

[0027] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 It is a schematic diagram of the architecture of an intelligent navigation system based on a traffic control platform provided in an embodiment of the present application;

[0030] Figure 2 It is a flow chart of an intelligent navigation method based on a traffic control platform provided in an embodiment of the present application;

[0031] Figure 3 It is a structural schematic diagram of a traffic control platform provided in an embodiment of the present application;

[0032] Figure 4 It is a structural schematic diagram of an intelligent navigation device based on a traffic control platform provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0034] The following are detailed descriptions of each.

[0035] The terms "first", "second", "third" and "fourth" etc. in the specification and claims of the present application and the drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0036] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0037] Below, some terms in this application are explained to facilitate understanding by those skilled in the art.

[0038] In the embodiment of the present application, the traffic control platform may include at least one of the following: a server, a controller, a video matrix, etc., which are not limited here.

[0039] The following is a detailed description of the embodiments of the present application.

[0040] like Figure 1 As shown, Figure 1 This is an architectural diagram of an intelligent navigation system based on a traffic control platform provided in an embodiment of the present application. The intelligent navigation system based on the traffic control platform may include a traffic control platform, at least one vehicle, at least one camera, at least one sensor, etc., which are not limited here.

[0041] In the specific implementation, the traffic control platform and the vehicle can communicate, and the communication method may include at least one of the following: mobile communication (3G, 4G, 5G, 6G, etc.), satellite communication, Bluetooth communication, LoRa communication, millimeter wave communication, zigbee communication, etc., which are not limited here.

[0042] The sensor may include at least one of the following: an environmental sensor, a temperature sensor, an infrared sensor, a meteorological sensor, a humidity sensor, etc., which are not limited here.

[0043] In the embodiment of the present application, the traffic control platform can be used to implement the following functions:

[0044] Receiving a navigation request from a target vehicle, the navigation request carrying a current position and a target position of the target vehicle;

[0045] Generate m navigation routes according to the current location and the target location, where m is an integer greater than 1;

[0046] Obtaining an estimated navigation duration for each of the m navigation routes to obtain m estimated navigation durations;

[0047] Obtaining an estimated cost of each of the m navigation routes to obtain m estimated costs;

[0048] Obtaining a traffic congestion index of each of the m navigation routes in a preset time period between the current time, to obtain m traffic congestion index sets; each of the m traffic congestion index sets includes a plurality of traffic congestion indexes, and each traffic congestion index corresponds to a sampling time;

[0049] Perform fitting according to the m traffic congestion index sets to obtain m fitting results;

[0050] Optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations;

[0051] Determining an evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation times, to obtain m evaluation parameters;

[0052] A maximum value among the m evaluation parameters is selected, and a navigation route corresponding to the maximum value is pushed to the target vehicle.

[0053] It can be seen that in the traffic control platform described in the embodiments of the present application, firstly, each traffic congestion index set reflects the traffic change trend of each navigation route to a certain extent. The traffic change trend is used to make the estimated navigation time more in line with future traffic changes, making the navigation time more accurate. Secondly, each navigation route can be accurately evaluated based on the cost and time that users care about, which helps to push navigation routes accurately, deeply and humanely, and helps to improve user experience. In this way, the intelligence of navigation can be improved.

[0054] like Figure 2 As shown, Figure 2 is a flow chart of an intelligent navigation method based on a traffic control platform provided in an embodiment of the present application, which is applied to Figure 1 The traffic control platform in the intelligent navigation system based on the traffic control platform shown, the method includes:

[0055] 201. Receive a navigation request from a target vehicle, where the navigation request carries a current position and a target position of the target vehicle.

[0056] The navigation request carries the current position and the target position of the target vehicle.

[0057] In an embodiment of the present application, a communication connection can be established between the target vehicle and the traffic control platform, and then the target vehicle can send a navigation request to the traffic control platform, and the traffic control platform can receive the navigation request of the target vehicle, which can be used to request the traffic control platform to generate a navigation route between the current location and the target location.

[0058] 202. Generate m navigation routes according to the current location and the target location, where m is an integer greater than 1.

[0059] In an embodiment of the present application, a navigation route between a current location and a target location may be generated based on a path planning algorithm to obtain m navigation routes, where m is an integer greater than 1.

[0060] 203. Obtain an estimated navigation duration for each of the m navigation routes to obtain m estimated navigation durations.

[0061] In a specific implementation, the estimated navigation time of each of the m navigation routes may be obtained to obtain m estimated navigation times, where the estimated navigation time of each navigation route is based on the time required for the navigation route to travel from the current location to the target location.

[0062] 204. Obtain an estimated cost of each of the m navigation routes to obtain m estimated costs.

[0063] The estimated cost may include at least one of the following: tolls (highway fees, bridge fees, etc.), fuel consumption, power consumption, vehicle damage costs, etc., which are not limited here. Due to different roads, the road conditions (bumpiness) are different, the vehicle damage is different, and thus the vehicle damage costs are also different.

[0064] In the embodiment of the present application, since there may be some toll sections (such as road sections) in the navigation route, the cost of each navigation route may be different. Therefore, the estimated cost of each navigation route in the m navigation routes can be obtained to obtain m estimated costs.

[0065] 205. Obtain a traffic congestion index of each of the m navigation routes in a preset time period between the current time to obtain m traffic congestion index sets; each of the m traffic congestion index sets includes multiple traffic congestion indexes, and each traffic congestion index corresponds to a sampling time.

[0066] The preset time period may be pre-set or set by system default. In a specific implementation, the traffic control platform may be connected to a plurality of monitoring devices, and then, corresponding monitoring data may be obtained, and the monitoring data may be used to analyze the traffic congestion index of each navigation route in real time.

[0067] In an embodiment of the present application, the traffic congestion index of each of the m navigation routes in a preset time period between the current time can be obtained to obtain m traffic congestion index sets, each of the m traffic congestion index sets including multiple traffic congestion indexes, and each traffic congestion index corresponds to a sampling time.

[0068] 206. Perform fitting according to the m traffic congestion index sets to obtain m fitting results.

[0069] In an embodiment of the present application, each traffic congestion index set reflects the result of big data analysis, and each traffic congestion index set of the m traffic congestion index sets includes multiple traffic congestion indices, and each traffic congestion index may correspond to a sampling time, and then, each traffic congestion index and the corresponding sampling time may correspond to a coordinate point, so that multiple coordinate points can be obtained, and then these coordinate points can be mapped to a coordinate system, the horizontal axis of the coordinate system is time and the vertical axis is the traffic congestion index, and then, the coordinate points corresponding to each traffic congestion index set can be fitted to obtain corresponding fitting results, and each fitting result may include a fitting straight line and / or a fitting curve.

[0070] 207. Optimize the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations.

[0071] Each of the m estimated navigation durations may be understood as an estimated navigation duration corresponding to the current moment.

[0072] Among them, the fitting results reflect the changes in the congestion level of each navigation route to a certain extent. Therefore, m estimated navigation times can be optimized based on m fitting results to obtain m target estimated navigation times, so that the estimated navigation time is more in line with future traffic changes and the navigation time is more accurate, thereby improving the intelligence of intelligent navigation.

[0073] Optionally, the first fitting result includes a first fitting straight line and a first fitting curve, and the first fitting result is any fitting result of the m fitting results; the above step 207, optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations, may include the following steps:

[0074] intercepting a fitting curve segment of a first estimated navigation duration after the current moment through the first fitting curve, where the first estimated navigation duration is the estimated navigation duration corresponding to the first fitting result;

[0075] Obtaining a first slope of the first fitting straight line;

[0076] determining a first adjustment parameter corresponding to the first slope;

[0077] Obtaining the maximum value and the minimum value in the fitting curve segment to obtain multiple maximum values ​​and multiple minimum values;

[0078] Determine a target fine-tuning parameter according to the plurality of maximum values ​​and the plurality of minimum values;

[0079] The first estimated navigation duration is adjusted according to the first adjustment parameter and the target fine-tuning parameter to obtain a target estimated navigation duration corresponding to the first fitting result.

[0080] In a specific implementation, taking the first fitting result as an example, the first fitting result is any fitting result among the m fitting results, and the first fitting result includes a first fitting straight line and a first fitting curve. The first estimated navigation duration is the estimated navigation duration corresponding to the first fitting result.

[0081] Specifically, since the first estimated navigation duration is slightly different from its actual navigation duration, the first fitting curve can be used to intercept a fitting curve segment of the first estimated navigation duration after the current moment. The fitting curve segment reflects future traffic trend changes, specifically future traffic trend changes corresponding to its actual navigation duration.

[0082] Furthermore, the first slope of the first fitting straight line can be obtained, and the first slope reflects the changing trend of traffic congestion on the navigation route to a certain extent. The mapping relationship between the preset slope and the adjustment parameter can also be pre-stored, and then the first adjustment parameter corresponding to the first slope can be determined based on the mapping relationship.

[0083] In addition, the maximum value and the minimum value in the fitting curve segment can also be obtained to obtain multiple maxima and multiple minima. The multiple maxima and multiple minima reflect the stability of traffic changes to a certain extent. Then, the target fine-tuning parameters can be determined according to the multiple maxima and multiple minima, that is, the estimated navigation time can be dynamically fine-tuned in combination with the stability of traffic changes. Finally, the first estimated navigation time can be adjusted according to the first adjustment parameter and the target fine-tuning parameter to obtain the target estimated navigation time corresponding to the first fitting result, that is, the target estimated navigation time corresponding to the first fitting result = (1 + first adjustment parameter) * (1 + target fine-tuning parameter) * first estimated navigation time. In this way, on the one hand, the estimated navigation time can be adjusted in combination with the changing trend of traffic congestion, and on the other hand, the estimated navigation time can be dynamically fine-tuned in combination with the stability of traffic changes, so that the final estimated navigation time can be more accurate.

[0084] Optionally, the above step of adjusting the first estimated navigation duration according to the first adjustment parameter and the target fine-tuning parameter to obtain a target estimated navigation duration corresponding to the first fitting result may include the following steps:

[0085] Perform fitting according to the multiple maximum values ​​to obtain a second fitting straight line;

[0086] Perform fitting according to the multiple minimum values ​​to obtain a third fitting straight line;

[0087] Obtaining a second slope of the second fitting straight line;

[0088] Obtaining a third slope of the third fitting straight line;

[0089] determining a first degree of deviation between the second slope and the third slope;

[0090] Determine a first mean square error according to the plurality of maximum values;

[0091] determining a second mean square error according to the plurality of minimum values;

[0092] determining a second deviation between the first mean square error and the second mean square error;

[0093] determining a first fine-tuning parameter corresponding to the first deviation;

[0094] determining a second fine-tuning parameter corresponding to the second deviation;

[0095] The target fine-tuning parameter is determined according to the first fine-tuning parameter and the second fine-tuning parameter.

[0096] In a specific implementation, each maximum value may correspond to a coordinate point, and fitting may be performed based on multiple maximum values ​​to obtain a second fitting straight line. Similarly, each minimum value may correspond to a coordinate point, and fitting may be performed based on multiple minimum values ​​to obtain a third fitting straight line. Furthermore, the second slope of the second fitting straight line may be obtained, and the third slope of the third fitting straight line may also be obtained. Then, the first deviation between the second slope and the third slope may be determined, and the first deviation = |second slope - third slope| / (second slope + third slope). The second slope and the third slope reflect the stability of traffic changes from the horizontal direction, respectively.

[0097] Furthermore, a first mean square error can be determined based on multiple maximum values, and a second mean square error can be determined based on multiple minimum values, and then a second deviation between the first mean square error and the second mean square error can be determined, the second deviation = |first mean square error - second mean square error| / (first mean square error - second mean square error), the first mean square error and the second mean square error respectively reflect the stability of traffic changes from a vertical direction.

[0098] Furthermore, a first mapping relationship between a preset deviation and a fine-tuning parameter can be pre-stored, and then a first fine-tuning parameter corresponding to the first deviation can be determined based on the first mapping relationship. The first mapping relationship between a preset deviation and a fine-tuning parameter can be pre-stored, and a second fine-tuning parameter corresponding to the second deviation can be determined based on the second mapping relationship, and then the target fine-tuning parameter can be determined based on the first fine-tuning parameter and the second fine-tuning parameter. In this way, the final fine-tuning parameter can be determined based on the lateral and longitudinal changes in traffic, which helps to achieve precise fine-tuning, thereby making the final estimated navigation time more accurate.

[0099] Optionally, the first fine-tuning parameter and the second fine-tuning parameter are both positive values; the above step of determining the target fine-tuning parameter according to the first fine-tuning parameter and the second fine-tuning parameter may include the following steps:

[0100] determining a first slope direction of the second slope;

[0101] determining a second slope direction of the third slope;

[0102] When the first slope direction is monotonically upward and the second slope direction is monotonically upward, taking the sum of the first fine-tuning parameter and the second fine-tuning parameter as the target fine-tuning parameter;

[0103] When the first slope direction is monotonically upward and the second slope direction is monotonically downward, taking the difference between the first fine-tuning parameter and the second fine-tuning parameter as the target fine-tuning parameter;

[0104] When the first slope direction is monotonically downward and the second slope direction is monotonically upward, taking the difference between the second fine-tuning parameter and the first fine-tuning parameter as the target fine-tuning parameter;

[0105] When the first slope direction is monotonically downward and the second slope direction is monotonically downward, the inverse of the sum of the first fine-tuning parameter and the second fine-tuning parameter is used as the target fine-tuning parameter.

[0106] In a specific implementation, the first fine-tuning parameter and the second fine-tuning parameter are both positive values.

[0107] In the embodiment of the present application, the first slope direction of the second slope can be determined, and the second slope direction of the third slope can be determined. In a specific implementation, when the first slope direction is monotonically upward and the second slope direction is monotonically upward, the sum of the first fine-tuning parameter and the second fine-tuning parameter is used as the target fine-tuning parameter, that is, the target fine-tuning parameter = first fine-tuning parameter + second fine-tuning parameter. When the first slope direction is monotonically upward and the second slope direction is monotonically downward, the difference between the first fine-tuning parameter and the second fine-tuning parameter is used as the target fine-tuning parameter, that is, the target fine-tuning parameter = first fine-tuning parameter - second fine-tuning parameter. When the first slope direction is monotonically downward and the second slope direction is monotonically upward, the difference between the second fine-tuning parameter and the first fine-tuning parameter is used as the target fine-tuning parameter, that is, the target fine-tuning parameter = second fine-tuning parameter - first fine-tuning parameter. When the first slope direction is monotonically downward and the second slope direction is monotonically downward, the inverse of the sum of the first fine-tuning parameter and the second fine-tuning parameter is used as the target fine-tuning parameter, that is, the target fine-tuning parameter = -(first fine-tuning parameter + second fine-tuning parameter). In this way, the final fine-tuning parameter can be determined based on the lateral and longitudinal changes in traffic changes, which helps to achieve precise fine-tuning, thereby making the final estimated navigation time more accurate.

[0108] 208. Determine an evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation times to obtain m evaluation parameters.

[0109] In specific implementations, users are often most concerned about cost and duration. Therefore, the evaluation parameters of each of the m navigation routes can be determined based on the m estimated costs and the m target estimated navigation times to obtain m evaluation parameters. In this way, each navigation route can be accurately evaluated based on the cost and duration that users are concerned about, which helps to push navigation routes in an accurate, in-depth and humanized manner, and helps to improve user experience.

[0110] Optionally, the above step 208, determining the evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation durations to obtain the m evaluation parameters, may include the following steps:

[0111] Determine a first evaluation parameter corresponding to each of the m estimated costs to obtain m first evaluation parameters;

[0112] Determine a second evaluation parameter corresponding to each of the m target estimated navigation durations to obtain m second evaluation parameters;

[0113] Determine m fees / hour according to the m estimated fees and the m target estimated navigation durations;

[0114] Determine the weight pairs corresponding to the m costs / hours to obtain m weight pairs;

[0115] The m evaluation parameters are determined according to the m weight pairs, the m first evaluation parameters and the m second evaluation parameters.

[0116] In an embodiment of the present application, a mapping relationship between a preset cost and a first evaluation parameter can be pre-stored, and then, based on the mapping relationship, a first evaluation parameter corresponding to each of the m estimated costs can be determined to obtain m first evaluation parameters. In addition, a mapping relationship between an estimated navigation duration and a second evaluation parameter can be pre-stored, and then, based on the mapping relationship, a second evaluation parameter corresponding to each of the m target estimated navigation durations can be determined to obtain m second evaluation parameters.

[0117] Furthermore, m fees / hours can be determined based on the m estimated fees and the m target estimated navigation times, and each fee / hour is equal to the ratio between the corresponding estimated fee and the target estimated navigation time. The smaller the m fees / hours, the higher the cost-effectiveness. Different cost-effectivenesses correspond to different weight pairs. Furthermore, the mapping relationship between preset fees / hours and weight pairs can be pre-stored. Each weight pair can include a first weight and a second weight. The sum of the first weight and the second weight is 1. The first weight is the weight corresponding to the first evaluation parameter, and the second weight is the weight corresponding to the second evaluation parameter.

[0118] Furthermore, based on the mapping relationship, the weight pairs corresponding to the m costs / hours can be determined to obtain m weight pairs, and then the m evaluation parameters can be determined according to the m weight pairs, m first evaluation parameters and m second evaluation parameters, that is, the evaluation parameter = first weight * first evaluation parameter + second weight * second evaluation parameter. In this way, firstly, based on the estimated navigation time, accurate navigation route evaluation can be achieved. Secondly, navigation route evaluation can be achieved in the cost dimension. Thirdly, since the cost / hour reflects the cost-effectiveness to a certain extent, the weight pairs can be dynamically configured based on the cost-effectiveness. Finally, based on the weight pairs, a humanized evaluation of the navigation route can be completed through the two dimensions of estimated navigation time and cost. In this way, it is helpful to accurately evaluate each navigation route in combination with the cost and time that users care about, and it is helpful to accurately, deeply and humanizedly push navigation routes, which helps to improve user experience.

[0119] 209. Select a maximum value among the m evaluation parameters, and push the navigation route corresponding to the maximum value to the target vehicle.

[0120] In the specific implementation, the maximum value of the m evaluation parameters can be selected, and the maximum value can be understood as the navigation route with the highest cost-effectiveness. Then, the navigation route corresponding to the maximum value can be pushed to the target vehicle. Then, each navigation route can be accurately evaluated based on the cost and duration that the user cares about, which helps to realize accurate, in-depth and humanized navigation route push and helps to improve the user experience.

[0121] Optionally, the above step 209, selecting the maximum value of the m evaluation parameters, may include the following steps:

[0122] Obtaining route attribute parameters of each of the m navigation routes to obtain m route attribute parameters;

[0123] Determining relevant attribute parameters of the target vehicle;

[0124] Determining a first feedback adjustment parameter corresponding to the relevant attribute parameter;

[0125] Determine a feedback fine-tuning parameter corresponding to each of the m route attribute parameters to obtain m feedback fine-tuning parameters;

[0126] Performing feedback adjustment on the m evaluation parameters according to the first feedback fine-tuning parameter and the m feedback fine-tuning parameters to obtain m target evaluation parameters;

[0127] Select the maximum value among the m target evaluation parameters.

[0128] Among them, the route attribute parameters may include at least one of the following: route road type, route length, mountain road conditions, number of route traffic lights, route speed limit, route road width, route construction conditions, route road humidity, route road temperature, road visibility, etc., without limitation here.

[0129] The relevant attribute parameters may include the attribute parameters of the driver and / or the attribute parameters of the target vehicle. The attribute parameters of the driver may include at least one of the following: driving experience, driving ability, driver gender, etc., which are not limited here. The attribute parameters of the target vehicle may include at least one of the following: vehicle model, vehicle performance, vehicle maintenance status, etc., which are not limited here.

[0130] In a specific implementation, the route attribute parameters of each navigation route in the m navigation routes can be obtained to obtain m route attribute parameters, and the relevant attribute parameters of the target vehicle can also be determined, and the mapping relationship between the preset attribute parameters and the feedback adjustment parameters can be pre-stored, and then, the first feedback adjustment parameter corresponding to the relevant attribute parameter can be determined based on the mapping relationship, and the mapping relationship between the preset route attribute parameters and the feedback adjustment parameters can also be pre-stored, and then, the feedback fine-tuning parameter corresponding to each route attribute parameter in the m route attribute parameters can be determined based on the mapping relationship to obtain m feedback fine-tuning parameters, and then the m evaluation parameters are feedback-adjusted according to the first feedback fine-tuning parameter and the m feedback fine-tuning parameters to obtain m target evaluation parameters. , that is, target evaluation parameter = (1 + first feedback fine-tuning parameter) * (1 + feedback fine-tuning parameter) * evaluation parameter, and then, the maximum value of the m target evaluation parameters can be selected. In this way, on the one hand, since the route attribute parameters reflect the characteristics of the route to a certain extent, the evaluation parameters can be adjusted based on the characteristic feedback of the route. On the other hand, the relevant attribute parameters of the vehicle reflect the relevant characteristics of the vehicle or the driver to a certain extent, and then, the evaluation parameters can be deeply fine-tuned based on the relevant characteristics of the vehicle or the driver. The final evaluation parameter depth conforms to the actual situation, and each navigation route can be deeply combined with the cost and duration that users care about to accurately evaluate, thereby accurately, deeply and humanely realizing navigation route push, which helps to improve user experience.

[0131] It can be seen that the intelligent navigation method based on the traffic control platform described in the embodiment of the present application is applied to the traffic control platform, receives a navigation request of a target vehicle, the navigation request carries the current position and the target position of the target vehicle, generates m navigation routes according to the current position and the target position, m is an integer greater than 1, obtains the estimated navigation time of each of the m navigation routes, obtains m estimated navigation times, obtains the estimated cost of each of the m navigation routes, obtains m estimated costs, obtains the traffic congestion index of each of the m navigation routes in a preset time period between the current time, obtains m traffic congestion index sets; each of the m traffic congestion index sets includes multiple traffic congestion indexes, each traffic congestion index corresponds to a sampling time, and is simulated according to the m traffic congestion index sets. The m fitting results are obtained, and m estimated navigation times are optimized according to the m fitting results to obtain m target estimated navigation times. Evaluation parameters of each of the m navigation routes are determined according to the m estimated costs and the m target estimated navigation times to obtain m evaluation parameters. The maximum value of the m evaluation parameters is selected, and the navigation route corresponding to the maximum value is pushed to the target vehicle. In this way, firstly, each traffic congestion index set reflects the traffic change trend of each navigation route to a certain extent. The traffic change trend is used to make the estimated navigation time more in line with future traffic changes, making the navigation time more accurate. Secondly, each navigation route can be accurately evaluated in combination with the cost and time that users care about, which is helpful to realize navigation route push in an accurate, in-depth and humanized manner, and helps to improve user experience. In this way, the intelligence of navigation can be improved.

[0132] In accordance with the above embodiment, please refer to Figure 3 , Figure 3 : is a structural diagram of a traffic control platform provided in an embodiment of the present application. As shown in the figure, the traffic control platform includes a processor, a memory, a communication interface, and one or more programs. The one or more programs are stored in the memory and configured to be executed by the processor. In the embodiment of the present application, the program includes instructions for executing the following steps:

[0133] Receiving a navigation request from a target vehicle, the navigation request carrying a current position and a target position of the target vehicle;

[0134] Generate m navigation routes according to the current location and the target location, where m is an integer greater than 1;

[0135] Obtaining an estimated navigation duration for each of the m navigation routes to obtain m estimated navigation durations;

[0136] Obtaining an estimated cost of each of the m navigation routes to obtain m estimated costs;

[0137] Obtaining a traffic congestion index of each of the m navigation routes in a preset time period between the current time, to obtain m traffic congestion index sets; each of the m traffic congestion index sets includes a plurality of traffic congestion indexes, and each traffic congestion index corresponds to a sampling time;

[0138] Perform fitting according to the m traffic congestion index sets to obtain m fitting results;

[0139] Optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations;

[0140] Determining an evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation times, to obtain m evaluation parameters;

[0141] A maximum value among the m evaluation parameters is selected, and a navigation route corresponding to the maximum value is pushed to the target vehicle.

[0142] Optionally, the first fitting result includes a first fitting straight line and a first fitting curve, and the first fitting result is any fitting result of the m fitting results; in optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations, the program includes instructions for executing the following steps:

[0143] intercepting a fitting curve segment of a first estimated navigation duration after the current moment through the first fitting curve, where the first estimated navigation duration is the estimated navigation duration corresponding to the first fitting result;

[0144] Obtaining a first slope of the first fitting straight line;

[0145] determining a first adjustment parameter corresponding to the first slope;

[0146] Obtaining the maximum value and the minimum value in the fitting curve segment to obtain multiple maximum values ​​and multiple minimum values;

[0147] Determine a target fine-tuning parameter according to the plurality of maximum values ​​and the plurality of minimum values;

[0148] The first estimated navigation duration is adjusted according to the first adjustment parameter and the target fine-tuning parameter to obtain a target estimated navigation duration corresponding to the first fitting result.

[0149] Optionally, in the aspect of adjusting the first estimated navigation duration according to the first adjustment parameter and the target fine-tuning parameter to obtain a target estimated navigation duration corresponding to the first fitting result, the program includes instructions for executing the following steps:

[0150] Perform fitting according to the multiple maximum values ​​to obtain a second fitting straight line;

[0151] Perform fitting according to the multiple minimum values ​​to obtain a third fitting straight line;

[0152] Obtaining a second slope of the second fitting straight line;

[0153] Obtaining a third slope of the third fitting straight line;

[0154] determining a first degree of deviation between the second slope and the third slope;

[0155] Determine a first mean square error according to the plurality of maximum values;

[0156] determining a second mean square error according to the plurality of minimum values;

[0157] determining a second deviation between the first mean square error and the second mean square error;

[0158] determining a first fine-tuning parameter corresponding to the first deviation;

[0159] determining a second fine-tuning parameter corresponding to the second deviation;

[0160] The target fine-tuning parameter is determined according to the first fine-tuning parameter and the second fine-tuning parameter.

[0161] Optionally, the first fine-tuning parameter and the second fine-tuning parameter are both positive values; in determining the target fine-tuning parameter according to the first fine-tuning parameter and the second fine-tuning parameter, the program includes instructions for executing the following steps:

[0162] determining a first slope direction of the second slope;

[0163] determining a second slope direction of the third slope;

[0164] When the first slope direction is monotonically upward and the second slope direction is monotonically upward, taking the sum of the first fine-tuning parameter and the second fine-tuning parameter as the target fine-tuning parameter;

[0165] When the first slope direction is monotonically upward and the second slope direction is monotonically downward, taking the difference between the first fine-tuning parameter and the second fine-tuning parameter as the target fine-tuning parameter;

[0166] When the first slope direction is monotonically downward and the second slope direction is monotonically upward, taking the difference between the second fine-tuning parameter and the first fine-tuning parameter as the target fine-tuning parameter;

[0167] When the first slope direction is monotonically downward and the second slope direction is monotonically downward, the inverse of the sum of the first fine-tuning parameter and the second fine-tuning parameter is used as the target fine-tuning parameter.

[0168] Optionally, in the aspect of determining the evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation durations to obtain the m evaluation parameters, the program includes instructions for executing the following steps:

[0169] Determine a first evaluation parameter corresponding to each of the m estimated costs to obtain m first evaluation parameters;

[0170] Determine a second evaluation parameter corresponding to each of the m target estimated navigation durations to obtain m second evaluation parameters;

[0171] Determine m fees / hour according to the m estimated fees and the m target estimated navigation durations;

[0172] Determine the weight pairs corresponding to the m costs / hours to obtain m weight pairs;

[0173] The m evaluation parameters are determined according to the m weight pairs, the m first evaluation parameters and the m second evaluation parameters.

[0174] It can be seen that, in the traffic control platform described in the embodiment of the present application, a navigation request of a target vehicle is received, the navigation request carries the current position and the target position of the target vehicle, m navigation routes are generated according to the current position and the target position, m is an integer greater than 1, an estimated navigation time of each of the m navigation routes is obtained, and m estimated navigation times are obtained, an estimated cost of each of the m navigation routes is obtained, and m estimated costs are obtained, a traffic congestion index of each of the m navigation routes in a preset time period between the current time is obtained, and m traffic congestion index sets are obtained; each of the m traffic congestion index sets includes multiple traffic congestion indexes, each traffic congestion index corresponds to a sampling time, and fitting is performed according to the m traffic congestion index sets to obtain m fittings. As a result, m estimated navigation times are optimized according to m fitting results to obtain m target estimated navigation times, and evaluation parameters of each of the m navigation routes are determined according to the m estimated costs and the m target estimated navigation times to obtain m evaluation parameters, and the maximum value of the m evaluation parameters is selected, and the navigation route corresponding to the maximum value is pushed to the target vehicle. In this way, firstly, each traffic congestion index set reflects the traffic change trend of each navigation route to a certain extent, and the traffic change trend is used to make the estimated navigation time more in line with future traffic changes, making the navigation time more accurate. Secondly, each navigation route can be accurately evaluated in combination with the cost and time that users care about, which is helpful to realize the navigation route push in an accurate, in-depth and humanized manner, and help to improve the user experience. In this way, the intelligence of navigation can be improved.

[0175] Figure 4 This is a functional unit block diagram of the intelligent navigation device 400 based on the traffic control platform involved in the embodiment of the present application. The intelligent navigation device 400 based on the traffic control platform is applied to the traffic control platform, and the intelligent navigation device 400 based on the traffic control platform includes: a receiving unit 401, a generating unit 402, an acquiring unit 403, a fitting unit 404, an optimizing unit 405, a determining unit 406 and a selecting unit 407, wherein:

[0176] The receiving unit 401 is used to receive a navigation request of a target vehicle, wherein the navigation request carries a current position and a target position of the target vehicle;

[0177] The generating unit 402 is used to generate m navigation routes according to the current position and the target position, where m is an integer greater than 1;

[0178] The acquisition unit 403 is used to acquire the estimated navigation duration of each of the m navigation routes to obtain m estimated navigation durations; acquire the estimated cost of each of the m navigation routes to obtain m estimated costs; acquire the traffic congestion index of each of the m navigation routes in a preset time period between the current time to obtain m traffic congestion index sets; each of the m traffic congestion index sets includes multiple traffic congestion indexes, and each traffic congestion index corresponds to a sampling time;

[0179] The fitting unit 404 is used to perform fitting according to the m traffic congestion index sets to obtain m fitting results;

[0180] The optimization unit 405 is used to optimize the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations;

[0181] The determining unit 406 is used to determine the evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation durations, so as to obtain m evaluation parameters;

[0182] The selection unit 407 is used to select a maximum value among the m evaluation parameters, and push the navigation route corresponding to the maximum value to the target vehicle.

[0183] Optionally, the first fitting result includes a first fitting straight line and a first fitting curve, and the first fitting result is any fitting result of the m fitting results; in optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations, the optimization unit 405 is specifically used to:

[0184] intercepting a fitting curve segment of a first estimated navigation duration after the current moment through the first fitting curve, where the first estimated navigation duration is the estimated navigation duration corresponding to the first fitting result;

[0185] Obtaining a first slope of the first fitting straight line;

[0186] determining a first adjustment parameter corresponding to the first slope;

[0187] Obtaining the maximum value and the minimum value in the fitting curve segment to obtain multiple maximum values ​​and multiple minimum values;

[0188] Determine a target fine-tuning parameter according to the plurality of maximum values ​​and the plurality of minimum values;

[0189] The first estimated navigation duration is adjusted according to the first adjustment parameter and the target fine-tuning parameter to obtain a target estimated navigation duration corresponding to the first fitting result.

[0190] Optionally, in adjusting the first estimated navigation duration according to the first adjustment parameter and the target fine-tuning parameter to obtain a target estimated navigation duration corresponding to the first fitting result, the optimization unit 405 is specifically used to:

[0191] Perform fitting according to the multiple maximum values ​​to obtain a second fitting straight line;

[0192] Perform fitting according to the multiple minimum values ​​to obtain a third fitting straight line;

[0193] Obtaining a second slope of the second fitting straight line;

[0194] Obtaining a third slope of the third fitting straight line;

[0195] determining a first degree of deviation between the second slope and the third slope;

[0196] Determine a first mean square error according to the plurality of maximum values;

[0197] determining a second mean square error according to the plurality of minimum values;

[0198] determining a second deviation between the first mean square error and the second mean square error;

[0199] determining a first fine-tuning parameter corresponding to the first deviation;

[0200] determining a second fine-tuning parameter corresponding to the second deviation;

[0201] The target fine-tuning parameter is determined according to the first fine-tuning parameter and the second fine-tuning parameter.

[0202] Optionally, the first fine-tuning parameter and the second fine-tuning parameter are both positive values; in determining the target fine-tuning parameter according to the first fine-tuning parameter and the second fine-tuning parameter, the optimization unit 405 is specifically configured to:

[0203] determining a first slope direction of the second slope;

[0204] determining a second slope direction of the third slope;

[0205] When the first slope direction is monotonically upward and the second slope direction is monotonically upward, taking the sum of the first fine-tuning parameter and the second fine-tuning parameter as the target fine-tuning parameter;

[0206] When the first slope direction is monotonically upward and the second slope direction is monotonically downward, taking the difference between the first fine-tuning parameter and the second fine-tuning parameter as the target fine-tuning parameter;

[0207] When the first slope direction is monotonically downward and the second slope direction is monotonically upward, taking the difference between the second fine-tuning parameter and the first fine-tuning parameter as the target fine-tuning parameter;

[0208] When the first slope direction is monotonically downward and the second slope direction is monotonically downward, the inverse of the sum of the first fine-tuning parameter and the second fine-tuning parameter is used as the target fine-tuning parameter.

[0209] Optionally, in determining the evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation durations to obtain the m evaluation parameters, the selection unit 407 is specifically used to:

[0210] Determine a first evaluation parameter corresponding to each of the m estimated costs to obtain m first evaluation parameters;

[0211] Determine a second evaluation parameter corresponding to each of the m target estimated navigation durations to obtain m second evaluation parameters;

[0212] Determine m fees / hour according to the m estimated fees and the m target estimated navigation durations;

[0213] Determine the weight pairs corresponding to the m costs / hours to obtain m weight pairs;

[0214] The m evaluation parameters are determined according to the m weight pairs, the m first evaluation parameters and the m second evaluation parameters.

[0215] It can be seen that the intelligent navigation device based on the traffic control platform described in the embodiment of the present application is applied to the traffic control platform, receives a navigation request of a target vehicle, the navigation request carries the current position and the target position of the target vehicle, generates m navigation routes according to the current position and the target position, m is an integer greater than 1, obtains the estimated navigation time of each of the m navigation routes, obtains m estimated navigation times, obtains the estimated cost of each of the m navigation routes, obtains m estimated costs, obtains the traffic congestion index of each of the m navigation routes in a preset time period between the current time, obtains m traffic congestion index sets; each of the m traffic congestion index sets includes multiple traffic congestion indexes, each traffic congestion index corresponds to a sampling time, and is simulated according to the m traffic congestion index sets. The m fitting results are obtained, and m estimated navigation times are optimized according to the m fitting results to obtain m target estimated navigation times. Evaluation parameters of each of the m navigation routes are determined according to the m estimated costs and the m target estimated navigation times to obtain m evaluation parameters. The maximum value of the m evaluation parameters is selected, and the navigation route corresponding to the maximum value is pushed to the target vehicle. In this way, firstly, each traffic congestion index set reflects the traffic change trend of each navigation route to a certain extent. The traffic change trend is used to make the estimated navigation time more in line with future traffic changes, making the navigation time more accurate. Secondly, each navigation route can be accurately evaluated in combination with the cost and time that users care about, which is helpful to realize navigation route push in an accurate, in-depth and humanized manner, and helps to improve user experience. In this way, the intelligence of navigation can be improved.

[0216] It can be understood that the functions of each program module of the intelligent navigation device based on the traffic control platform of this embodiment can be specifically implemented according to the method in the above method embodiment. The specific implementation process can refer to the relevant description of the above method embodiment, which will not be repeated here.

[0217] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method recorded in the above method embodiment, and the above computer includes a traffic control platform.

[0218] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes a traffic control platform.

[0219] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0220] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0221] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0222] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0223] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0224] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the above-mentioned methods of each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or CD-ROM and other media that can store program codes.

[0225] A person skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, and the memory can include: a flash drive, a read-only memory (English: Read-Only Memory, abbreviated as: ROM), a random access memory (English: Random Access Memory, abbreviated as: RAM), a magnetic disk or an optical disk, etc.

[0226] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. An intelligent navigation method based on a traffic control platform, characterized in that: Applied to a traffic control platform, the method comprises: Receiving a navigation request from a target vehicle, the navigation request carrying a current position and a target position of the target vehicle; Generate m navigation routes according to the current location and the target location, where m is an integer greater than 1; Obtaining an estimated navigation duration for each of the m navigation routes to obtain m estimated navigation durations; Obtaining an estimated cost of each of the m navigation routes to obtain m estimated costs; Obtaining a traffic congestion index of each of the m navigation routes in a preset time period between the current time, to obtain m traffic congestion index sets; each of the m traffic congestion index sets includes a plurality of traffic congestion indexes, and each traffic congestion index corresponds to a sampling time; Perform fitting according to the m traffic congestion index sets to obtain m fitting results; Optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations; Determining an evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation times, to obtain m evaluation parameters; A maximum value among the m evaluation parameters is selected, and a navigation route corresponding to the maximum value is pushed to the target vehicle.

2. The method according to claim 1, characterized in that The first fitting result includes a first fitting straight line and a first fitting curve, wherein the first fitting result is any fitting result of the m fitting results; and optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations includes: intercepting a fitting curve segment of a first estimated navigation duration after the current moment through the first fitting curve, where the first estimated navigation duration is the estimated navigation duration corresponding to the first fitting result; Obtaining a first slope of the first fitting straight line; determining a first adjustment parameter corresponding to the first slope; Obtaining the maximum value and the minimum value in the fitting curve segment to obtain multiple maximum values ​​and multiple minimum values; Determine a target fine-tuning parameter according to the plurality of maximum values ​​and the plurality of minimum values; The first estimated navigation duration is adjusted according to the first adjustment parameter and the target fine-tuning parameter to obtain a target estimated navigation duration corresponding to the first fitting result.

3. The method according to claim 2, characterized in that The adjusting the first estimated navigation duration according to the first adjustment parameter and the target fine-tuning parameter to obtain a target estimated navigation duration corresponding to the first fitting result includes: Perform fitting according to the multiple maximum values ​​to obtain a second fitting straight line; Perform fitting according to the multiple minimum values ​​to obtain a third fitting straight line; Obtaining a second slope of the second fitting straight line; Obtaining a third slope of the third fitting straight line; determining a first degree of deviation between the second slope and the third slope; Determine a first mean square error according to the plurality of maximum values; determining a second mean square error according to the plurality of minimum values; determining a second deviation between the first mean square error and the second mean square error; determining a first fine-tuning parameter corresponding to the first deviation; determining a second fine-tuning parameter corresponding to the second deviation; The target fine-tuning parameter is determined according to the first fine-tuning parameter and the second fine-tuning parameter.

4. The method according to claim 3, characterized in that The first fine-tuning parameter and the second fine-tuning parameter are both positive values; The determining the target fine-tuning parameter according to the first fine-tuning parameter and the second fine-tuning parameter includes: determining a first slope direction of the second slope; determining a second slope direction of the third slope; When the first slope direction is monotonically upward and the second slope direction is monotonically upward, taking the sum of the first fine-tuning parameter and the second fine-tuning parameter as the target fine-tuning parameter; When the first slope direction is monotonically upward and the second slope direction is monotonically downward, taking the difference between the first fine-tuning parameter and the second fine-tuning parameter as the target fine-tuning parameter; When the first slope direction is monotonically downward and the second slope direction is monotonically upward, taking the difference between the second fine-tuning parameter and the first fine-tuning parameter as the target fine-tuning parameter; When the first slope direction is monotonically downward and the second slope direction is monotonically downward, the inverse of the sum of the first fine-tuning parameter and the second fine-tuning parameter is used as the target fine-tuning parameter.

5. The method according to any one of claims 1 to 4, characterized in that: The step of determining the evaluation parameters of each of the m navigation routes according to the m estimated costs and the m target estimated navigation durations to obtain m evaluation parameters includes: Determine a first evaluation parameter corresponding to each of the m estimated costs to obtain m first evaluation parameters; Determine a second evaluation parameter corresponding to each of the m target estimated navigation durations to obtain m second evaluation parameters; Determine m fees / hour according to the m estimated fees and the m target estimated navigation durations; Determine the weight pairs corresponding to the m costs / hours to obtain m weight pairs; The m evaluation parameters are determined according to the m weight pairs, the m first evaluation parameters and the m second evaluation parameters.

6. An intelligent navigation device based on a traffic control platform, characterized in that: Applied to a traffic control platform, the device comprises: a receiving unit, a generating unit, an acquiring unit, a fitting unit, an optimizing unit, a determining unit and a selecting unit, wherein: The receiving unit is used to receive a navigation request of a target vehicle, wherein the navigation request carries a current position and a target position of the target vehicle; The generating unit is used to generate m navigation routes according to the current position and the target position, where m is an integer greater than 1; The acquisition unit is used to acquire the estimated navigation duration of each of the m navigation routes to obtain m estimated navigation durations; acquire the estimated cost of each of the m navigation routes to obtain m estimated costs; acquire the traffic congestion index of each of the m navigation routes in a preset time period between the current time to obtain m traffic congestion index sets; each of the m traffic congestion index sets includes a plurality of traffic congestion indexes, and each traffic congestion index corresponds to a sampling time; The fitting unit is used to perform fitting according to the m traffic congestion index sets to obtain m fitting results; The optimization unit is used to optimize the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations; The determining unit is used to determine the evaluation parameter of each of the m navigation routes according to the m estimated costs and the m target estimated navigation durations, so as to obtain m evaluation parameters; The selection unit is used to select a maximum value among the m evaluation parameters and push the navigation route corresponding to the maximum value to the target vehicle.

7. The device according to claim 6, characterized in that The first fitting result includes a first fitting straight line and a first fitting curve, and the first fitting result is any fitting result of the m fitting results; in optimizing the m estimated navigation durations according to the m fitting results to obtain m target estimated navigation durations, the optimization unit is specifically used to: intercepting a fitting curve segment of a first estimated navigation duration after the current moment through the first fitting curve, where the first estimated navigation duration is the estimated navigation duration corresponding to the first fitting result; Obtaining a first slope of the first fitting straight line; determining a first adjustment parameter corresponding to the first slope; Obtaining the maximum value and the minimum value in the fitting curve segment to obtain multiple maximum values ​​and multiple minimum values; Determine a target fine-tuning parameter according to the plurality of maximum values ​​and the plurality of minimum values; The first estimated navigation duration is adjusted according to the first adjustment parameter and the target fine-tuning parameter to obtain a target estimated navigation duration corresponding to the first fitting result.

8. The device according to claim 7, characterized in that In the aspect of adjusting the first estimated navigation duration according to the first adjustment parameter and the target fine-tuning parameter to obtain the target estimated navigation duration corresponding to the first fitting result, the optimization unit is specifically used for: Perform fitting according to the multiple maximum values ​​to obtain a second fitting straight line; Perform fitting according to the multiple minimum values ​​to obtain a third fitting straight line; Obtaining a second slope of the second fitting straight line; Obtaining a third slope of the third fitting straight line; determining a first degree of deviation between the second slope and the third slope; Determine a first mean square error according to the plurality of maximum values; determining a second mean square error according to the plurality of minimum values; determining a second deviation between the first mean square error and the second mean square error; determining a first fine-tuning parameter corresponding to the first deviation; determining a second fine-tuning parameter corresponding to the second deviation; The target fine-tuning parameter is determined according to the first fine-tuning parameter and the second fine-tuning parameter.

9. A traffic control platform, characterized in that: The method comprises a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 5.

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