Distribution management system of automatic logistics sorting machine

Abstract

The invention discloses a distribution management system of an automatic logistics sorting machine, which belongs to the technical field of intelligent logistics sorting and comprises a regional analysis module and a sorting module. The area analysis module is used for analyzing the service area and dividing the service area into a plurality of distribution areas to form an area distribution map; the sorting module is used for controlling the logistics sorting machine to sort, setting corresponding sorting areas according to distribution areas in the area distribution map, and configuring the logistics sorting machine according to the sorting areas; identifying the destination of each express in real time through a logistics sorting machine, marking the identified destination in the regional distribution map, identifying a distribution region corresponding to the destination position, and sorting the corresponding express to a sorting region corresponding to the distribution region; through mutual cooperation between the regional analysis module and the sorting module, intelligent sorting and loading of unmanned vehicles in current intelligent logistics are realized; the distribution efficiency of the unmanned vehicles is improved, the loading process is optimized, and the cargoes needing to be distributed by the unmanned vehicles are intelligently sorted out.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent logistics sorting, and in particular is a diversion management system for an automatic logistics sorting machine. Background Art

[0002] With the development of the logistics industry, unmanned delivery vehicles in the intelligent distribution industry have gradually become a hot technology; unmanned vehicles are tools that can move to designated locations by themselves without the need for human drivers. Unmanned delivery vehicles can save human resources and effectively increase the processing volume. At present, in order to improve the efficiency of express delivery, express deliveries to multiple delivery destinations are generally delivered together. This is a relatively simple matter for manual delivery, but it is more complicated for unmanned vehicle delivery, because the destination can be delivered unmanned, which destinations can be delivered at the same time, etc., will result in different express deliveries to be delivered by unmanned vehicles with different performance; therefore, in order to improve the delivery efficiency of unmanned vehicles and optimize the loading process, the present invention provides an automatic logistics sorting machine diversion management system for intelligently sorting out the goods that each unmanned vehicle needs to deliver. Summary of the invention

[0003] In order to solve the problems existing in the above-mentioned solutions, the present invention provides a diversion management system for an automatic logistics sorting machine.

[0004] The purpose of the present invention can be achieved through the following technical solutions:

[0005] An automatic logistics sorting machine diversion management system, including a regional analysis module and a sorting module;

[0006] The regional analysis module is used to analyze the service area, divide it into several delivery areas, and form a regional delivery map.

[0007] Furthermore, the service area is analyzed, including:

[0008] Marking each unit area and the unit features corresponding to each unit area in the service area map, screening each unit area in the service area map, and obtaining a second process map;

[0009] Evaluate the correlation values ​​between the unit areas in the second process diagram, and list the unit areas whose correlation values ​​are greater than a threshold value X2 as a correlation set;

[0010] The initial unit area in the association set is identified, and each unit area in the association set is merged based on the initial unit area to obtain the delivery area.

[0011] Furthermore, the unit area is screened, including:

[0012] Based on the characteristics of each unit, it is judged whether the corresponding unit area meets the current delivery requirements of the unmanned vehicle, and the unit area that does not meet the delivery requirements is removed from the service area map to obtain a first process map;

[0013] Mark the delivery routes of each unit area in the first process diagram, evaluate the delivery value corresponding to each delivery route, and eliminate the delivery routes with delivery values ​​lower than the threshold value X1;

[0014] The remaining delivery routes are marked as candidate routes, and the unit areas without candidate routes are removed from the first process graph to obtain the second process graph.

[0015] Furthermore, the unit areas are merged, including:

[0016] Generate a first sequence according to the correlation values ​​between the initial unit area and other unit areas in the correlation set;

[0017] The corresponding maximum delivery distance is set according to the performance of the unmanned vehicle, and the unit areas are merged based on the maximum delivery distance and the first sequence to obtain the delivery area.

[0018] Furthermore, the first sequence includes step-by-step distances between unit regions.

[0019] Furthermore, the regional analysis module is arranged at the main end.

[0020] Furthermore, a regional distribution map is formed, including:

[0021] Mark each delivery area in the service area map, identify the unit area corresponding to the non-delivery area in the service area map, mark the manual area, merge the manual areas, obtain the delivery area for manual delivery, and make corresponding marks in the service area map, marking the current service area map as a regional delivery map.

[0022] The sorting module is used to control the logistics sorting machine to perform sorting, set the corresponding sorting area according to the distribution area in the regional distribution map, and configure the logistics sorting machine according to each sorting area;

[0023] The destination of each express is identified in real time by the logistics sorting machine, the identified destination is marked on the regional distribution map, the distribution area corresponding to the destination location is identified, and the corresponding express is sorted to the sorting area corresponding to the distribution area.

[0024] Furthermore, for the destination marked in the regional delivery map, when the corresponding delivery area is identified, the corresponding mark is cancelled.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] Through the mutual cooperation between the regional analysis module and the sorting module, the intelligent sorting and loading of unmanned vehicles in the current intelligent logistics can be realized; the delivery efficiency of unmanned vehicles can be improved, the loading process can be optimized, and the goods that need to be delivered by each unmanned vehicle can be intelligently sorted; the rapid loading of unmanned vehicles can be realized to avoid spending a lot of time on screening during the loading process, and at the same time solve the problem that the assembled goods cannot be delivered; and in order to improve resource utilization and reduce the capital investment of each distribution point, distribution center, etc., the regional analysis module is set at the main end. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0029] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0030] like Figure 1 As shown, an automatic logistics sorting machine diversion management system includes a regional analysis module and a sorting module;

[0031] The regional analysis module is used to analyze the service area and divide the service area into several delivery areas. The express within the delivery area can be delivered simultaneously by unmanned vehicles. When sorting, the express is sorted according to each delivery area. When the unmanned vehicle delivers, it only needs to load the express according to the corresponding sorted express area, without the need for complex loading analysis again, thus improving the delivery efficiency of the unmanned vehicle; specifically:

[0032] The service area is the distribution service area corresponding to the distribution point and distribution center. The service area map of the service area is obtained. According to the various unit areas in the service area map such as residential areas, office buildings, shopping malls, etc., in the specific application process, the feature type corresponding to the unit area can be set. Based on the preset feature type and the existing recognition technology, each unit area is identified and marked in the service area map, and the corresponding unit features are marked for each unit area. The unit features include unit type, building features, express receiving features, etc. The unit type refers to the types of residential areas, office buildings, shopping malls, storefronts, etc. The building features refer to the building features that have an impact on the delivery of unmanned vehicles, such as elevator access, stair access, width, number of floors, etc. The express receiving features are the historical express delivery corresponding to the unit area, which is delivered to the door, express box, self-pickup point, and doorman placement. That is, the corresponding data content in the unit features is related to whether the unmanned vehicle can deliver. Specifically, it can be adjusted accordingly through manual methods so that the marked unit features meet its actual application needs; using the existing recognition technology, the unit features corresponding to each unit area can be extracted from the large amount of historical delivery data accumulated.

[0033] The obtained unit features are marked at the positions corresponding to each unit area in the service area map, and a corresponding evaluation model is established based on the CNN network or the DNN network. A corresponding training set is established manually based on the possible types of unit features, the performance of the unmanned vehicle and the corresponding evaluation results for training, and the evaluation results include qualified and unqualified; the service area map is analyzed by the evaluation model after successful training, and the evaluation results corresponding to each unit feature are output. The unit areas with qualified evaluation results are retained, and the unit areas with unqualified evaluation results are eliminated and the marks are cancelled, that is, the unit areas with unqualified evaluation results cannot be delivered by unmanned vehicles under the current unmanned vehicle performance; because neural networks are existing technologies in this field, the specific establishment and training process will not be described in detail; the service area map after unit area screening is marked as the first process diagram.

[0034] Identify the delivery routes that unmanned vehicles have to reach each unit area, and obtain the distance, road condition and other data corresponding to each delivery route. The road condition data represents the congestion, traffic lights, pedestrians, road occupation and other data of each road section, which is used to evaluate whether the unmanned vehicle can pass normally under different road conditions, and continuously update the road condition data in combination with historical traffic data, set the road condition value in combination with the distance and road condition data, and set the operation value according to the distance and the delivery performance of the unmanned vehicle and other data; specifically, establish a corresponding road analysis model based on the CNN network or the DNN network, establish the corresponding training set manually for training, and analyze the road analysis model after successful training to obtain the road condition value and operation value corresponding to each candidate route. The better the road condition and the shorter the distance, the higher the road condition value and the operation value.

[0035] Mark the road condition value and the running value as LQ and YP respectively. Calculate the corresponding delivery value PQ according to the formula PQ = λ × (b1 × LQ + b2 × YP), where λ is a correction factor with a value range of 0 ≤ λ ≤ 1. The correction factor is set according to the road condition value and the running value. For any value that does not meet the standard in the road condition value and the running value, that is, the road condition or distance does not meet the delivery conditions, then λ = 0. After that, as the conditions change, λ gradually increases. Specifically, a corresponding coordinate system can be established manually with the road condition value and the running value as coordinates, and different coordinate regions are divided for different λ values. Subsequently, the corresponding correction factor λ is obtained according to the coordinate positions corresponding to the road condition value and the running value; or an analysis model can be established based on a neural network for intelligent analysis; both b1 and b2 are proportionality coefficients with value ranges of 0 < b1 ≤ 1 and 0 < b2 ≤ 1; Eliminate the delivery routes with a delivery value lower than the threshold X1, mark the remaining delivery routes as candidate routes, eliminate and unmark the unit areas without candidate routes, and mark the current first process diagram as the second process diagram.

[0036] Identify the candidate routes corresponding to each unit area in the second process diagram, and set corresponding correlation values according to the candidate routes of each unit area. The correlation value evaluates the correlation between unit areas based on data such as the distance between them and the overlap of delivery routes. Specifically, a corresponding correlation analysis model can be established based on a CNN network or a DNN network, and a corresponding training set can be established manually for training. After successful training, the correlation analysis model is used for analysis to obtain the correlation values between unit areas; Integrate the numbers of the unit areas with correlation values greater than the threshold X2 into a correlation set, and determine the initial unit area, which is generally the farthest one in the correlation set, or the corresponding initial unit screening method can be adjusted manually according to the actual situation; Based on the initial unit area, sort the other unit areas in the correlation set according to the magnitude of the correlation value with the initial unit area, that is, sort them in descending order of the correlation value, and mark the step-by-step distances from the initial unit area to each unit area in the sequence to obtain the first sequence. For example, they are sorted as the initial unit area, the first unit, the second unit area, etc., identify the distance between the initial unit area and the first unit area, the distance between the first unit area and the second unit area, and so on; Obtain the step-by-step distances;

[0037] Set the corresponding maximum delivery distance according to the performance of the unmanned vehicle, and merge the unit areas based on the maximum delivery distance and the first sequence to obtain the delivery area, that is, merge the unit areas step by step in the order of the first sequence. After each merge, calculate the corresponding delivery distance and the corresponding return distance, and compare them with the maximum delivery distance; Make subsequent merges according to the comparison results.

[0038] Each unit area corresponding to the obtained delivery area is marked with the same similar label in the service area map, that is, the express within the unit area corresponding to the same similar label can be delivered unmanned at the same time; the unit area in the service area map that is not marked with the same similar label is identified and marked as a manual area, and the manual areas are merged and allocated based on the existing manual delivery method to obtain the corresponding delivery area for manual delivery, and the corresponding mark is made in the service area map, and the current service area map is marked as a regional delivery map.

[0039] In one embodiment, in order to achieve comprehensive utilization of resources, the regional analysis module can be set up at the headquarters, that is, it is not necessary to set up a regional analysis module for each distribution point and distribution center. The headquarters can perform unified analysis without frequent analysis, or multiple distribution points and distribution centers can use one regional analysis module together. For the sake of convenience, the headquarters, common use points and other locations where the regional analysis module is set are marked as the main end, that is, the regional analysis module is set at the main end.

[0040] The sorting module is used to control the logistics sorting machine to perform sorting, and specifically includes:

[0041] Identify each distribution area in the regional distribution map, set corresponding express sorting areas according to the number of distribution areas, and store the express in the corresponding distribution area; configure the logistics sorting machine according to each sorting area;

[0042] The logistics sorting machine can identify the delivery destination of each express in real time, mark the identified destination on the regional distribution map, identify the distribution area corresponding to the destination location, and sort the corresponding express to the sorting area corresponding to the distribution area; when the unmanned vehicle loads the goods subsequently, it only needs to load according to the corresponding sorting area.

[0043] For the destinations marked in the regional distribution map, once the corresponding delivery area is identified, they will be removed from the regional distribution map and unmarked.

[0044] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0045] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An automatic logistics sorting machine diversion management system, characterized in that: Including regional analysis module and sorting module; The regional analysis module is used to analyze the service area, divide it into several delivery areas, and form a regional delivery map; The sorting module is used to control the logistics sorting machine to perform sorting, set the corresponding sorting area according to the distribution area in the regional distribution map, and configure the logistics sorting machine according to each sorting area; The destination of each express is identified in real time by the logistics sorting machine, the identified destination is marked on the regional distribution map, the distribution area corresponding to the destination location is identified, and the corresponding express is sorted to the sorting area corresponding to the distribution area.

2. The automatic logistics sorting machine diversion management system according to claim 1 is characterized in that: Analyze the service area, including: Marking each unit area and the unit features corresponding to each unit area in the service area map, screening each unit area in the service area map, and obtaining a second process map; Evaluate the correlation values ​​between the unit areas in the second process diagram, and list the unit areas whose correlation values ​​are greater than a threshold value X2 as a correlation set; The initial unit area in the association set is identified, and each unit area in the association set is merged based on the initial unit area to obtain the delivery area.

3. The automatic logistics sorting machine diversion management system according to claim 2 is characterized in that: Filter the unit area, including: Based on the characteristics of each unit, it is judged whether the corresponding unit area meets the current delivery requirements of the unmanned vehicle, and the unit area that does not meet the delivery requirements is removed from the service area map to obtain a first process map; Mark the delivery routes of each unit area in the first process diagram, evaluate the delivery value corresponding to each delivery route, and eliminate the delivery routes with delivery values ​​lower than the threshold value X1; The remaining delivery routes are marked as candidate routes, and the unit areas without candidate routes are removed from the first process graph to obtain the second process graph.

4. The automatic logistics sorting machine diversion management system according to claim 3 is characterized in that: Merge each unit area, including: Generate a first sequence according to the correlation values ​​between the initial unit area and other unit areas in the correlation set; The corresponding maximum delivery distance is set according to the performance of the unmanned vehicle, and the unit areas are merged based on the maximum delivery distance and the first sequence to obtain the delivery area.

5. The automatic logistics sorting machine diversion management system according to claim 4 is characterized in that: The first sequence includes step-by-step distances between unit regions.

6. The automatic logistics sorting machine diversion management system according to claim 5 is characterized in that: Form a regional distribution map, including: Mark each delivery area in the service area map, identify the unit area corresponding to the non-delivery area in the service area map, and mark the artificial area; The manual areas are merged to obtain the distribution area for manual distribution, and the corresponding marks are made in the service area map, and the current service area map is marked as the regional distribution map.

7. The automatic logistics sorting machine diversion management system according to claim 1 is characterized in that: The regional analysis module is arranged at the main end.

8. The automatic logistics sorting machine diversion management system according to claim 1, characterized in that: For the destinations marked in the regional delivery map, when the corresponding delivery area is identified, the corresponding mark will be cancelled.

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