Intelligent distribution equipment path control system

Abstract

The application discloses an intelligent distribution equipment path control system, and relates to the technical field of control, comprising: an order acquisition module, which is used for acquiring orders needing logistics distribution through a logistics platform server, performing address analysis on the orders, matching address coordinates in combination with GIS geographic coding, and obtaining order address coordinates; a determination module, which is used for performing distribution analysis according to order article types, determining the distribution equipment types and distribution timeliness of each order, and obtaining distribution priority analysis results; a path planning module, which is used for performing intelligent path planning according to the order address coordinates and the distribution priority analysis results; a resource scheduling module, which is used for performing distribution equipment scheduling based on order article types; a control module, which is used for accessing real-time road conditions and performing real-time control of the distribution path based on the real-time road conditions; and a verification module, which is used for verifying order article distribution results, obtaining verification results, and feeding back and optimizing the distribution effect and user experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent control, and particularly relates to an intelligent distribution equipment path control system. BACKGROUND

[0002] The rapid development of e-commerce platforms has deeply reshaped the operation logic of the logistics industry, driving the transition of logistics transportation from traditional labor-intensive mode to intelligent, automated and data-driven modern system. In this transformation, the rapid iteration and application of unmanned equipment such as drones and unmanned vehicles, and intelligent equipment such as automatic sorting systems and Internet of Things sensors, not only greatly improve the logistics efficiency and accuracy, but also force traditional logistics transportation systems to face challenges and urgently need to realize transformation and upgrading through technology integration and mode innovation.

[0003] Regarding the above situation, the problems are as follows:

[0004] 1. Lack of intelligent control strategy for the whole process of the logistics system and processing strategy for emergency situations of unmanned equipment;

[0005] 2. The logistics transportation equipment mainly relies on the transfer of goods transfer sites, and the utilization rate of intelligent equipment and unmanned equipment transfer equipment is low at present;

[0006] 3. Lack of feedback optimization mechanism and encryption verification mechanism. SUMMARY

[0007] In view of the above problems, the present application provides an intelligent distribution equipment path control system to solve the above problems.

[0008] An intelligent distribution equipment path control system comprises:

[0009] An order acquisition module is configured to acquire orders requiring logistics distribution through a logistics platform server, analyze the addresses of the orders, match the address coordinates with GIS geographic coding, and obtain the order address coordinates;

[0010] A determination module is configured to analyze the distribution according to the order item type, determine the distribution equipment type and distribution time limit of each order, and obtain the distribution priority analysis result;

[0011] A path planning module is configured to perform intelligent path planning according to the order address coordinates and the distribution priority analysis result;

[0012] A resource scheduling module is configured to schedule the distribution equipment based on the order item type;

[0013] A control module is configured to access real-time traffic and perform real-time control of the distribution path based on the real-time traffic;

[0014] The verification module is configured to verify the order item delivery result, and obtain a verification result to feed back and optimize the delivery effect and user experience.

[0015] Preferably, the order obtaining module comprises:

[0016] The obtaining submodule is configured to filter orders in a "to-be-shipped" state through an order API interface of an e-commerce platform and determine the orders as to-be-delivered orders, and obtain specific contents of the to-be-delivered orders.

[0017] The extraction submodule is configured to extract an address field of the to-be-delivered order, remove random codes and special symbols in the address, and unify abbreviations.

[0018] The address resolution submodule is configured to split elements of the address field by using a natural language processing algorithm, verify validity in combination with an address knowledge base, automatically correct errors, and generate a structured address.

[0019] The coordinate matching submodule is configured to call a map service API to convert the structured address into latitude and longitude coordinates, perform geographical position precision matching, preferentially match the highest precision coordinates, and downgrade to a community, a street, and a district-level coordinate in sequence in case of failure, and generate address coordinates.

[0020] Preferably, the determination module comprises:

[0021] The first classification submodule is configured to classify delivery time efficiency and delivery equipment according to an item type of the to-be-delivered order, and generate a first classification result.

[0022] The second classification submodule is configured to classify delivery priority based on an item value and an item volume of the to-be-delivered order item, and generate a second classification result.

[0023] The weight allocation submodule is configured to allocate delivery priority weight according to a preset priority weight of the first classification result and the second classification result of each order, and generate a delivery equipment type label according to the first classification result.

[0024] The generation submodule is configured to calculate a priority score based on a delivery priority weight allocation result of each order, generate a delivery priority label, obtain the delivery equipment type label, and determine a delivery equipment type and a delivery time efficiency used by each order based on the delivery priority label and the delivery equipment type label.

[0025] Preferably, the generation submodule comprises:

[0026] The calculation unit is configured to calculate a priority score by using a delivery priority weight based on a delivery priority weight allocation result of each order.

[0027] The label generation unit is configured to grade the priority score according to preset first and second priority thresholds, and generate a delivery priority label based on the priority grade.

[0028] The determination unit is configured to obtain a delivery device type label of each order, determine a delivery device type based on the delivery device type label, obtain the delivery priority label of each order, and determine a delivery time limit based on a delivery time limit range corresponding to the delivery priority label.

[0029] The output unit is configured to output the delivery device type and delivery time limit information used by each order.

[0030] Preferably, the path planning module comprises:

[0031] The semantic analysis submodule is configured to perform semantic analysis on the order content, and obtain volume, weight, and storage requirement information of the order item.

[0032] The mapping submodule is configured to generate a mapping table based on the order number, order address coordinates, and the delivery priority label of the order.

[0033] The path generation submodule is configured to obtain the order information mapping table, and generate an optimal path sequence through crossover and mutation based on a multi-objective optimization genetic algorithm.

[0034] The clustering optimization submodule is configured to obtain the semantic analysis result of each order, perform clustering adjustment on orders that coincide in the optimal path sequence according to a coincidence keyword based on the semantic analysis result, and generate an optimal path for each order based on the clustering adjustment result.

[0035] Preferably, the resource scheduling module comprises:

[0036] The information acquisition submodule is configured to obtain the optimal path, delivery time limit, and delivery device type of each order in the to-be-delivered order, and obtain the device type, current position, and current state of each delivery device in the delivery device library.

[0037] The order allocation submodule is configured to determine a currently available device based on the current state of each delivery device in the delivery device library, and perform order allocation based on the optimal path of each order in the to-be-delivered order according to the position of the currently available device.

[0038] The device allocation submodule is configured to allocate orders to a device pool formed by delivery devices of a corresponding type based on the delivery device type of each order.

[0039] The scheduling submodule is configured to obtain energy reserve and energy consumption information of each delivery device in the device pool in real time, and generate a multi-objective optimization solution set based on the delivery time limit, risk value, and optimal path predicted delivery energy consumption of each order as an optimization target.

[0040] The optimization sub-module is configured to perform target optimization based on the principle that high-priority delivery orders are preferentially allocated to delivery devices, establish a unique correspondence between each order in the multi-target optimization solution set and each delivery device in the multi-target optimization solution set, allocate orders based on the correspondence, and generate a delivery path for each delivery device.

[0041] Preferably, the control module comprises:

[0042] The data access sub-module is configured to obtain real-time traffic information of the delivery path of each delivery device through a map API interface and access current weather warning information of the delivery path of each delivery device through a weather API interface.

[0043] The calculation sub-module is configured to calculate a traffic congestion index of the delivery path of each delivery device based on the real-time traffic information and the weather warning information.

[0044] The judgment sub-module is configured to perform segmentation processing on the delivery path of each delivery device, judge the traffic congestion index of the segmented delivery path, and divide a road segment with a traffic congestion index above a preset threshold into an impassable road segment.

[0045] The path re-planning sub-module is configured to use AI to avoid the impassable road segment and re-plan an order delivery sequence based on the new path and the order address coordinates corresponding to the delivery device.

[0046] Preferably, the verification module comprises:

[0047] The signed confirmation sub-module is configured to obtain, as a delivery voucher, an electronic signature of a customer, a customer usage score, a delivery record, and a photo of the state of goods when the order is delivered.

[0048] The system verification sub-module is configured to compare a planned delivery time with an actual signed time, automatically mark a delayed order, and verify a planning coincidence degree of a delivery path of each order.

[0049] The index analysis sub-module is configured to obtain timeliness, integrity, quality compliance rate, and user satisfaction index of each order, perform a qualified rate and offset degree analysis on the index of each order, and perform a cause analysis on unqualified orders according to characteristics.

[0050] The update sub-module is configured to perform algorithm feature optimization according to the cause analysis result and perform system update on the intelligent delivery device path control system based on the algorithm feature optimization result.

[0051] Preferably, the system further comprises an emergency module configured to detect the real-time state of the delivery device based on sensors built in the delivery device, generate a real-time detection result of the delivery device, and start emergency management measures when the real-time detection result of the delivery device is abnormal, and the emergency module is configured to:

[0052] Obtain the type of each delivery device, determine the parts that need to be installed with sensors based on the type of the delivery device, and determine the target sensor to be installed for each part based on the type of the parameter detected by each part;

[0053] Determine the fault weight level corresponding to each part based on a preset part fault weight level library, and determine the data acquisition frequency of each target sensor according to the detection frequency corresponding to the fault weight level;

[0054] Clean and filter the data collected by the target sensor, and use a preset rule engine to perform abnormality judgment on the real-time data of the sensor;

[0055] Determine the abnormality judgment result as the real-time detection result of the delivery device, and determine the part with an abnormal judgment result as an abnormal part when the real-time detection result of the delivery device is abnormal;

[0056] Obtain the fault weight level corresponding to the abnormal part based on a preset part fault weight level library, determine the abnormal part with a fault weight level lower than the preset fault weight level as a repairable abnormal part, and determine other abnormal parts as non-operational abnormal parts;

[0057] According to the abnormality level judgment result, execute corresponding emergency management measures, push an abnormal report to the nearest repair point to the repairable abnormal part, and call the nearest delivery device in the delivery device library to transport the non-operational abnormal part;

[0058] Push an abnormal report and a predicted delay time to the user corresponding to each order of the abnormal delivery device.

[0059] Preferably, the system further comprises a label writing module configured to write the transportation node, transportation state, and delivery device information into each order corresponding delivery goods using NFC label technology, and automatically push a transportation report to the mobile terminal interface corresponding to each order, and the label writing module is configured to:

[0060] Generate a unique key pair for each NFC label, and issue a digital certificate by a logistics server to bind the correspondence between each order and the NFC label;

[0061] Write order basic information and an initial state code into the goods of each order using the NFC label when the goods of each order are collected, and the order basic information includes an order number, encrypted IDs of the sender and the receiver, and a cargo type.

[0062] At each order item pickup, the NFC writing device of the pickup device writes the sorting device ID and operation timestamp into the NFC tag;

[0063] After each order item enters the transportation process, the transportation device automatically synchronizes the transportation device code and GPS location to the NFC tag;

[0064] Obtain the corresponding item type of each order, judge the transportation conditions, and when the item type needs special transportation environment, write the sensor detection data of the internal environment data of the delivery device through the sensor corresponding to the special transportation environment into the NFC tag;

[0065] Each node device and the NFC tag verify the identity through the response protocol, generate a temporary session key after authentication, and encrypt the transmission of the node data corresponding to each node device;

[0066] When the tag state code changes, the server automatically initiates a mobile terminal push to each order;

[0067] When receiving, the user touches the tag, and the mobile terminal decrypts the NFC tag data and compares the consistency of the server report and the local NFC tag data.

[0068] Through the above technical means, the present application has the following beneficial effects:

[0069] 1) Through natural language processing and address knowledge base, accurate analysis and coordinate conversion of complex address are realized, high-precision spatial mapping is constructed combined with GIS geographic coding, and reliable spatial reference is provided for path planning; based on the multi-factor weight model of item value, time limit requirement and equipment capacity, intelligent matching of delivery equipment type and time limit is realized, and key demand priority response is ensured; genetic algorithm and semantic clustering technology are fused to generate the delivery sequence with the lowest energy consumption and the optimal time limit under traffic constraints.

[0070] 2) Based on real-time analysis of device state, energy consumption data and path coincidence degree, tasks are dynamically allocated through multi-objective optimization solution set, so that the equipment utilization is improved; relying on real-time monitoring of device state through sensor network, near maintenance and cargo transfer are started through fault weight grading mechanism; dynamic path re-planning integrated with traffic and weather data, combined with AI real-time routing algorithm reduces the risk of sudden delay.

[0071] 3) Based on the encrypted data chain of NFC tag, the non-tamperable record of transportation conditions and environmental parameters is realized, and the user end and server data bidirectional verification ensure the safety of goods; through multi-dimensional index analysis such as timeliness, integrity and user satisfaction, the iteration of path algorithm and scheduling strategy is driven, and the customer satisfaction is improved.

[0072] Additional features and advantages of the present application will be set forth in the description that follows, and in part will be apparent from the description, or can be learned by practice of the application. The objectives and other advantages of the present application will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.

[0073] The technical solutions of the present application are described in further detail below with the aid of the accompanying drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0074] The accompanying drawings are included to provide a further understanding of the present application and are incorporated in and constitute a part of the specification, illustrate embodiments of the present application and are used to explain the present application, but do not constitute a limitation of the present application.

[0075] Figure 1 A schematic diagram of an intelligent distribution equipment path control system provided by the present application;

[0076] Figure 2 A schematic diagram of an order acquisition module of an intelligent distribution equipment path control system provided by the present application;

[0077] Figure 3 A schematic diagram of a determination module of an intelligent distribution equipment path control system provided by the present application. DETAILED DESCRIPTION

[0078] The exemplary embodiments will be described in detail herein with reference to the accompanying drawings. In the following description, the same numbers refer to the same elements throughout the drawings. The embodiments described in the following exemplary embodiments do not represent all the embodiments consistent with the present disclosure.

[0079] The rapid development of e-commerce platforms has deeply reshaped the operation logic of the logistics industry, driving the logistics transportation to transition from the traditional labor-intensive mode to the intelligent, automated, and data-driven modern system. In this transformation, the rapid iteration and application of unmanned equipment such as drones, unmanned vehicles, and intelligent equipment such as automatic sorting systems and Internet of Things sensors not only significantly improve logistics efficiency and accuracy, but also force traditional logistics transportation systems to face challenges and urgently need to achieve transformation and upgrading through technology integration and mode innovation.

[0080] Regarding the above situation, the problems are as follows:

[0081] 1. Lack of intelligent control strategies for the entire logistics system and handling strategies for unexpected situations of unmanned equipment;

[0082] 2. The logistics transportation equipment mainly relies on cargo transfer stations for transfer, and the utilization rate of intelligent equipment and unmanned equipment for transfer is currently low;

[0083] 3. Lack of feedback optimization mechanism and encryption verification mechanism.

[0084] To solve the above problems, an intelligent distribution equipment path control system is adopted, as shown in the figure, comprising: Figure 1

[0085] An order acquisition module 101 is configured to acquire an order requiring logistics distribution through a logistics platform server, perform address resolution on the order, match address coordinates by combining GIS geographic coding, and obtain order address coordinates.

[0086] After obtaining the order to be distributed on the e-commerce platform, the embodiment first performs intelligent resolution on the text address in the order, identifies key elements such as provinces, cities, districts, roads, landmark buildings, and house numbers through natural language processing technology; then, combined with GIS geographic coding technology, the structured address after resolution is matched with the background address library in space, and the longitude and latitude coordinates are accurately positioned by using topological relationship analysis and semantic similarity algorithm, so as to realize the mapping of address from text description to geographic space position.

[0087] A determination module 102 is configured to perform distribution analysis according to the order item type, determine the distribution equipment type and distribution time limit of each order, and obtain a distribution priority analysis result.

[0088] Based on the item type of the order to be distributed, such as medical emergency supplies requiring instant delivery, fresh food requiring next-day delivery, and ordinary items suitable for regular distribution, the embodiment determines the time limit priority; at the same time, combined with the physical properties of the items, such as size and weight, and environmental requirements, such as cold chain and shockproof, the optimal distribution equipment type is intelligently matched, for example, a drone is used for emergency small items, an AGV is used for warehouse transfer, and a cold chain vehicle is used for temperature-sensitive goods; finally, according to the priority rules and equipment performance, a differentiated distribution time limit commitment is dynamically set, such as fresh food 2 hours delivery and ordinary items next-day delivery, so as to realize accurate adaptation of logistics resources and maximize the fulfillment efficiency.

[0089] A path planning module 103 is configured to perform intelligent path planning according to the order address coordinates and the distribution priority analysis result.

[0090] Based on the accurate geographic coordinate points generated by address resolution, combined with the equipment type matching scheme and time limit requirement obtained by distribution priority analysis, the embodiment constructs a dynamic path model through multi-source data fusion, and generates an optimal distribution path that meets the time limit, safety, and resource utilization efficiency by using intelligent optimization algorithm.

[0091] A resource scheduling module 104 is configured to perform distribution equipment scheduling based on the order item type.

[0092] ​The embodiment is based on the order-in distribution article type, such as fresh food, fragile goods, large goods, etc., combined with the real-time state of the corresponding distribution equipment type, such as idle transport capacity, temperature control function, load capacity and transportation condition constraint, through intelligent scheduling algorithm to dynamically match the optimal equipment, realize the precise adaptation of goods characteristics and transportation equipment, ensure the collaborative optimization of distribution efficiency and goods safety;

[0093] The control module 105 is used for accessing real-time traffic, and performing real-time control on the distribution path based on the real-time traffic;

[0094] The embodiment constructs a dynamic traffic sensing network by accessing real-time traffic data, combining GPS positioning and speed information of the distribution vehicle, and using intelligent algorithms to analyze road congestion index, accident warning and temporary regulation information in real time, dynamically triggering path re-planning mechanism, optimizing turning strategy and driving order in millisecond-level response, ensuring that the distribution vehicle actively avoids congestion nodes and balances road network load, realizing adaptive navigation and time efficiency precise control in the whole process;

[0095] The verification module 106 is used for verifying the order article distribution result, and obtaining the verification result to feedback and optimize the distribution effect and user experience.

[0096] The embodiment performs multi-dimensional result verification after the order distribution is completed, including goods integrity, quantity accuracy, time efficiency compliance and service specification, and generates a verification report through real-time data collection; based on the report, the system combines customer satisfaction survey and complaint data to identify weak links in the distribution process and drive dynamic optimization of service strategy.

[0097] The working principle of the above technical solution is as follows: first, the order needs to be distributed by the e-commerce platform, and the address is analyzed, and the address coordinates are matched with GIS geographic coding; second, the distribution priority of the order article type is analyzed to determine the distribution equipment type and the distribution time efficiency of each order; intelligent path planning is performed according to the address analysis result and the distribution priority analysis result; distribution equipment scheduling is performed based on the specific distribution articles of the order; real-time traffic is accessed, and real-time control of the distribution path is performed based on the real-time traffic; finally, the order article distribution result is verified, and the distribution effect and user experience are feedback and optimized based on the verification result.

[0098] The beneficial effects of the above technical solutions are: through deep integration of order intelligent analysis, dynamic path planning and real-time resource scheduling, the overall efficiency and user experience of logistics distribution are significantly improved. First, the system realizes accurate resource allocation based on intelligent matching of item type and delivery timeliness, effectively compresses order processing time and optimizes equipment utilization; second, through real-time road condition perception and dynamic path adjustment capability, the anti-interference and compliance stability in complex city distribution environment are greatly enhanced; third, the feedback loop formed by the verification module continuously drives algorithm optimization and strategy iteration, not only reduces the operation risks such as cold chain loss and high-value product damage, but also improves the end-user satisfaction through transparent distribution process and proactive abnormal response mechanism, finally realizes the coordinated optimization in the dimensions of cost control, service quality and green emission reduction.

[0099] In one embodiment, as shown in Figure 2 The order acquisition module includes:

[0100] The acquisition submodule 1011 screens the orders in the state of "to be shipped" through the order API interface of the e-commerce platform and determines them as to-be-delivered orders, and acquires the specific content of the to-be-delivered orders;

[0101] This embodiment screens the valid orders in the state of "to be shipped" in real time through the order API interface of the e-commerce platform, automatically identifies and locks them as to-be-delivered orders; at the same time, the core field content in the order is accurately extracted, including the information of the sender and receiver, item details, payment status and logistics requirements, etc. key data, realizing the flow from order state identification to business data analysis;

[0102] The extraction submodule 1012 is used for address field extraction of the to-be-delivered order, removing the garbled code, special symbols in the address, and unifying the abbreviations;

[0103] The address resolution submodule 1013 is used for splitting the elements of the address field using natural language processing algorithm, verifying the validity in combination with the address knowledge base, automatically correcting errors, and generating a structured address;

[0104] This embodiment uses natural language processing technology to perform semantic analysis and entity recognition on the original address text, accurately disassembles the core elements such as multi-level administrative division, road, and house number; at the same time, the standard address knowledge base and geographic information graph are linked to verify the hierarchical compliance and detect the completeness of the elements, and based on the error correction model, the missing levels are automatically completed, spelling errors or redundant descriptions are corrected;

[0105] The coordinate matching submodule 1014 is used for calling the map service API to convert the structured address into latitude and longitude coordinates, and performing geographic position accuracy matching, preferentially matching the highest accuracy coordinates, and failing to downgrade to the community, street, and district coordinates in sequence, to generate address coordinates.

[0106] The embodiment calls a map service API to convert a structured address into latitude and longitude coordinates, and by parsing the confidence index and accuracy level in the returned result, the highest accuracy coordinates are preferentially matched. If the matching fails, the confidence threshold is gradually degraded to cell, road or district level coordinates, and an interpolation algorithm combined with an address knowledge base is supported to supplement the missing positions, and finally the address coordinates meeting the requirements of spatial analysis are generated.

[0107] The beneficial effects of the above technical solution are: through full-link automation and intelligent processing, the source data quality and processing efficiency of the logistics distribution system are significantly improved: the address information in the e-commerce scene is effectively solved. The problems of disorder, ambiguity and error greatly reduce the cost of manual intervention; deep address analysis and knowledge base verification not only ensure the validity of the address, but also reduce the order lag caused by address abnormalities through intelligent error correction capability; the dynamic coordinate matching strategy ensures that each order can obtain the optimal geographic location anchor point through a multi-level degradation mechanism, providing a high-precision, full-coverage spatial foundation for subsequent path planning, thereby opening up the precise mapping channel from the order to the geographic space and determining the data basis for global distribution optimization.

[0108] In one embodiment, as shown in Figure 3 The determining module comprises:

[0109] The first classification submodule 1021 is configured to classify the delivery efficiency and the delivery equipment according to the type of the goods of the order to be delivered, and generate a first classification result.

[0110] The embodiment determines the time efficiency priority based on the type of the goods of the order to be delivered, such as instant delivery for medical emergency supplies, next-day delivery for fresh food, and regular delivery for ordinary goods, and matches the delivery equipment type according to the characteristics of the goods, wherein cold-chain goods are automatically allocated to temperature-controlled vehicles and refrigerated containers, heavy goods are dispatched by heavy forklifts and flatbed trucks, high-value precision goods are enabled by shockproof and explosion-proof special equipment, and e-commerce small goods are adapted to unmanned aerial vehicles and other automated terminal delivery devices, thereby realizing the precise mapping of time efficiency classification and equipment selection.

[0111] The second classification submodule 1022 is configured to classify the delivery priority based on the value and volume of the goods of the order to be delivered, and generate a second classification result.

[0112] The embodiment constructs a priority evaluation system based on the value and volume of the goods, generates a second-level classification label through a weighting algorithm, optimizes the loading rate, and generates a delivery time efficiency priority.

[0113] The weight allocation submodule 1023 is configured to allocate a delivery priority weight according to a preset priority weight of the first classification result and the second classification result of each order, and generate a delivery equipment type label according to the first classification result.

[0114] The embodiment generates a comprehensive priority weight value by a weighted fusion algorithm according to the first classification result, the time limit / device type, and a preset weight ratio of the second classification result (for example, the time limit weight accounts for 60%, and the value volume accounts for 40%); meanwhile, the item characteristic parameters in the first classification are independently extracted, matched with a device constraint library by a rule engine, and a device type label that cannot be covered is generated, for example, a cold chain demand is forcibly bound to a refrigerated vehicle.

[0115] The generation sub-module 1024 is configured to calculate a priority score based on a delivery priority weight allocation result of each order, generate a delivery priority label, obtain a delivery device type label, and determine a delivery device type and a delivery time limit used by each order based on the delivery priority label and the delivery device type label.

[0116] The embodiment calculates a priority quantitative score of each order by a weighted aggregation algorithm based on the allocation result of the delivery priority weight, maps the score to a preset threshold interval, for example, 0-5 points correspond to a regular level, 5-8 points correspond to an urgent level, and 8-10 points correspond to an emergency level, generates a delivery priority label, and simultaneously links the device type label generated in the early stage, such as a rigid device constraint of a refrigerated vehicle, a shockproof unmanned aerial vehicle, and the like; finally, a final delivery device type and a dynamic time limit commitment of each order are jointly output by a device time limit matching rule of the delivery priority label, such as an automatic matching of a 2-hour delivery time limit window for an emergency level and an adaptation of a same-day delivery for an urgent level, in combination with a resource limitation of the device label, to realize a collaborative decision of resources and time limits.

[0117] The above technical solution has the following beneficial effects: through a multi-dimensional decision and a dynamic weight adaptation mechanism, the resource matching accuracy and response efficiency of a logistics system are significantly improved; on one hand, a basic classification label is generated based on the time limit and the device rigidity demand of the item type, to ensure the basic adaptability of the delivery resources; on the other hand, the resource inclination for high-value and small-volume orders is realized through the elastic priority judgment of the item value and volume, a double-track decision model is formed, the preset rules and dynamic parameters are converted into quantifiable priority scores, and finally the cost waste caused by the abuse of high-end devices is avoided, and the optimal resource combination is ensured for key orders, so that collaborative optimization is realized in the dimensions of order fulfillment rate, device utilization rate, and customer satisfaction.

[0118] In one embodiment, the generation sub-module includes:

[0119] The calculation unit is configured to calculate a priority score using a delivery priority weight based on a delivery priority weight allocation result of each order;

[0120] The priority score calculated in this embodiment is dynamically compared with preset multi-level threshold intervals, such as an emergency level threshold of 90 points and an urgent level threshold of 80 points, and the abstract score is mapped to a discrete priority level through hierarchical boundary determination; then, a corresponding level of distribution priority label is automatically generated according to a level identification rule library, which serves as a unified scheduling identifier for each link in the distribution process, and drives subsequent equipment matching, path planning and time limit commitment;

[0121] The label generation unit is configured to perform level division on the priority score according to preset first and second priority threshold values, and generate a distribution priority label based on the priority level;

[0122] The determination unit is configured to obtain a distribution equipment type label of each order, determine a distribution equipment type based on the distribution equipment type label, obtain the distribution priority label of each order, and determine a distribution time limit based on a distribution time limit range corresponding to the distribution priority label;

[0123] This embodiment determines the equipment model and technical specifications by analyzing the distribution equipment type label bound to the order and accurately matching the equipment data in the logistics resource library; at the same time, the distribution priority label of the order is obtained, and mapping matching is performed based on a preset time limit rule library, such as automatically triggering a 2-hour express delivery time limit commitment for a T0 label and binding a 4-hour time limit delivery window for a T1 label, and dynamically calibrating in combination with the equipment performance boundary, to finally output a distribution time limit commitment range strictly corresponding to the label level;

[0124] The output unit is configured to output the distribution equipment type and distribution time limit information used by each order.

[0125] The above technical solution has the following beneficial effects: the priority score is quantitatively calculated based on the weight allocation result, complex multi-dimensional rules are converted into operable numerical indicators, and objective basis is provided for subsequent decision-making; the distribution priority label is generated through dynamic level division of the preset threshold, the standardization and flexibility of order classification are realized, and resource inclination for high-value orders is ensured; through the double-track matching of the equipment type label and the distribution priority label, the matching distribution equipment type is accurately locked and the time limit range is delimited, and through the matching of equipment resources and time limit requirements, the utilization rate of the transportation capacity is maximized while the fulfillment quality is ensured.

[0126] In one embodiment, the path planning module includes:

[0127] The semantic analysis sub-module is configured to perform semantic analysis on the order content to obtain volume, weight and storage requirement information of the order items;

[0128] The mapping sub-module is configured to generate a mapping table based on the order number, order address coordinates and the distribution priority label of the order;

[0129] The system of the embodiment constructs a primary key index through a unique order number, fuses high-precision address coordinates and delivery priority labels such as T0 emergency and T1 urgent to generate a dynamic mapping table; the mapping table takes spatial attributes as topological nodes and takes delivery priority labels as scheduling weights to form a data matrix with both geographic spatial relationship and time constraint, thereby providing a multi-dimensional decision basis including position, level and state for subsequent path optimization algorithms;

[0130] The path generation submodule is configured to acquire the order information mapping table and generate an optimal path sequence through cross variation based on a multi-objective optimization genetic algorithm.

[0131] The embodiment initializes a genetic algorithm population individual according to multi-dimensional data in the order information mapping table, and each individual represents a feasible path sequence; a roulette wheel selection mechanism is used to screen path individuals with better fitness, parent path gene fragments are recombined through an ordered crossover operator, and a probability mutation is introduced to break local optimality; after multiple generations of evolution iteration, the optimal solution frontier is converged, and an optimal path sequence set that balances timeliness, economy and risk is output.

[0132] The clustering optimization submodule is configured to acquire semantic analysis results of each order, perform clustering adjustment on orders with overlapping optimal path sequences according to overlapping keywords based on the semantic analysis results, and generate an optimal path for each order based on a clustering adjustment result.

[0133] The embodiment identifies order clusters with overlapping geographic trajectories in the optimal path sequence according to item feature labels generated by order semantic analysis, such as "cold chain demand", "shockproof packaging" and "dangerous goods"; through multi-dimensional feature similarity calculation, business attributes such as temperature control level and safety protection requirements are covered, and secondary dynamic clustering is performed on orders with the same path, such as merging all orders requiring -18℃ cold chain; based on the clustering result, the order sequence in the sub-path segment is locally optimized and recombined on the premise of maintaining global path efficiency, for example, temperature control orders are arranged in the central constant temperature zone of the cold chain compartment, shockproof orders avoid bumpy road sections, and finally personalized optimal paths that fuse geographic efficiency and item characteristic protection are generated.

[0134] The beneficial effects of the above technical solutions are: the order content is converted into structured item characteristic parameters, providing fine decision basis for resource matching; the mapping sub-module constructs a multi-dimensional space relationship network by integrating order numbers, geographic coordinates and delivery priority labels, and converts discrete information into a unified scheduling base; the path generation sub-module dynamically optimizes among conflicting targets such as path length, time efficiency cost and road condition complexity based on a multi-objective genetic algorithm, breaks through the limitation of local optimal solution through crossover and mutation operations, and realizes global delivery efficiency maximization; the clustering optimization sub-module dynamically clusters and reorganizes overlapping path orders according to item characteristics, so that the delivery route has local flexible adjustment capability while maintaining global optimality.

[0135] In one embodiment, the resource scheduling module comprises:

[0136] An information acquisition sub-module is configured to acquire the optimal path, delivery time limit and delivery device type of each order in the to-be-delivered orders, and acquire the device type, current position and current state of each delivery device in the delivery device pool;

[0137] An order allocation sub-module is configured to determine the current available devices based on the current state of each delivery device in the delivery device pool, and allocate orders based on the optimal path of each order in the to-be-delivered orders according to the position of the current available devices;

[0138] This embodiment first scans the working state of each device in the delivery device pool, filters out the current available device pool based on the real-time state; then acquires the accurate position coordinates of the available devices, and performs spatial topology matching with the optimal path sequence of the to-be-delivered orders, and through path coincidence degree analysis and distance cost function, dynamically allocates orders to the nearest and path direction consistent available devices, realizing the collaborative optimization of transport resources and delivery demand in the time and space dimensions;

[0139] A device allocation sub-module is configured to allocate orders to a device pool composed of delivery devices of corresponding types based on the delivery device type of each order;

[0140] This embodiment matches the to-be-delivered orders to the resource pool of corresponding type devices according to the delivery device type label bound by the to-be-delivered orders, such as "cold storage vehicle special", "unmanned aerial vehicle delivery", including cold storage vehicle pool, unmanned aerial vehicle pool, shockproof vehicle team, etc., performs secondary resource matching in the device pool dimension, i.e. cost-optimal dynamic scheduling based on real-time position, remaining capacity and task queue state among devices of the same type;

[0141] A scheduling sub-module is configured to acquire the energy reserve and energy consumption information of each delivery device in the device pool in real time, and generate a multi-objective optimization solution set based on the delivery time limit, risk value and optimal path predicted delivery energy consumption of each order as the optimization target;

[0142] The embodiment monitors the energy reserves of each delivery device in the device pool in real time, such as the electric vehicle power, the fuel vehicle fuel, and the unit mileage energy consumption data, simultaneously loads the delivery time limit requirement and the risk assessment value of each order, and constructs a multi-objective optimization model of time limit maximization, energy consumption minimization, and risk optimization based on the above, and generates a non-dominated solution set by using an evolutionary algorithm, and selects a global optimal scheduling strategy that meets the constraint conditions and balances the interests of multiple parties from the non-dominated solution set.

[0143] The optimization submodule is configured to perform target optimization based on the principle that high-priority orders are preferentially allocated to delivery devices, establish a unique correspondence between each order in the multi-objective optimization solution set and each delivery device in the multi-objective optimization solution set, perform order allocation based on the correspondence, and generate a delivery path for each delivery device.

[0144] The embodiment merges orders with a high spatiotemporal path coincidence degree higher than a preset threshold, such as orders with a path coincidence degree of greater than or equal to 80%, into intensive delivery units to reduce the unit energy consumption, strictly follows the device preemption principle of high-priority orders to ensure the high-quality service of key orders, establishes a unique mapping relationship between each order and a delivery device in the optimal solution set generated by the multi-objective algorithm, and finally generates a customized device path that takes into account the efficiency of merged delivery and the priority guarantee based on the mapping relationship.

[0145] The above technical solution has the following beneficial effects: based on the stereoscopic resource portrait of the device type, real-time position, and energy state, the accurate pre-matching of delivery capacity and order demand is achieved; through the dynamic device pool layering mechanism, high-value devices are used for specific order types to avoid performance loss caused by resource mismatch; the scheduling submodule introduces a multi-objective optimization model, combines real-time energy consumption monitoring and path prediction, and realizes dynamic balance between energy efficiency and contract stability; the optimization submodule reduces the empty running rate while guaranteeing the time limit rigidity of key orders through the order merging of spatiotemporal coincidence and the preemption allocation of high-priority orders, improves the device utilization rate, and reduces the delivery abnormal rate through the risk prediction and energy self-adaptive mechanism.

[0146] In one embodiment, the control module comprises:

[0147] The data access submodule is configured to obtain real-time traffic information of the delivery path of each delivery device through a map API interface and access current weather warning information of the delivery path of each delivery device through a weather API interface.

[0148] The calculation submodule is configured to calculate a traffic congestion index of the delivery path of each delivery device based on the real-time traffic information and the weather warning information.

[0149] This embodiment constructs a dynamic road condition assessment model by combining real-time traffic information, including average road travel speed, congestion mileage ratio, and traffic flow, with meteorological early warning information, such as rainfall intensity, visibility, and road icing risk. Based on the speed thresholds for different road grades and meteorological influencing factors, it calculates the traffic congestion index of each delivery equipment's planned route. This index comprehensively reflects the congestion level of the entire route (0-10 levels, corresponding to smooth to severe congestion).

[0150] The judgment submodule is used to segment the delivery path of each delivery device, judge the traffic congestion index of the segmented delivery path, and classify the road segments with the traffic congestion index above a preset threshold as impassable road segments.

[0151] This embodiment performs spatial segmentation processing on the planned path of each delivery device based on the GIS topology network, dividing the continuous driving route into discrete sub-segments according to road intersections or mileage markers; then, it integrates real-time traffic information and meteorological early warning data to calculate the traffic congestion index for each sub-segment; based on the preset impassable threshold, such as expressway index ≥7.5 and secondary arterial road index ≥8.5, the sub-segments exceeding the standard are marked as impassable sections, and an avoidance heat map grid is automatically constructed to provide accurate spatial obstacle constraints for subsequent dynamic route replanning;

[0152] The route replanning submodule is used to use AI to avoid the impassable road sections and re-find the route. Based on the new route, it re-plans the order delivery order according to the order address coordinates corresponding to this delivery device.

[0153] This embodiment uses an artificial intelligence path planning model to recalculate the optimal detour route for delivery equipment based on a dynamically updated grid map of impassable road segments. Simultaneously, based on the spatial topology of the new route, inflection points, distances, travel times, and the geographical coordinate distribution characteristics of the orders carried by the equipment, the vehicle routing problem is resolved. Under the premise of meeting the delivery time constraints, the order delivery sequence is replanned, achieving coordinated optimization of detour and sequence adjustment.

[0154] The beneficial effects of the above technical solutions are as follows: Based on the real-time risk perception system of map API and meteorological API, a three-dimensional early warning network for traffic conditions and meteorological disasters is constructed, providing updated dynamics for route decision-making; the quantitative modeling of congestion index and the route segmentation mechanism transform complex road conditions into decision parameters that can be processed in a graded manner, accurately identifying impassable road sections through threshold judgment, and realizing the transformation of risk from fuzzy early warning to spatial positioning; the AI ​​dynamic replanning engine integrates avoidance logic and order time constraints, and simultaneously completes route topology reconstruction and delivery sequence optimization within seconds, reducing the delivery anomaly rate caused by extreme weather or sudden congestion, and improving the overall delivery efficiency after dynamic adjustment compared with traditional manual scheduling.

[0155] In one embodiment, the verification module comprises:

[0156] The signed confirmation sub-module is configured to synchronously acquire the customer electronic signature, customer use score, delivery record and goods status photo as delivery proof when the order is delivered;

[0157] The system verification sub-module is configured to compare the planned delivery time with the actual signed receipt time, automatically mark the delayed order, and verify the planning coincidence of the delivery path of each order.

[0158] The embodiment compares the promised delivery timestamp of the order with the actual signed receipt timestamp, automatically calculates the time deviation value such as the delay minutes, and triggers the order delay marking according to the preset delay tolerance threshold. At the same time, based on the GIS spatial analysis engine, the actual running track of the device is overlaid and calculated with the initial planning path in space topology, the path execution compliance is quantified through the track point coincidence rate (such as path matching degree < 85%), key node offset distance and other indicators, the deviation rationality is verified in combination with the real-time road condition, and finally the order fulfillment evaluation report with delay identification and path deviation analysis is output.

[0159] The index analysis sub-module is configured to acquire the timeliness, integrity, quality compliance rate and user satisfaction index of each order, analyze the qualified rate and offset degree of the index of each order, and analyze the reasons of the unqualified orders according to the characteristics.

[0160] The embodiment acquires the four core indexes of timeliness, integrity, quality compliance rate and user satisfaction of each order in real time, determines the single-item qualified rate based on the preset threshold, such as that the timeliness deviation is less than 4 hours, and calculates the offset degree of the actual value and the target value of each index. For unqualified orders, the system applies clustering algorithm to automatically group according to characteristics such as supplier category, logistics path complexity and quality inspection problem type, and identifies high-frequency failure modes such as specific regional delivery delay and packaging damage concentration.

[0161] The updating sub-module is configured to perform algorithm feature optimization according to the reason analysis result, and perform system updating on the intelligent delivery device path control system based on the algorithm feature optimization result.

[0162] The embodiment drives the dynamic optimization of the algorithm model according to the reasons revealed by the clustering analysis, such as strengthening the regional road condition weight coefficient of the timeliness prediction module and adding the constraint of avoiding bumpy road sections in path planning. At the same time, based on the optimized algorithm feature set, the decision core of the intelligent delivery system is parameter migrated and model hot updated through online hot deployment mechanism, so as to realize the ability evolution of the system to continuously adapt to complex scenes.

[0163] The beneficial effects of the above technical solutions are: based on the synchronization solidification of multi-dimensional delivery certificates such as electronic signatures and cargo state photos, the traceability of end performance is realized, the compliance review of delivery execution and preset rules is completed through time deviation automatic marking and path coincidence degree verification; dynamic indicators such as timeliness and integrity are fused to analyze and locate the distribution rule of abnormal orders by offset degree, and the accurate source of the problem is realized through feature clustering; finally, the update submodule converts the clustering results into algorithm feature optimization parameters to drive the path control system to dynamically iterate, thereby reducing the abnormal recurrence rate and improving user satisfaction while continuously enhancing the adaptability of the system to complex scenarios.

[0164] In one embodiment, the system further comprises an emergency module for detecting the real-time state of the delivery device based on the sensors built into the delivery device, generating a real-time detection result of the delivery device, and starting emergency management measures when the real-time detection result of the delivery device is abnormal, which is configured to:

[0165] Obtain the type of each delivery device, determine the parts that need to be installed with sensors based on the type of the delivery device, and determine the target sensors to be installed for each part based on the parameter types detected by each part;

[0166] This embodiment identifies the type of each delivery device, such as AGV, drone, delivery robot, etc., analyzes the key parts that need to be monitored based on the device type, such as drive motor, battery module, steering mechanism, cargo compartment door lock, etc.; then according to the specific parameters that need to be detected by the parts, such as motor temperature and speed, battery voltage and temperature, mechanical arm stress, environmental temperature and humidity, obstacle distance, etc., determine the target sensor type that fits, such as thermocouple to monitor motor temperature rise, Hall sensor to collect speed, infrared sensor to detect near-field obstacles, IMU to detect vehicle attitude, laser radar to construct environment point cloud;

[0167] Determine the fault weight level corresponding to each part based on the preset part fault weight level library, and determine the data acquisition frequency of each target sensor according to the detection frequency corresponding to the fault weight level;

[0168] This embodiment allocates corresponding fault weight levels to various parts according to the preset part fault weight level library, such as key level, important level, and general level, through fault occurrence rate, downtime impact, and safety risk; based on the detection frequency requirement mapped by the weight level, such as high-frequency real-time monitoring of key-level parts, and combined with the parameter characteristics of the target sensor, the data acquisition frequency of each sensor is dynamically set;

[0169] The data collected by the target sensor is cleaned and filtered, and a preset rule engine is used to make an abnormality judgment on the real-time data of the sensor;

[0170] The embodiment performs data cleaning on the multi-modal data stream collected by the target sensor, such as vibration, temperature, pressure and other physical quantities, removes outliers, fills in missing values and corrects drift data; then applies an adaptive filtering algorithm to improve signal quality; the cleaned data is subjected to real-time abnormality judgment through a pre-set rule engine, which integrates threshold triggering, statistical rules (such as data outside the standard deviation for three consecutive times) and pattern matching (such as vibration spectrum feature abnormality) and other multi-dimensional logic, and outputs abnormal confidence score and type label in real time;

[0171] The abnormality judgment result is determined as the real-time detection result of the distribution equipment, and when the real-time detection result of the distribution equipment is abnormal, the component with the abnormality judgment result is determined as an abnormal component;

[0172] Based on the pre-set component failure weight level library, the failure weight level corresponding to the abnormal component is obtained, and the abnormal component with a pre-set failure weight level is determined as a repairable abnormal component, and other abnormal components are determined as non-stop abnormal components;

[0173] The embodiment determines the pre-set weight level of the abnormal component based on the failure weight level library, such as the key level and the important level, and classifies the abnormal components with a weight level lower than the pre-set threshold as repairable abnormal components; for abnormal components with a weight equal to or higher than the threshold, such as key level power system failure, the non-stop abnormal component is forcibly marked; the classification decision maps a differentiated emergency response strategy, the repairable abnormal component triggers the nearest repair instruction and maintains partial function operation, and the non-stop abnormal component immediately freezes the task and starts the equipment replacement process;

[0174] According to the abnormal level judgment result, corresponding emergency management measures are executed, the repairable abnormal component is pushed to the nearest repair point for repair, and the non-stop abnormal component is transferred by calling the nearest distribution equipment in the distribution equipment library;

[0175] An abnormal report and a predicted delay time are pushed to the user corresponding to each order of the abnormal distribution equipment.

[0176] The embodiment automatically identifies all in-transit orders corresponding to the abnormal distribution equipment, such as 10 fresh orders carried by the cold chain temperature control abnormal vehicle, calls the order management interface to obtain the user contact information and order details; generates a personalized abnormal report containing the failure type, emergency disposal scheme and predicted delivery time after re-planning; synchronously pushes the user through the APP pop-up window, SMS and voice call three channels, provides a compensation option, and opens a real-time tracking page of the logistics track.

[0177] The beneficial effects of the above technical solutions are: based on the dynamic configuration of the sensor network according to the equipment type and the key part, the high-weight part can obtain a higher frequency of health state scanning, and the fault can be captured at the fault budding stage; through the hierarchical response strategy driven by the fault weight, the abnormality is divided into "repairable level" and "need to stop level", which can avoid the over-disposal of low-risk faults, and can forcibly start the equipment replacement and order transfer for high-risk faults, so as to minimize the risk of chain distribution interruption; in combination with the real-time diagnosis and resource scheduling of the rule engine, the multi-dimensional abnormal logic is embedded in the cleaned and filtered data stream, the maintenance resource library and the spare equipment pool are synchronously linked, and the efficiency is improved compared with the traditional manual investigation; the user-side transparent emergency management can convert passive complaints into active service cooperation through real-time pushing of abnormal reports and delayed estimation, so as to significantly improve customer trust, and finally form a fault immune network, so as to achieve systematic optimization in the aspects of reducing equipment downtime, compressing maintenance response window, and guaranteeing order fulfillment rate.

[0178] In one embodiment, the system further comprises a label writing module configured to write the transportation node, the transportation state and the delivery equipment information into each order corresponding delivery goods using NFC label technology, and automatically push the transportation report to each order corresponding mobile terminal interface, and configured to:

[0179] A unique key pair is generated for each NFC label, and a digital certificate is issued by a logistics server to bind the correspondence between each order and the NFC label;

[0180] In this embodiment, a unique asymmetric key pair is dynamically generated for each NFC label, including a private key and a public key, and a digital certificate is issued for the public key by a logistics server, the certificate including core information such as label unique identification code UID, order number, and validity period; the server writes the certificate into the encrypted storage area of the label, and establishes a binding relationship between the label UID, the certificate fingerprint and the order details, so as to ensure that the logistics flow data of each order is strongly associated with a specific NFC label through a digital signature mechanism and cannot be tampered with;

[0181] The NFC label is used to write order basic information and an initial state code when the goods of each order are collected, and the order basic information includes an order number, encrypted IDs of a sender and a receiver, and a cargo type;

[0182] The NFC label is used to write order basic information and an initial state code when the goods of each order are collected, and the order basic information includes an order number, encrypted IDs of a sender and a receiver, and a cargo type;

[0183] The embodiment collects the NFC read-write module integrated with the collection equipment and the NFC tag of the order package to perform near field communication, and after the bidirectional identity authentication is passed, the key business metadata, the unique code of the sorting equipment and the high-precision time stamp of the time operation are synchronously written in the secure storage area of the tag, and the digital signature of the operator is additionally attached, to form a chain tracking anchor point containing the operation subject, the space-time coordinates and the trusted certificate;

[0184] After each order item enters the transportation process, the transportation equipment code and the GPS position are automatically synchronized to the NFC tag by the transportation equipment;

[0185] After the order enters the transportation process, the NFC read-write module built in the transportation equipment such as the cold chain vehicle / unmanned aerial vehicle automatically establishes near field communication with the cargo tag, and after the bidirectional encryption authentication ensures the data safety, the unique code of the transportation equipment and the real-time GPS positioning coordinates are synchronously written in the encrypted storage partition of the NFC tag; and the time stamp and the equipment state code are additionally attached to form the space-time dynamic branding of the logistics mobile node;

[0186] The corresponding item type of each order is acquired, the transportation condition is judged, and when it is judged that the item type needs special transportation environment, the internal environment data of the distribution equipment is written into the NFC tag by the sensor corresponding to the special transportation environment;

[0187] The embodiment is based on the order item type, such as biological preparation and precision instrument, to judge the transportation environment requirement, such as constant temperature, shockproof and sterile, and when the special transportation requirement is identified, the special sensor network built in the distribution equipment is automatically activated; the environment data stream is aggregated in real time by the edge computing unit of the equipment, and after being encrypted by AES-128, the environment data stream is written into the encrypted storage partition of the NFC tag, and the sensor ID and the time stamp are additionally attached to form the unforgeable environment history;

[0188] Each node equipment and the NFC tag verify the identity through the response protocol, and after the authentication, a temporary session key is generated to encrypt the transmission of the node data corresponding to each node equipment;

[0189] Before each logistics node performs the operation, the node equipment needs to perform bidirectional authentication with the NFC tag: the equipment sends a dynamic random number challenge instruction, the tag uses a private key to sign a response and returns a digital certificate; after the authentication is passed, a unique temporary session key is generated to encrypt the real-time transmission of the node exclusive data, and the time stamp is additionally attached to ensure the confidentiality and integrity of the node data interaction;

[0190] When the tag state code is changed, the server automatically initiates a mobile terminal push to each order;

[0191] The embodiment detects the state code of the NFC tag, such as "unused", "sealed", "unsealed", "abnormal" and the like, and when the state code changes, the logistics server automatically triggers the message push engine to push a state update notification to the order associated user mobile terminal bound to the tag through real-time monitoring of the change event of the tag state database; the push content includes the order number, the changed state value, the timestamp and the operation node information, and is transmitted through the long connection channel encryption, thereby realizing real-time transparent synchronization of the logistics whole-process state.

[0192] When the user touches the tag at the time of receiving the goods, the mobile terminal decrypts the NFC tag data, and compares the consistency of the server report and the local NFC tag data.

[0193] In the embodiment, at the receiving link, the user touches the NFC tag of the goods through the mobile terminal supporting the NFC function, and triggers automatic reading of the encrypted data in the tag; the mobile terminal calls the local security module to decrypt the data using the preset key, and simultaneously initiates a verification request to the logistics server; the mobile terminal compares the decrypted local tag data with the server report in multiple dimensions, and if the consistency verification passes, a green verification mark and logistics details are displayed, otherwise, an alarm prompt information exception is triggered.

[0194] The beneficial effects of the above technical scheme are as follows: based on the binding mechanism of the unique key pair and the digital certificate, an unalterable corresponding relationship between the NFC tag and the order system is established, so that the risk of tag forgery and data tampering is eliminated from the source; the multi-node collaborative writing mechanism makes the tag become a mobile distributed database, and the temporary session key is used for encrypted transmission, so that the safety of data interaction between nodes is ensured, and real-time environmental monitoring of special needs such as temperature-sensitive goods is realized; the two-way verification mechanism builds a closed-loop evidence chain from storage to delivery, so that any abnormal state change can be captured and automatically pushed; the final user touch verification link converts the traditional passive signing into active data right, and through the cross verification of local decryption and cloud report, the consumer trust is improved, and the electronic evidence that cannot be denied for dispute tracing is provided.

[0195] Those skilled in the art should understand that the first and second in the present application refer to different application stages.

[0196] Other embodiments of the present disclosure will be readily apparent to those skilled in the art upon considering the description and practicing the disclosure herein. The present application is intended to cover any variations, uses or adaptive changes to the present disclosure that follow the general principles of the present disclosure and include common knowledge or conventional technical means in the technical field not disclosed by the present disclosure. The specification and examples are only regarded as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

[0197] It should be understood that the present disclosure is not limited to the precise construction that has been described above and shown in the accompanying drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the present disclosure. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A path control system for intelligent delivery equipment, characterized in that, include: The order acquisition module retrieves orders for logistics and delivery, and performs address parsing on the orders to obtain the order address coordinates; The module determines the type of delivery equipment and delivery time for each order, and obtains the delivery priority analysis results; The route planning module performs intelligent route planning based on order address coordinates and delivery priority analysis results. The resource scheduling module schedules delivery equipment based on the type of items in the order. The control module controls delivery routes in real time based on real-time traffic conditions, including: a real-time risk perception system based on map API and meteorological API to build a three-dimensional early warning network for traffic conditions and meteorological disasters; identifying impassable road sections through threshold judgment; using AI to avoid impassable road sections and re-find routes, and re-planning the order delivery sequence based on the new routes; The verification module verifies the delivery results of ordered items and provides feedback on delivery performance and user experience. The emergency module uses built-in sensors to monitor the real-time status of the delivery equipment. It determines the fault weight level of each component based on a pre-defined fault weight level library and determines the data acquisition frequency of each target sensor according to the corresponding detection frequency. When the real-time monitoring results of the delivery equipment show an anomaly, it identifies the abnormal component. Based on the fault weight level library, it locates the pre-defined weight level of the abnormal component. Components below the pre-defined fault weight level are identified as repairable anomalies, while other abnormal components are identified as requiring shutdown. Corresponding emergency management measures are then implemented based on the anomaly level. The tag writing module uses NFC tags to write transportation node, transportation status and delivery equipment information into the delivery items corresponding to each order, generates a unique key pair for each NFC tag, and verifies the identity of each node device with the NFC tag through the response protocol. After authentication, a temporary session key is generated to encrypt and transmit the node data of each node device.

2. The intelligent delivery equipment path control system according to claim 1, characterized in that, The order acquisition module includes: The acquisition submodule filters orders with a status of "pending shipment" through the order API interface of the e-commerce platform and identifies them as orders to be delivered, and obtains the specific content of the orders to be delivered; The extraction submodule is used to extract the address field from the orders to be delivered, remove garbled characters and special symbols from the address, and standardize abbreviations. The address resolution submodule is used to split the elements of the address field using natural language processing algorithms, verify the validity by combining the address knowledge base, automatically correct errors, and generate a structured address. The coordinate matching submodule is used to call the map service API to convert the structured address into latitude and longitude coordinates, perform geographic location accuracy matching, prioritize matching the highest accuracy coordinates, and if the matching fails, it will be downgraded to the community, street and district level coordinates to generate address coordinates.

3. The intelligent delivery equipment path control system according to claim 1, characterized in that, The determining module includes: The first classification submodule is used to classify delivery time and delivery equipment according to the type of items in the order to be delivered, and generate the first classification result; The second classification submodule is used to classify delivery priorities based on the value and volume of the items in the order to be delivered, and generate the second classification result; The weight allocation submodule is used to allocate delivery priority weights based on the preset priority weights of the first classification result and the second classification result for each order, and to generate delivery equipment type labels based on the first classification result. The generation submodule is used to calculate the priority score based on the delivery priority weight allocation result of each order, generate a delivery priority label, obtain a delivery equipment type label, and determine the delivery equipment type and delivery time for each order based on the delivery priority label and the delivery equipment type label.

4. The intelligent delivery equipment path control system according to claim 3, characterized in that, The generation submodule includes: The calculation unit is used to calculate the priority score based on the delivery priority weight allocation result for each order. The tag generation unit is used to classify the priority score into levels according to a preset first priority threshold and a second priority threshold, and generate delivery priority tags based on the priority levels. The determining unit is used to obtain the delivery equipment type label for each order, determine the delivery equipment type based on the delivery equipment type label, obtain the delivery priority label for each order, and determine the delivery time based on the delivery time range corresponding to the delivery priority label; The output unit is used to output the type of delivery equipment used for each order and the delivery time information.

5. The intelligent delivery equipment path control system according to claim 4, characterized in that, The path planning module includes: The semantic parsing submodule is used to perform semantic parsing on the order content to obtain information such as the volume, weight, and storage requirements of the items in the order. The mapping submodule is used to generate a mapping table based on the order number, order address coordinates, and the order's delivery priority tag; The path generation submodule is used to obtain the order information mapping table and generate the optimal path sequence through crossover and mutation based on a multi-objective optimization genetic algorithm. The clustering optimization submodule is used to obtain the semantic parsing result of each order, and based on the semantic parsing result, to cluster and adjust the orders that overlap with the optimal path sequence according to the overlapping keywords, and to generate the optimal path for each order based on the clustering adjustment result.

6. The intelligent delivery equipment path control system according to claim 1, characterized in that, The resource scheduling module includes: The information acquisition submodule is used to obtain the optimal route, delivery time and delivery equipment type for each order in the order to be delivered, and to obtain the equipment type, current location and current status of each delivery equipment in the delivery equipment library; The order allocation submodule is used to determine the currently available equipment based on the current status of each delivery equipment in the delivery equipment library, and to allocate orders based on the optimal path of each order in the orders to be delivered, according to the location of the currently available equipment. The equipment allocation submodule is used to allocate orders to a pool of equipment consisting of the corresponding type of delivery equipment based on the delivery equipment type of each order; The scheduling submodule is used to obtain the energy reserves and energy consumption information of each delivery device in the device pool in real time, and generate a multi-objective optimization solution set based on the delivery timeliness, risk value, and estimated delivery energy consumption of the optimal route for each order. The optimization submodule is used to optimize the objectives based on the principle of merging and delivering orders with an optimal path overlap greater than a preset threshold and prioritizing the allocation of delivery equipment for orders with high delivery priority. It establishes a unique correspondence between each order in the multi-objective optimization solution set and each delivery device in the multi-objective optimization solution set, allocates orders based on the correspondence, and generates a delivery path for each delivery device.

7. The intelligent delivery equipment path control system according to claim 1, characterized in that, The control module includes: The data access submodule is used to obtain real-time traffic information of the delivery route of each delivery device through the map API interface, and to access the current weather warning information of the delivery route of each delivery device through the weather API interface. The calculation submodule is used to calculate the traffic congestion index of the delivery route of each delivery device based on the real-time traffic information and the meteorological early warning information; The judgment submodule is used to segment the delivery path of each delivery device, judge the traffic congestion index of the segmented delivery path, and classify the road segments with the traffic congestion index above a preset threshold as impassable road segments. The route replanning submodule is used to use AI to avoid the impassable road sections and find new routes. Based on the new routes, the order delivery order is replanned according to the order address coordinates corresponding to this delivery device.

8. The intelligent delivery equipment path control system according to claim 1, characterized in that, The verification module includes: The receipt confirmation submodule is used to simultaneously obtain the customer's electronic signature, customer usage rating, delivery record, and goods status photos as delivery proof when the order is delivered; The system verification submodule is used to compare the planned delivery time with the actual receipt time, automatically mark delayed orders, and verify the overlap of the planned delivery route for each order. The indicator analysis submodule is used to obtain the timeliness, completeness, quality compliance rate and user satisfaction indicators for each order, perform pass rate and deviation analysis on the indicators of each order, and cluster non-conforming orders by feature for cause analysis. The update submodule is used to optimize algorithm features based on the cause analysis results, and to update the intelligent delivery equipment path control system based on the algorithm feature optimization results.

9. The intelligent delivery equipment path control system according to claim 1, characterized in that, The system also includes an emergency module, configured to detect the real-time status of the delivery equipment based on its built-in sensors, generate real-time detection results, and activate emergency management measures when the real-time detection results show an anomaly. Obtain the delivery equipment type corresponding to each delivery device, determine the components that need to be equipped with sensors based on the delivery equipment type, and determine the target sensor to be installed on each component based on the parameter type detected by each component. The fault weight level of each component is determined based on a preset component fault weight level library, and the data acquisition frequency of each target sensor is determined based on the detection frequency corresponding to the fault weight level. The data collected by the target sensor is cleaned and filtered, and a preset rule engine is used to judge anomalies in the real-time sensor data. The anomaly judgment result is determined as the real-time detection result of the delivery equipment. When the real-time detection result of the delivery equipment is abnormal, the parts with the abnormal judgment result are determined as abnormal parts. Based on a preset component fault weight level library, the fault weight level corresponding to the abnormal component is obtained. The abnormal components below the preset fault weight level are identified as repairable abnormalities, and the other abnormal components are identified as out-of-operation abnormalities. Based on the anomaly level judgment result, corresponding emergency management measures are executed. For repairable anomalies, an anomaly report is pushed to the nearest repair point for repair. For anomalies requiring shutdown, the nearest delivery equipment in the delivery equipment warehouse is called for transfer. An error report and estimated delay time are pushed to the user corresponding to each order on the abnormal delivery device.

10. The intelligent delivery equipment path control system according to claim 1, characterized in that, The system also includes a tag writing module, used to write transportation node, transportation status, and delivery equipment information to the delivery item corresponding to each order using NFC tag technology, and automatically push transportation reports to the mobile interface corresponding to each order, configured as follows: A unique key pair is generated for each NFC tag, and a digital certificate is issued by the logistics server to bind the correspondence between each order and the NFC tag; When the items for each order are picked up, an NFC tag is used to write the basic order information and initial status code. The basic order information includes the order number, sender and receiver encrypted IDs, and goods type. When each order item is picked up, the sorting device ID and operation timestamp are written into the NFC tag via the NFC writing device of the pickup equipment. After each order item enters the transportation process, the transportation equipment code and GPS location are automatically synchronized to the NFC tag through the transportation equipment; Obtain the corresponding item type for each order, determine the transportation conditions, and when it is determined that the item type requires a special transportation environment, write the sensor detection data of the internal environment of the delivery equipment into the NFC tag through the sensor corresponding to the special transportation environment. Each node device verifies its identity with the NFC tag through a response protocol. After authentication, a temporary session key is generated, and the node data corresponding to each node device is transmitted in encrypted form. When the tag status code changes, the server automatically sends a push notification to the mobile app for each order. When receiving goods, the user touches the tag, and the mobile device decrypts the NFC tag data and compares it with the server report to ensure consistency with the local NFC tag data.

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