Unmanned logistics distribution system based on Beidou navigation

Abstract

The invention discloses an unmanned logistics distribution system based on Beidou navigation, and belongs to the technical field of logistics distribution. Data statistics and processing analysis are carried out from the aspect of navigation signal intensity to obtain real-time navigation signal conditions of different distribution vehicles of different service providers in different distribution ways, and reliable local data support can be provided for navigation signal abnormal influence analysis and data sharing of subsequent different abnormal areas; different abnormal navigation data appearing in the navigation abnormal area are integrated and calculated, and the navigation abnormal influence state is evaluated and classified, so that the prompt sharing effect and the avoidance processing effect of different navigation abnormal areas are improved; the method is used for solving the technical problems that in an existing scheme, monitoring statistics and analysis of abnormal data cannot be carried out in the unmanned logistics distribution process, abnormal data analysis results provided by different targets cannot be integrated to carry out abnormal influence evaluation on different abnormal areas, and targeted dynamic alarm prompting cannot be carried out.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics and distribution, and in particular to an unmanned logistics and distribution system based on Beidou navigation. Background Art

[0002] Unmanned logistics and delivery refers to the transportation and delivery of goods through computer systems, automated equipment, intelligent algorithms and other technologies without the direct participation of people. The realization of unmanned logistics and delivery requires the use of advanced computer systems, sensors, positioning technologies, intelligent algorithms and other technical means. Through the synergy of these technical means, the automated, accurate and safe transportation and delivery of goods can be achieved.

[0003] Although unmanned logistics delivery has many advantages, there are still some problems in practical applications. For example, when an unmanned logistics delivery vehicle encounters an abnormality during the delivery process, only emergency response measures are taken for emergency handling. It is impossible to implement abnormal data monitoring and analysis and data sharing for the abnormal process of unmanned logistics delivery, and integrate the abnormal data analysis results provided by different targets to evaluate the abnormal impact of different abnormal areas and provide targeted dynamic alarm prompts. Summary of the Invention

[0004] The purpose of the present invention is to provide an unmanned logistics distribution system based on Beidou navigation, which is used to solve the technical problem that the existing solutions cannot implement monitoring, statistics and analysis of abnormal data in the unmanned logistics distribution process, and integrate the abnormal data analysis results provided by different targets to evaluate the abnormal impact of different abnormal areas and provide targeted dynamic alarm prompts.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] An unmanned logistics delivery system based on Beidou navigation includes a target delivery navigation anomaly monitoring and statistics module for collecting and processing data on real-time navigation anomalies of unmanned logistics delivery by different service providers that have joined the unmanned delivery sharing platform; including:

[0007] Obtain basic information corresponding to the delivery vehicle, including the vehicle number and the service provider to which it belongs; when compiling data statistics on real-time abnormal situations of different unmanned logistics delivery operations in terms of navigation signal strength, obtain the real-time navigation signal strength of the delivery vehicle during each delivery; when conducting an effectiveness assessment of the obtained real-time navigation signal strength, compare and classify the real-time navigation signal strength with the preset standard navigation signal strength;

[0008] If the navigation signal strength is not less than the standard navigation signal strength, a normal navigation tag is generated and its associated navigation identifier value is set to 0;

[0009] If the navigation signal strength is less than the standard navigation signal strength and the duration is less than the standard duration, a navigation slight anomaly label is generated and its associated navigation identifier value is set to 1, and the route traveled by the delivery vehicle during this duration is marked as a navigation slight anomaly area;

[0010] If the navigation signal strength is less than the standard navigation signal strength and the duration is not less than the standard duration, a severe navigation anomaly label is generated and its associated navigation identifier value is set to 2. The route traveled by the delivery vehicle during this duration is marked as a severe navigation anomaly area.

[0011] Arrange the real-time navigation identification values ​​of the delivery vehicles in chronological order to obtain a navigation identification array and upload it to the unmanned delivery sharing platform in real time;

[0012] The target delivery navigation anomaly assessment management module is used to conduct navigation anomaly impact assessment on different abnormal areas in different delivery routes based on the navigation identification arrays uploaded by different delivery vehicles to the unmanned delivery sharing platform, and to provide dynamic warning prompts from the navigation aspect for subsequent delivery routes of different delivery vehicles based on the assessment results.

[0013] Preferably, all navigation identification arrays and delivery identification arrays uploaded to the unmanned delivery sharing platform by all unmanned logistics delivery vehicles of different service providers are obtained, and all navigation mild abnormality areas, navigation severe abnormality areas and traffic abnormality areas are traversed and counted respectively;

[0014] When performing anomaly impact assessment on all navigation anomaly areas that appear, all anomaly areas are sorted in descending order according to the total number of times different anomaly areas appear, and the corresponding anomaly weight YQk is obtained according to the type of anomaly label each time, where k = 1, 2; and the corresponding duration Tk is obtained according to the navigation identifier values ​​1 and 2 in the navigation identifier array;

[0015] By the formula φ=∑ Z 1YQk×Tk is used to calculate the navigation anomaly impact coefficient φ corresponding to the navigation anomaly area; where Z is the total number of navigation mild anomaly labels and navigation severe anomaly labels appearing in the anomaly area.

[0016] Preferably, when evaluating the navigation anomaly impact status of different navigation anomaly areas according to the navigation anomaly impact coefficient, the navigation anomaly impact coefficient is compared with a preset navigation anomaly impact threshold for classification;

[0017] Marking the navigation anomaly area corresponding to the navigation anomaly impact coefficient not greater than the navigation anomaly impact threshold as a low-impact navigation area;

[0018] and marking the navigation anomaly area corresponding to the navigation anomaly impact coefficient greater than the navigation anomaly impact threshold as a high-impact navigation area;

[0019] Low-impact navigation areas or high-impact navigation areas constitute navigation anomaly impact analysis data and are uploaded to the unmanned delivery sharing platform in real time. They are then synchronously shared with all service providers through the unmanned delivery sharing platform and targeted navigation optimization warning prompts are implemented for navigation anomaly areas.

[0020] Preferably, the target delivery traffic anomaly monitoring and statistics module is used to collect data statistics and process the real-time traffic anomalies of unmanned logistics delivery of different service providers that have joined the unmanned delivery sharing platform.

[0021] Preferably, when performing data statistics on real-time abnormal situations of different unmanned logistics deliveries in terms of residence time, a monitoring instruction is generated when the delivery vehicle's speed is zero during the delivery process, and the duration of the zero speed is counted according to the monitoring instruction and the positioning is performed simultaneously, and the duration of the zero speed counted by monitoring is compared and classified.

[0022] Preferably, if the duration is not greater than the standard duration, a first avoidance normal tag is generated and the value of the delivery identifier associated with the tag is set to 0;

[0023] If the duration is longer than the standard duration and the positioning area belongs to the pre-set whitelist area table, a second avoidance normal tag is generated and its associated delivery identification value is set to 1;

[0024] If the duration is longer than the standard duration and the positioning area does not belong to the pre-set whitelist area table, an avoidance exception label is generated and its associated delivery identifier value is set to 2, and the corresponding area is marked as a traffic exception area according to the avoidance exception label;

[0025] The delivery identification values ​​that appear in the delivery process of the delivery vehicle are arranged and combined in chronological order to obtain a delivery identification array and uploaded to the unmanned delivery sharing platform in real time.

[0026] Preferably, the target delivery traffic anomaly assessment management module is used to implement traffic anomaly impact assessment on different abnormal areas in different delivery routes based on the delivery identification arrays uploaded by different delivery vehicles to the unmanned delivery sharing platform, and to provide dynamic warning prompts for subsequent delivery routes of different delivery vehicles in terms of traffic based on the assessment results.

[0027] Preferably, when performing abnormal impact assessment on all abnormal traffic areas that appear, the total number of times the same abnormal traffic area appears is counted based on the delivery identification array, the average stay time of the abnormal traffic area is obtained based on all the stay times of the delivery vehicles braking in the abnormal traffic area, and an assessment classification is performed to obtain the abnormal traffic impact analysis data consisting of low-impact traffic areas or high-impact traffic areas and upload it to the unmanned delivery sharing platform in real time. It is then synchronously shared to all service providers through the unmanned delivery sharing platform and targeted route optimization alarm prompts are implemented for abnormal traffic areas.

[0028] Preferably, the abnormal traffic area with a total number of occurrences greater than K and an average stay time not greater than a stay time threshold is marked as a low-impact traffic area, where K is a positive integer;

[0029] And the traffic abnormality areas with a total occurrence number greater than K and an average stay time greater than the stay time threshold are marked as high-impact traffic areas.

[0030] Compared with the existing solutions, the present invention achieves the following beneficial effects:

[0031] The present invention performs data statistics and processing analysis based on the navigation signal strength, which can not only obtain the real-time navigation signal conditions of different delivery vehicles of different service providers on different delivery routes, but also provide reliable local data support for the subsequent analysis of the abnormal impact of navigation signals in different abnormal areas and the sharing of abnormal navigation signals, thereby improving the reliability and diversity of unmanned logistics distribution in terms of navigation signal monitoring and analysis; by integrating and calculating different abnormal navigation data appearing in the navigation abnormality area and evaluating and classifying its navigation abnormality impact status, more accurate and reliable abnormal navigation status and specific location sharing and alarm prompts can be implemented, so that the unmanned delivery vehicles of other service providers can prepare navigation preparation processing solutions in advance to improve the prompt sharing effect and avoidance processing effect of different navigation abnormality areas.

[0032] The present invention can obtain the real-time stop status of different delivery vehicles of different service providers on different delivery routes by performing data statistics and processing analysis based on the stop duration, and can also provide reliable local data support for subsequent stop abnormality impact analysis in different abnormal areas and sharing of abnormal stop areas, thereby improving the reliability and diversity of monitoring and analysis of unmanned logistics distribution in terms of passage and stop; by calculating the stop duration of all delivery vehicles braking in the passage abnormality area to obtain the average stop duration of the passage abnormality area, and evaluating and classifying the passage abnormality impact status of the passage abnormality area according to the average stay duration, more accurate and reliable sharing and alarm prompts of abnormal passage status and specific locations can be implemented, so that unmanned delivery vehicles of other service providers can prepare route preparation plans in advance to improve the prompt sharing effect and avoidance processing effect of different passage abnormality areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The present invention will be further described below with reference to the accompanying drawings.

[0034] Figure 1 This is a module block diagram of an unmanned logistics distribution system based on Beidou navigation in the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary operation and maintenance personnel in this field without making any creative efforts are within the scope of protection of the present invention.

[0036] Example 1: Figure 1 As shown, the present invention is an unmanned logistics distribution system based on Beidou navigation, including a target distribution navigation anomaly monitoring and statistics module, a target distribution navigation anomaly assessment and management module and an unmanned distribution sharing platform;

[0037] The target delivery navigation anomaly monitoring and statistics module is used to collect data statistics and process real-time navigation anomalies of unmanned logistics delivery by different service providers that have joined the unmanned delivery sharing platform; it includes:

[0038] Obtain basic information corresponding to the delivery vehicle, including the vehicle number and the service provider to which it belongs, including but not limited to various express delivery service providers and food delivery service providers; when collecting data statistics on real-time abnormal situations of different unmanned logistics deliveries in terms of navigation signal strength, the navigation signal strength can be the signal strength based on Beidou navigation, and Beidou navigation is used to navigate the delivery route of the unmanned logistics delivery vehicle, and the real-time navigation signal strength of the delivery vehicle during each delivery is obtained. The real-time navigation signal strength is obtained based on existing navigation equipment. When implementing an effectiveness evaluation of the obtained real-time navigation signal strength, the real-time navigation signal strength is compared and classified with the preset standard navigation signal strength; the standard navigation signal strength is determined based on the existing navigation signal design parameters;

[0039] If the navigation signal strength is not less than the standard navigation signal strength, a normal navigation tag is generated and its associated navigation identifier value is set to 0;

[0040] If the navigation signal strength is less than the standard navigation signal strength and the duration is less than the standard duration, a navigation slight anomaly tag is generated and its associated navigation identifier value is set to 1, and the route traveled by the delivery vehicle during this duration is marked as a navigation slight anomaly area; the duration is in seconds, and the comparison is performed here by extracting the duration value. The standard duration is determined according to the design requirements of the specific delivery vehicle. The navigation slight anomaly tag indicates that the navigation signal on the corresponding road section is temporarily unusable.

[0041] If the navigation signal strength is less than the standard navigation signal strength and the duration is not less than the standard duration, a severe navigation anomaly tag is generated and its associated navigation identifier value is set to 2. The route traveled by the delivery vehicle during this duration is marked as a severe navigation anomaly area. The severe navigation anomaly tag indicates that the navigation signal on the corresponding road section is continuously unavailable.

[0042] Arrange the real-time navigation identification values ​​of the delivery vehicles in chronological order to obtain a navigation identification array and upload it to the unmanned delivery sharing platform in real time;

[0043] During the delivery process, existing unmanned logistics delivery vehicles are unable to monitor and count abnormal problems that arise, and the monitored and counted abnormal problem data cannot be shared, resulting in unmanned logistics delivery vehicles of different types of service providers easily encountering the same abnormal problems repeatedly, and the abnormal data exchange between unmanned logistics delivery vehicles of different service providers is not effective. In the embodiment of the present invention, by performing data statistics and processing and analysis from the perspective of navigation signal strength, it is possible to obtain real-time navigation signal conditions of different delivery vehicles of different service providers on different delivery routes, and provide reliable local data support for subsequent analysis of the impact of navigation signal anomalies in different abnormal areas and sharing of abnormal navigation signals, thereby improving the reliability and diversity of monitoring and analysis of navigation signals in unmanned logistics delivery.

[0044] The target delivery navigation anomaly assessment management module is used to evaluate the impact of navigation anomalies on different abnormal areas along different delivery routes based on the navigation identification arrays uploaded by different delivery vehicles to the unmanned delivery sharing platform. Based on the assessment results, dynamic navigation warning prompts are issued for subsequent delivery routes of different delivery vehicles. This module includes:

[0045] Obtain all navigation identification arrays and delivery identification arrays uploaded to the unmanned delivery sharing platform by all unmanned logistics delivery vehicles of different service providers, and traverse and count all navigation mild anomaly areas, navigation severe anomaly areas, and traffic anomaly areas respectively;

[0046] When performing anomaly impact assessment on all navigation anomaly areas that appear, all anomaly areas are sorted in descending order according to the total number of times different anomaly areas appear, and the corresponding anomaly weight YQk is obtained according to the type of anomaly label that appears each time, where k = 1, 2; YQ1 is the anomaly weight corresponding to the navigation mild anomaly label, and YQ2 is the anomaly weight corresponding to the navigation severe anomaly label; and, according to the navigation identifier values ​​of 1 and 2 in the navigation identifier array, the corresponding duration Tk is obtained, where T1 is the duration corresponding to the navigation mild anomaly label, and T2 is the duration corresponding to the navigation severe anomaly label;

[0047] By the formula φ=∑ Z 1YQk×Tk is used to calculate the navigation anomaly impact coefficient φ corresponding to the navigation anomaly area; where Z is the total number of navigation mild anomaly labels and navigation severe anomaly labels appearing in the anomaly area;

[0048] It should be noted that the navigation anomaly impact coefficient is a value used to evaluate the impact of navigation anomaly by integrating and calculating different abnormal navigation data appearing in the navigation anomaly area;

[0049] When evaluating the navigation anomaly impact status of different navigation anomaly areas based on the navigation anomaly impact coefficient, the navigation anomaly impact coefficient is compared with the preset navigation anomaly impact threshold for classification; the navigation anomaly impact threshold is determined based on the navigation signal design parameters;

[0050] Marking the navigation anomaly area corresponding to the navigation anomaly impact coefficient not greater than the navigation anomaly impact threshold as a low-impact navigation area;

[0051] and marking the navigation anomaly area corresponding to the navigation anomaly impact coefficient greater than the navigation anomaly impact threshold as a high-impact navigation area;

[0052] The low-impact navigation area or high-impact navigation area constitutes navigation anomaly impact analysis data and is uploaded to the unmanned delivery sharing platform in real time. The data is then synchronously shared with all service providers through the unmanned delivery sharing platform, and targeted navigation optimization warning prompts are implemented for navigation anomaly areas.

[0053] Since there are errors in the data of a single monitoring and analysis, and the planned routes of some unmanned delivery vehicles cannot be changed at will, it is necessary to further implement navigation anomaly impact verification and analysis on the shared navigation anomaly area; in an embodiment of the present invention, by integrating and calculating different abnormal navigation data appearing in the navigation anomaly area and evaluating and classifying its navigation anomaly impact status, more accurate and reliable sharing and alarm prompts of abnormal navigation status and specific locations can be implemented, so that the unmanned delivery vehicles of subsequent other service providers can prepare navigation preparation processing solutions in advance to improve the prompt sharing effect and avoidance processing effect of different navigation anomaly areas.

[0054] Example 2: A target delivery traffic anomaly monitoring and statistics module is used to collect data statistics and process real-time traffic anomalies of unmanned logistics delivery by different service providers that have joined the unmanned delivery sharing platform; including:

[0055] When collecting data statistics on real-time abnormal situations of different unmanned logistics deliveries in terms of dwell time, a monitoring instruction is generated when the delivery vehicle's speed reaches zero during delivery. The duration of the zero speed period is counted based on the monitoring instruction and the vehicle is positioned simultaneously. The zero speed period counted is then compared and classified.

[0056] If the duration is not greater than the standard duration, the delivery vehicle is judged to be in a reasonable avoidance state and a first avoidance normal label is generated and the associated delivery identifier value is set to 0; for example, sudden braking for emergency avoidance;

[0057] If the duration is longer than the standard duration and the positioning area belongs to the pre-set whitelist area table, the corresponding delivery vehicle is judged to be in normal avoidance and a second avoidance normal tag is generated and its associated delivery identifier value is set to 1; for example, for normal avoidance waiting at traffic lights, the whitelist area table is constructed based on the traffic light location areas that the delivery vehicle needs to pass through on all pre-planned routes;

[0058] If the duration is longer than the standard duration and the positioning area does not belong to the pre-set whitelist area table, the corresponding delivery vehicle's braking process is determined to be abnormal and an avoidance abnormality label is generated. The associated delivery identification value is set to 2. At the same time, the corresponding area is marked as a traffic abnormality area according to the avoidance abnormality label; for example, the road area is under maintenance and cannot be passed normally, or there are obstacles in the road area and cannot be passed normally;

[0059] Arrange and combine the delivery identification values ​​of several delivery vehicles during the delivery process in chronological order to obtain a delivery identification array and upload it to the unmanned delivery sharing platform in real time;

[0060] In the embodiment of the present invention, by performing data statistics and processing analysis based on the length of stay, it is possible to obtain the real-time stay status of different delivery vehicles of different service providers during different delivery routes, and to provide reliable local data support for the subsequent analysis of the impact of stay anomalies in different abnormal areas and the sharing of abnormal stay areas, thereby improving the reliability and diversity of monitoring and analysis of unmanned logistics distribution in terms of passage and stay.

[0061] The target delivery traffic anomaly assessment management module is used to evaluate the impact of traffic anomalies on different abnormal areas along different delivery routes based on the delivery identification arrays uploaded by different delivery vehicles to the unmanned delivery sharing platform. Based on the assessment results, dynamic warning prompts are issued for subsequent delivery routes of different delivery vehicles in terms of traffic. This module includes:

[0062] When evaluating the impact of all abnormal traffic areas, the total number of times the same abnormal traffic area appears is counted based on the delivery identification array, and the average duration of the delivery vehicles braking in the abnormal traffic area is obtained based on the total duration of the delivery vehicles braking in the abnormal traffic area.

[0063] Mark the abnormal traffic areas with a total number of occurrences not greater than K as sudden abnormal traffic areas;

[0064] The traffic abnormality areas with a total number of occurrences greater than K and an average stay time no greater than the stay time threshold are marked as low-impact traffic areas, where K is a positive integer; the stay time threshold is determined by the longest traffic light waiting time during the delivery process;

[0065] And mark the traffic abnormality areas with a total number of occurrences greater than K and an average stay time greater than the stay time threshold as high-impact traffic areas;

[0066] Low-impact traffic areas or high-impact traffic areas constitute traffic abnormality impact analysis data and are uploaded to the unmanned delivery sharing platform in real time. The data is then synchronously shared with all service providers through the unmanned delivery sharing platform and targeted route optimization warning prompts are implemented in traffic abnormality areas.

[0067] In an embodiment of the present invention, the average duration of stay in the abnormal traffic area is obtained by calculating the duration of brake stops of all delivery vehicles that appear in the abnormal traffic area. The abnormal traffic impact status of the abnormal traffic area is evaluated and classified according to the average duration of stay. This allows for more accurate and reliable sharing and warning of abnormal traffic status and specific locations. This allows subsequent unmanned delivery vehicles of other service providers to prepare route preparation plans in advance to improve the prompt sharing effect and avoidance processing effect of different abnormal traffic areas.

[0068] In addition, the formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and simulating it through simulation software.

[0069] In the several embodiments provided by the present invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative. For example, the division of modules is only a logical function division, and other division methods may be used in actual implementation.

[0070] Modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of these modules may be selected to achieve the objectives of this embodiment based on actual needs.

[0071] In addition, the functional modules in various embodiments of the present invention may be integrated into a single processing module, each module may exist physically separately, or two or more modules may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or hardware plus software functional modules.

[0072] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the basic characteristics of the present invention.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, ordinary operation and maintenance personnel in this field should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An unmanned logistics distribution system based on Beidou navigation, characterized in that: It includes a target delivery navigation anomaly monitoring and statistics module, which is used to collect data statistics and process real-time navigation anomalies of unmanned logistics delivery of different service providers that have joined the unmanned delivery sharing platform; including: Obtain basic information corresponding to the delivery vehicle, including the vehicle number and the service provider to which it belongs; when compiling data statistics on real-time abnormal situations of different unmanned logistics delivery operations in terms of navigation signal strength, obtain the real-time navigation signal strength of the delivery vehicle during each delivery; when conducting an effectiveness assessment of the obtained real-time navigation signal strength, compare and classify the real-time navigation signal strength with the preset standard navigation signal strength; If the navigation signal strength is not less than the standard navigation signal strength, a normal navigation tag is generated and its associated navigation identifier value is set to 0; If the navigation signal strength is less than the standard navigation signal strength and the duration is less than the standard duration, a navigation slight anomaly label is generated and its associated navigation identifier value is set to 1, and the route traveled by the delivery vehicle during this duration is marked as a navigation slight anomaly area; If the navigation signal strength is less than the standard navigation signal strength and the duration is not less than the standard duration, a severe navigation anomaly label is generated and its associated navigation identifier value is set to 2. The route traveled by the delivery vehicle during this duration is marked as a severe navigation anomaly area. Arrange the real-time navigation identification values ​​of the delivery vehicles in chronological order to obtain a navigation identification array and upload it to the unmanned delivery sharing platform in real time; The target delivery navigation anomaly assessment management module is used to conduct navigation anomaly impact assessment on different abnormal areas in different delivery routes based on the navigation identification arrays uploaded by different delivery vehicles to the unmanned delivery sharing platform, and to provide dynamic warning prompts from the navigation aspect for subsequent delivery routes of different delivery vehicles based on the assessment results.

2. The Beidou navigation-based unmanned logistics distribution system according to claim 1, characterized in that: Obtain all navigation identification arrays and delivery identification arrays uploaded to the unmanned delivery sharing platform by all unmanned logistics delivery vehicles of different service providers, and traverse and count all navigation mild anomaly areas, navigation severe anomaly areas, and traffic anomaly areas respectively; When performing anomaly impact assessment on all navigation anomaly areas that appear, all anomaly areas are sorted in descending order according to the total number of times different anomaly areas appear, and the corresponding anomaly weight YQk is obtained according to the type of anomaly label each time, where k = 1, 2; and the corresponding duration Tk is obtained according to the navigation identifier values ​​1 and 2 in the navigation identifier array; By the formula φ=∑ Z 1YQk×Tk is used to calculate the navigation anomaly impact coefficient φ corresponding to the navigation anomaly area; where Z is the total number of navigation mild anomaly labels and navigation severe anomaly labels appearing in the anomaly area.

3. The unmanned logistics distribution system based on Beidou navigation according to claim 2 is characterized in that: When evaluating the navigation anomaly impact status of different navigation anomaly areas based on the navigation anomaly impact coefficient, the navigation anomaly impact coefficient is compared with the preset navigation anomaly impact threshold for classification; Marking the navigation anomaly area corresponding to the navigation anomaly impact coefficient not greater than the navigation anomaly impact threshold as a low-impact navigation area; and marking the navigation anomaly area corresponding to the navigation anomaly impact coefficient greater than the navigation anomaly impact threshold as a high-impact navigation area; Low-impact navigation areas or high-impact navigation areas constitute navigation anomaly impact analysis data and are uploaded to the unmanned delivery sharing platform in real time. They are then synchronously shared with all service providers through the unmanned delivery sharing platform and targeted navigation optimization warning prompts are implemented for navigation anomaly areas.

4. The unmanned logistics distribution system based on Beidou navigation according to claim 3 is characterized in that: The target delivery traffic anomaly monitoring and statistics module is used to collect data statistics and process the real-time traffic anomalies of unmanned logistics delivery of different service providers that have joined the unmanned delivery sharing platform.

5. The unmanned logistics distribution system based on Beidou navigation according to claim 4 is characterized in that: When collecting data statistics on real-time abnormal situations of different unmanned logistics deliveries in terms of stay time, a monitoring instruction is generated when the delivery vehicle's speed is zero during the delivery process. The duration of the zero speed is counted according to the monitoring instruction and the positioning is performed simultaneously. The duration of the zero speed counted by the monitoring is then compared and classified.

6. The Beidou navigation-based unmanned logistics distribution system according to claim 5, characterized in that: If the duration is not greater than the standard duration, a first avoidance normal label is generated and its associated delivery identifier value is set to 0; If the duration is longer than the standard duration and the positioning area belongs to the pre-set whitelist area table, a second avoidance normal tag is generated and its associated delivery identification value is set to 1; If the duration is longer than the standard duration and the positioning area does not belong to the pre-set whitelist area table, an avoidance exception label is generated and its associated delivery identifier value is set to 2, and the corresponding area is marked as a traffic exception area according to the avoidance exception label; The delivery identification values ​​that appear in the delivery process of the delivery vehicle are arranged and combined in chronological order to obtain a delivery identification array and uploaded to the unmanned delivery sharing platform in real time.

7. The unmanned logistics distribution system based on Beidou navigation according to claim 6 is characterized in that: The target delivery traffic anomaly assessment management module is used to conduct traffic anomaly impact assessment on different abnormal areas in different delivery routes based on the delivery identification arrays uploaded by different delivery vehicles to the unmanned delivery sharing platform, and to issue dynamic warning prompts to the subsequent delivery routes of different delivery vehicles in terms of traffic based on the assessment results.

8. The Beidou navigation-based unmanned logistics distribution system according to claim 7, characterized in that: When implementing abnormal impact assessment on all abnormal traffic areas that appear, the total number of times the same abnormal traffic area appears is counted based on the delivery identification array, and the average stay time of the abnormal traffic area is obtained based on all the stay times of the delivery vehicles braking in the abnormal traffic area, and assessment classification is implemented to obtain the abnormal traffic impact analysis data consisting of low-impact traffic areas or high-impact traffic areas and upload it to the unmanned delivery sharing platform in real time. It is then synchronously shared with all service providers through the unmanned delivery sharing platform and targeted route optimization warning prompts are implemented for abnormal traffic areas.

9. The Beidou navigation-based unmanned logistics distribution system according to claim 8, characterized in that: The traffic abnormality areas with a total number of occurrences greater than K and an average stay duration not greater than the stay duration threshold are marked as low-impact traffic areas, where K is a positive integer; And the traffic abnormality areas with a total occurrence number greater than K and an average stay time greater than the stay time threshold are marked as high-impact traffic areas.

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