Logistics vehicle unloading efficiency optimization method and device, equipment and storage medium

Abstract

The invention relates to the technical field of logistics transportation, in particular to a logistics vehicle unloading efficiency optimization method, device and equipment and a storage medium, unloading related data of a vehicle is obtained in real time, a preset time sequence processing model is used for carrying out time calibration on unloading related preprocessing data, the timeliness and accuracy of the data are improved, and the unloading efficiency of the vehicle is improved. On the basis of a preset overtime non-unloading index, a sliding window algorithm is adopted to calculate unloading related preprocessed data, real-time overtime non-unloading information is obtained, the overtime non-unloading visual information is displayed on an unloading condition multi-dimensional query page in real time through a data binding technology, a multi-dimensional data query function is provided, and the real-time non-unloading information is obtained. Management personnel are helped to accurately locate problem areas and vehicles, unloading efficiency analysis is carried out on real-time overtime non-unloading information, an optimization suggestion strategy is generated according to a key factor analysis result, the unloading process can be dynamically adjusted according to the optimization suggestion strategy, the unloading efficiency is improved, the operation cost is reduced, and the competitiveness of logistics service is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics transportation, and in particular to a method, device, equipment and storage medium for optimizing the unloading efficiency of logistics vehicles. Background Art

[0002] Currently, during the logistics transportation process, the unloading efficiency is an important factor affecting the logistics operation cost and service quality. However, there are many deficiencies in the traditional monitoring methods for the unloading efficiency of logistics vehicles. Existing monitoring systems usually cannot track the vehicle unloading status in real time, resulting in difficulties for management personnel to discover and solve unloading delay problems in a timely manner, reducing the timeliness and accuracy of vehicle unloading delay data. Moreover, the statistical dimension of vehicle unloading delay data is single, and it cannot be analyzed from multiple levels (such as large regions, provinces, sorting centers, etc.), making it difficult to accurately locate the problem area and unable to provide a query function for multi-dimensional data. Management personnel are difficult to accurately locate the problem area and vehicles. In addition, there is a lack of effective optimization strategies, and the unloading process cannot be dynamically adjusted according to real-time data, reducing the unloading efficiency, increasing the operation cost, and reducing the competitiveness of logistics services. Summary of the Invention

[0003] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method, device, equipment and storage medium for optimizing the unloading efficiency of logistics vehicles, which realizes real-time monitoring of the unloading efficiency of logistics vehicles, improves the timeliness and accuracy of data, provides multi-dimensional data analysis and query functions, helps management personnel accurately locate the problem area and vehicles, can dynamically adjust the unloading process according to the optimization suggestion strategy, improves the unloading efficiency, reduces the operation cost, and enhances the competitiveness of logistics services.

[0004] The first aspect of the present invention provides a method for optimizing the unloading efficiency of logistics vehicles, including: obtaining the unloading-related data of the vehicle in real time from the handover note service system, preprocessing the unloading-related data to obtain preprocessed unloading-related data; using a preset time series processing model to perform time calibration on the preprocessed unloading-related data, and based on a preset timeout-unloaded vehicle index, using a sliding window algorithm to calculate the preprocessed unloading-related data to obtain real-time timeout-unloaded vehicle information; loading a visualization mapping template, mapping the real-time timeout-unloaded vehicle information into the visualization mapping template to obtain timeout-unloaded vehicle visualization information; constructing a multi-dimensional query page for the unloading situation using a front-end framework, and displaying the timeout-unloaded vehicle visualization information on the multi-dimensional query page for the unloading situation in real time through data binding technology; performing unloading efficiency analysis on the real-time timeout-unloaded vehicle information to obtain a key factor analysis result, and generating an optimization suggestion strategy according to the key factor analysis result.

[0005] Optionally, in the first implementation manner of the first aspect of the present invention, the method of obtaining the vehicle unloading-related data in real time from the handover list service system and preprocessing the unloading-related data to obtain preprocessed unloading-related data includes: obtaining the vehicle unloading-related data in real time from the handover list service system; performing data cleaning on the unloading-related data to obtain cleaned unloading-related data; filling in missing values in the cleaned unloading-related data to obtain filled unloading-related data; and performing format conversion on the filled unloading-related data based on a preset target standard format to obtain preprocessed unloading-related data.

[0006] Optionally, in the second implementation manner of the first aspect of the present invention, the method of performing time calibration on the preprocessed unloading-related data by using a preset time series processing model and calculating the preprocessed unloading-related data by using a sliding window algorithm based on a preset timeout for non-unloading indicator to obtain real-time timeout for non-unloading information includes: performing time calibration on the preprocessed unloading-related data by using a preset time series processing model; calculating the preprocessed unloading-related data by using a sliding window algorithm based on a preset timeout for non-unloading indicator to obtain real-time timeout for non-unloading information; and saving the real-time timeout for non-unloading information to a database.

[0007] Optionally, in the third implementation manner of the first aspect of the present invention, the method of loading a visualization mapping template and mapping the real-time timeout for non-unloading information into the visualization mapping template to obtain visualized timeout for non-unloading information includes: loading a corresponding visualization mapping template from a template library according to the information type of the real-time timeout for non-unloading information; identifying the visual elements of the visualization mapping template; and mapping the real-time timeout for non-unloading information into the visual elements of the visualization mapping template to obtain visualized timeout for non-unloading information.

[0008] Optionally, in the fourth implementation manner of the first aspect of the present invention, the method of constructing a multi-dimensional query page for unloading situation by using a front-end framework and displaying the visualized timeout for non-unloading information on the multi-dimensional query page for unloading situation in real time through data binding technology includes: constructing a multi-dimensional query page for unloading situation by using a front-end framework; displaying the visualized timeout for non-unloading information on the multi-dimensional query page for unloading situation in real time through data binding technology, and establishing a real-time communication connection with the multi-dimensional query page for unloading situation by using WebSocket technology; listening for data change information, and when the data change information is detected, pushing the latest data to the multi-dimensional query page for unloading situation; and setting data caching on the multi-dimensional query page for unloading situation, and when the latest data is received, updating the cached data by using the latest data.

[0009] Optionally, in the fifth implementation manner of the first aspect of the present invention, the analyzing the unloaded vehicle efficiency of the real-time overtime unloaded vehicle information to obtain a key factor analysis result, and generating an optimization recommendation strategy according to the key factor analysis result includes: obtaining historical overtime unloaded vehicle information, and mining the correlation between the unloaded vehicle efficiency and different factors in the historical overtime unloaded vehicle information; analyzing the unloaded vehicle efficiency of the real-time overtime unloaded vehicle information according to the correlation to obtain a key factor analysis result, where the key factor analysis result is one of the distribution center operation efficiency problem and the vehicle scheduling problem; when the key factor analysis result is the distribution center operation efficiency problem, the process mining algorithm is used to analyze the operation process of the distribution center to obtain an operation process analysis result, and the bottleneck link in the operation process analysis result is identified; generating an operation process optimization recommendation according to the bottleneck link, where the operation process optimization recommendation includes adjusting the operation order and adding operation equipment; when the key factor analysis result is the vehicle scheduling problem, the genetic algorithm is used and combined with the real-time vehicle position information, the unloaded vehicle duration information and the cargo weight information to generate a vehicle scheduling optimization recommendation.

[0010] Optionally, in the sixth implementation manner of the first aspect of the present invention, after the analyzing the unloaded vehicle efficiency of the real-time overtime unloaded vehicle information to obtain a key factor analysis result, and generating an optimization recommendation strategy according to the key factor analysis result, it further includes: sending the optimization recommendation strategy to the management terminal; receiving the strategy execution situation information sent by the management terminal, and analyzing the execution effect of the strategy execution situation information; encrypting the execution effect to obtain an execution effect encrypted information, and uploading the execution effect encrypted information to the blockchain.

[0011] The second aspect of the present invention provides a logistics vehicle unloaded vehicle efficiency optimization device, including: an acquisition and processing module, configured to obtain the unloaded vehicle related data of the vehicle in real time from the handover order service system, and preprocess the unloaded vehicle related data to obtain preprocessed unloaded vehicle related data; a calibration and calculation module, configured to perform time calibration on the preprocessed unloaded vehicle related data by using a preset time series processing model, and calculate the preprocessed unloaded vehicle related data by using a sliding window algorithm based on a preset overtime unloaded vehicle index to obtain real-time overtime unloaded vehicle information; a loading and mapping module, configured to load a visualization mapping template, and map the real-time overtime unloaded vehicle information into the visualization mapping template to obtain overtime unloaded vehicle visualization information; a construction and display module, configured to construct a multi-dimensional query page of the unloaded vehicle situation by using a front-end framework, and display the overtime unloaded vehicle visualization information on the multi-dimensional query page of the unloaded vehicle situation in real time through data binding technology; an analysis and generation module, configured to analyze the unloaded vehicle efficiency of the real-time overtime unloaded vehicle information to obtain a key factor analysis result, and generate an optimization recommendation strategy according to the key factor analysis result.

[0012] Optionally, in the first implementation manner of the second aspect of the present invention, the acquisition and processing module includes: an acquisition unit for real-time acquiring the unloading-related data of the vehicle from the handover list service system; a cleaning unit for cleaning the unloading-related data to obtain cleaned unloading-related data; a filling unit for filling the missing values in the cleaned unloading-related data to obtain filled unloading-related data; and a conversion unit for converting the format of the filled unloading-related data based on a preset target standard format to obtain preprocessed unloading-related data.

[0013] Optionally, in the second implementation manner of the second aspect of the present invention, the calibration and calculation module includes: a calibration unit for calibrating the time of the preprocessed unloading-related data by using a preset time series processing model; a calculation unit for calculating the preprocessed unloading-related data based on a preset overtime-unloaded vehicle index by using a sliding window algorithm to obtain real-time overtime-unloaded vehicle information; and a storage unit for storing the real-time overtime-unloaded vehicle information in a database.

[0014] Optionally, in the third implementation manner of the second aspect of the present invention, the loading and mapping module includes: a loading unit for loading a corresponding visualization mapping template from a template library according to the information type of the real-time overtime-unloaded vehicle information; an identification unit for identifying the visual elements of the visualization mapping template; and a mapping unit for mapping the real-time overtime-unloaded vehicle information to the visual elements of the visualization mapping template to obtain overtime-unloaded vehicle visualization information.

[0015] Optionally, in the fourth implementation manner of the second aspect of the present invention, the construction and display module includes: a construction unit for constructing a multi-dimensional query page for the unloading situation by using a front-end framework; a display establishment unit for real-time displaying the overtime-unloaded vehicle visualization information on the multi-dimensional query page for the unloading situation through data binding technology and establishing a real-time communication connection with the multi-dimensional query page for the unloading situation by using WebSocket technology; a monitoring and pushing unit for monitoring data change information and pushing the latest data to the multi-dimensional query page for the unloading situation when the data change information is monitored; and a setting and updating unit for setting data caching on the multi-dimensional query page for the unloading situation and updating the cached data with the latest data when the latest data is received.

[0016] Optionally, in the fifth implementation manner of the second aspect of the present invention, the analysis and generation module includes: an acquisition and mining unit, configured to acquire historical information on unloaded vehicles overdue, and mine the correlation relationship between the unloading efficiency and different factors in the historical information on unloaded vehicles overdue; an analysis unit, configured to perform unloading efficiency analysis on the real-time information on unloaded vehicles overdue according to the correlation relationship to obtain a key factor analysis result, where the key factor analysis result is one of the operation efficiency problem and vehicle scheduling problem of the distribution center; an analysis and recognition unit, configured to, when the key factor analysis result is the operation efficiency problem of the distribution center, analyze the operation process of the distribution center by using a process mining algorithm to obtain an operation process analysis result, and identify the bottleneck link in the operation process analysis result; a first generation unit, configured to generate an operation process optimization suggestion according to the bottleneck link, where the operation process optimization suggestion includes adjusting the operation sequence and adding operation equipment; a second generation unit, configured to, when the key factor analysis result is the vehicle scheduling problem, use a genetic algorithm and combine the real-time vehicle position information, the duration information of the vehicle to be unloaded, and the cargo weight information to generate a vehicle scheduling optimization suggestion.

[0017] Optionally, in the sixth implementation manner of the second aspect of the present invention, it further includes: a sending module, configured to send the optimization suggestion strategy to the management terminal; a receiving and analyzing module, configured to receive the strategy execution situation information sent by the management terminal and analyze the execution effect of the strategy execution situation information; an encryption and uploading module, configured to encrypt the execution effect to obtain an execution effect encrypted information, and upload the execution effect encrypted information to the blockchain.

[0018] The third aspect of the present invention provides a logistics vehicle unloading efficiency optimization device, where the logistics vehicle unloading efficiency optimization device includes: a memory and at least one processor, and instructions are stored in the memory; at least one of the processors invokes the instructions in the memory so that the logistics vehicle unloading efficiency optimization device executes each step of the above-mentioned logistics vehicle unloading efficiency optimization method.

[0019] The fourth aspect of the present invention provides a computer-readable storage medium, where instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, each step of the above-mentioned logistics vehicle unloading efficiency optimization method is implemented.

[0020] In the technical solution of the present invention, the unloading-related data of the vehicle is obtained in real time from the handover note service system, realizing the real-time monitoring of the unloading efficiency of the logistics vehicle. The unloading-related data is preprocessed, and the preset time series processing model is used to calibrate the time of the unloading-related preprocessed data, improving the timeliness and accuracy of the data. Based on the preset index of unloaded vehicles overdue, the sliding window algorithm is used to calculate the unloading-related preprocessed data to obtain the real-time information of unloaded vehicles overdue. The real-time information of unloaded vehicles overdue is mapped into the visual mapping template to obtain the visual information of unloaded vehicles overdue. The front-end framework is used to construct a multi-dimensional query page for the unloading situation, and the visual information of unloaded vehicles overdue is displayed in real time on the multi-dimensional query page for the unloading situation through the data binding technology, providing a multi-dimensional data query function to help the management personnel accurately locate the problem areas and vehicles. The unloading efficiency analysis is carried out on the real-time information of unloaded vehicles overdue to obtain the analysis result of key factors. According to the analysis result of key factors, an optimization suggestion strategy is generated, and the unloading process can be dynamically adjusted according to the optimization suggestion strategy, improving the unloading efficiency, reducing the operation cost, and enhancing the competitiveness of the logistics service. Description of the Drawings

[0021] Figure 1 It is the first flow chart of the method for optimizing the unloading efficiency of the logistics vehicle provided by the embodiment of the present invention;

[0022] Figure 2 It is the second flow chart of the method for optimizing the unloading efficiency of the logistics vehicle provided by the embodiment of the present invention;

[0023] Figure 3 It is the third flow chart of the method for optimizing the unloading efficiency of the logistics vehicle provided by the embodiment of the present invention;

[0024] Figure 4 It is the fourth flow chart of the method for optimizing the unloading efficiency of the logistics vehicle provided by the embodiment of the present invention;

[0025] Figure 5 It is a schematic structural diagram of an apparatus for optimizing the unloading efficiency of the logistics vehicle provided by the embodiment of the present invention;

[0026] Figure 6 It is another schematic structural diagram of an apparatus for optimizing the unloading efficiency of the logistics vehicle provided by the embodiment of the present invention;

[0027] Figure 7 It is a schematic structural diagram of a device for optimizing the unloading efficiency of the logistics vehicle provided by the embodiment of the present invention. Detailed Embodiment

[0028] The present invention provides a method, device, equipment and storage medium for optimizing the unloading efficiency of logistics vehicles, realizing real-time monitoring of the unloading efficiency of logistics vehicles, improving the timeliness and accuracy of data, providing multi-dimensional data analysis and query functions, helping management personnel accurately locate problem areas and vehicles, being able to dynamically adjust the unloading process according to the optimization suggestion strategy, improving the unloading efficiency, reducing the operating cost, and enhancing the competitiveness of logistics services.

[0029] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "comprising" or "having" and any variation thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or equipment comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0030] For the convenience of understanding, the specific process of the embodiment of the present invention will be described below. Please refer to Figure 1 , an embodiment of the method for optimizing the unloading efficiency of logistics vehicles in the embodiment of the present invention includes:

[0031] 101. Obtain the unloading-related data of the vehicle in real time from the handover note service system, preprocess the unloading-related data, and obtain the preprocessed unloading-related data;

[0032] In this embodiment, the original unloading-related data is obtained from the handover note service system, including information such as vehicles, goods, unloading time, location, operators, etc. After the data is obtained, the first step is to clean the data, aiming to delete invalid, duplicate, and format-error data, and correct possible errors. Fill in the missing values for the cleaned unloading-related data, and convert the format of the filled unloading-related data to obtain the preprocessed unloading-related data.

[0033] 102. Use a preset time series processing model to calibrate the time of the preprocessed unloading-related data, and calculate the preprocessed unloading-related data using a sliding window algorithm based on a preset timeout-unloaded vehicle index to obtain real-time timeout-unloaded vehicle information;

[0034] In this embodiment, the unloading data is corrected through a time series model to ensure that the data is consistent with the actual time logic, avoiding time errors caused by data transmission delays or other factors. Based on the preset index of unloaded vehicles overdue, by setting a window with a fixed length and "sliding" it in the data stream, calculations and judgments are performed within each time window. For the unloading data, it is possible to determine whether it is overdue based on the unloading time within the "window" time period. First, set the size of a time window. For example, assume the window size is 30 minutes, which means the unloading task needs to be completed within every 30 minutes. If the unloading is not completed after more than 30 minutes, it will be marked as overdue. The window will gradually slide in the preprocessed unloading data, sliding one time unit (such as 1 minute or 5 minutes) each time, and the system will check in real time whether the unloading task within this window is overdue. Within each time window, by comparing with the preset overdue index, it is determined whether there is an overdue situation for unloading. For example, if the difference between the unloading start time and the actual completion time exceeds the predetermined maximum duration, or the unloading time exceeds the set time window, the system will mark this task as "unloaded vehicles overdue". As the data is updated in real time, the window will continue to slide, and new unloading task data will be incorporated into the window to recalculate whether it is overdue.

[0035] 103. Load the visualization mapping template and map the real-time information of unloaded vehicles overdue into the visualization mapping template to obtain the visualized information of unloaded vehicles overdue;

[0036] In this embodiment, the visualization mapping template is loaded. The mapping template is a pre-designed visualization framework or model. The real-time information of unloaded vehicles overdue is mapped into the visualization mapping template to obtain the visualized information of unloaded vehicles overdue.

[0037] 104. Use the front-end framework to construct a multi-dimensional query page for the unloading situation, and display the visualized information of unloaded vehicles overdue on the multi-dimensional query page for the unloading situation in real time through data binding technology;

[0038] In this embodiment, use the front-end framework to construct a multi-dimensional query page for the unloading situation. Users can select different filtering conditions, such as time range, location, unloading task ID, task type, overdue status, etc. The visualized information of unloaded vehicles overdue is displayed on the multi-dimensional query page for the unloading situation in real time through data binding technology. The page structure includes a query condition area, a result display area, and a data visualization area. The data visualization area displays visualization information (such as heat maps, bar charts, maps, etc.) so that users can intuitively understand the situation of unloaded vehicles overdue. The result display area shows the queried information of unloaded vehicles overdue, presented in the form of tables, charts, etc., and at the same time allows users to perform operations such as sorting, paging, and exporting on the query results.

[0039] 105. Analyze the unloading efficiency of real-time overtime unloaded vehicle information to obtain the key factor analysis results, and generate an optimization suggestion strategy according to the key factor analysis results;

[0040] In this embodiment, analyze the unloading efficiency of real-time overtime unloaded vehicle information to obtain the key factor analysis results affecting the unloading efficiency, and generate an optimization suggestion strategy according to the key factor analysis results.

[0041] In the embodiment of the present invention, the unloading-related data of the vehicle is obtained in real time from the handover order service system, realizing the real-time monitoring of the unloading efficiency of the logistics vehicle. The unloading-related data is preprocessed, and the preset time series processing model is used to calibrate the time of the unloading-related preprocessed data, improving the timeliness and accuracy of the data. Based on the preset overtime unloaded vehicle index, the sliding window algorithm is used to calculate the unloading-related preprocessed data to obtain the real-time overtime unloaded vehicle information. The real-time overtime unloaded vehicle information is mapped into the visual mapping template to obtain the overtime unloaded vehicle visual information. The front-end framework is used to construct a multi-dimensional query page for the unloading situation, and the overtime unloaded vehicle visual information is displayed in real time on the multi-dimensional query page for the unloading situation through the data binding technology, providing a multi-dimensional data query function to help the management personnel accurately locate the problem area and vehicle. Analyze the unloading efficiency of the real-time overtime unloaded vehicle information to obtain the key factor analysis results, and generate an optimization suggestion strategy according to the key factor analysis results. The unloading process can be dynamically adjusted according to the optimization suggestion strategy, improving the unloading efficiency, reducing the operation cost, and enhancing the competitiveness of the logistics service.

[0042] Please refer to Figure 2 , the second embodiment of the logistics vehicle unloading efficiency optimization method in the embodiment of the present invention includes:

[0043] 201. Obtain the unloading-related data of the vehicle in real time from the handover order service system;

[0044] In this embodiment, the unloading-related data of the vehicle is obtained in real time from the handover order service system through the API interface or the message queue.

[0045] 202. Perform data cleaning on the unloading-related data to obtain the unloading-related cleaned data;

[0046] In this embodiment, the duplicate data of the unloading-related data is removed, and the outliers of the unloading-related data are processed to obtain the unloading-related cleaned data.

[0047] 203. Perform missing value filling on the unloading-related cleaned data to obtain the unloading-related filled data;

[0048] In this embodiment, missing values in the unloading-related cleaning data are filled to obtain the unloading-related filled data. If the unloading start time is missing, the end time of the previous task or the start time of the next task can be used for inference and filling. If the task has a standard duration, the end time can be inferred based on the type of the task or the average duration of historical records.

[0049] 204. Based on a preset target standard format, the unloading-related filled data is subjected to format conversion to obtain the unloading-related preprocessed data;

[0050] In this embodiment, the preset target standard format is clarified, the fields of the unloading-related filled data are aligned with the fields of the target standard format, and the unloading-related filled data is converted into the target standard format to obtain the unloading-related preprocessed data.

[0051] 205. Use a preset time series processing model to perform time calibration on the unloading-related preprocessed data;

[0052] In this embodiment, a preset time series processing model is used to perform time calibration on the unloading-related preprocessed data. If the data comes from different regions or time zones, there may be time zone deviations, and it is necessary to unify the time zones of all data to ensure the standardization of time and align data with different time frequencies (for example, unloading data recorded hourly and data recorded daily) to a unified frequency.

[0053] 206. Based on a preset index of unloaded vehicles overdue, the sliding window algorithm is used to calculate the unloading-related preprocessed data to obtain real-time information on unloaded vehicles overdue;

[0054] In this embodiment, based on a preset index of unloaded vehicles overdue, the sliding window algorithm is used to calculate the unloading-related preprocessed data to obtain real-time information on unloaded vehicles overdue. The core idea of the sliding window algorithm is to gradually traverse the time series data through a fixed-size window and perform real-time calculations on the data within the window.

[0055] 207. Save the real-time information on unloaded vehicles overdue to the database;

[0056] In this embodiment, a suitable database table structure is designed. The design of this table can record the overdue task information within each time window, including the start time, end time, and number of overdue tasks of the time window, and save the real-time information on unloaded vehicles overdue to the database.

[0057] In the embodiments of the present invention, through steps such as data cleaning, missing value filling, and format conversion, the data quality from the source data to the final result is ensured, the impact of incorrect and incomplete data on subsequent analysis is avoided, and the entire process automatically processes each link from data acquisition to storage, greatly reducing manual intervention, improving the processing efficiency and response speed. By obtaining data in real time and combining with the sliding window algorithm to calculate the information of unloaded vehicles beyond the time limit, instant feedback can be provided to help process and adjust the unloading progress in a timely manner. Through time series processing and the calculation of the index of unloaded vehicles beyond the time limit, unloading anomalies can be monitored and identified in real time, providing accurate early warning information for management decisions and enhancing the overall operation efficiency. Saving the processed information to the database ensures the traceability of data and the integrity of historical records, facilitating subsequent analysis, query, and decision support.

[0058] Please refer to Figure 3 , the third embodiment of the method for optimizing the unloading efficiency of logistics vehicles in the embodiments of the present invention includes:

[0059] 301. Load the corresponding visual mapping template from the template library according to the information type of the real-time information of unloaded vehicles beyond the time limit;

[0060] In this embodiment, each type of timeout task information can be matched with a specific visual template, and the corresponding visual mapping template is loaded from the template library according to the information type of the real-time information of unloaded vehicles beyond the time limit.

[0061] 302. Identify the visual elements of the visual mapping template;

[0062] In this embodiment, each visual template contains a series of visual elements, such as graphics, colors, sizes, shapes, labels, etc., and the visual elements of the visual mapping template are identified.

[0063] 303. Map the real-time information of unloaded vehicles beyond the time limit into the visual elements of the visual mapping template to obtain the visual information of unloaded vehicles beyond the time limit;

[0064] In this embodiment, the real-time information of unloaded vehicles beyond the time limit is mapped into each visual element of the visual mapping template to obtain the visual information of unloaded vehicles beyond the time limit.

[0065] 304. Use the front-end framework to build a multi-dimensional query page for the unloading situation;

[0066] In this embodiment, select the front-end framework, and on the basis of the front-end framework, the basic architecture of the multi-dimensional query page for the unloading situation can be built.

[0067] 305. Real-time display the visual information of unloaded vehicles beyond the time limit on the multi-dimensional query page for the unloading situation through data binding technology, and establish a real-time communication connection with the multi-dimensional query page for the unloading situation using WebSocket technology;

[0068] In this embodiment, the visualization information of unloaded vehicles overdue is displayed in real time on the multi-dimensional query page of unloaded vehicle conditions through data binding technology, and a real-time communication connection is established with the multi-dimensional query page of unloaded vehicle conditions by using WebSocket technology, and the visualization information of unloaded vehicles overdue is obtained through the API.

[0069] 306. Listen for data change information, and when the data change information is monitored, push the latest data to the multi-dimensional query page of unloaded vehicle conditions;

[0070] In this embodiment, a listener is created to listen for data change information by using the listener. When the listener monitors the data change information, the latest data is obtained and the latest data is pushed to the multi-dimensional query page of unloaded vehicle conditions.

[0071] 307. Set data caching on the multi-dimensional query page of unloaded vehicle conditions, and when the latest data is received, use the latest data to update the cached data;

[0072] In this embodiment, by using a cache update strategy, data caching is set on the multi-dimensional query page of unloaded vehicle conditions at the front end. When new data is received, it is first compared with the cached data, and only the changed data part is updated, reducing the data transmission volume and the page refresh burden, and improving the user experience.

[0073] In the embodiment of the present invention, through real-time loading and updating of the information of unloaded vehicles overdue, the instant reflection of data is realized, the monitoring efficiency of unloaded vehicle conditions is improved. By loading the visualization mapping template and mapping the real-time data, the key data can be intuitively displayed, enhancing the user's operation experience and data understanding. Through WebSocket and data binding technologies, the page can be automatically updated when the data changes, ensuring that the user always sees the latest information and avoiding manual refreshing. The front-end framework supports the multi-dimensional query page, which can meet the needs of different users, provide multi-angle data analysis views, and improve the flexibility of data query. By setting data caching and intelligent updating, the response speed and performance of the system are improved, and the consumption of repeated data loading is reduced.

[0074] Please refer to Figure 4 , the fourth embodiment of the method for optimizing the unloading efficiency of logistics vehicles in the embodiment of the present invention includes:

[0075] 401. Obtain the historical information of unloaded vehicles overdue, and mine the correlation relationship between the unloading efficiency and different factors in the historical information of unloaded vehicles overdue;

[0076] In this embodiment, historical information on unloaded vehicles with overtime is obtained. The historical information on unloaded vehicles with overtime includes task ID, vehicle ID, estimated unloading time of the task, actual unloading time, status, cargo type, unloading location, etc. The correlation between unloading efficiency and different factors in the historical information on unloaded vehicles with overtime is mined. The unloading efficiency is evaluated by calculating the actual unloading duration of each task. Preliminary exploratory data analysis (EDA) is carried out, and through visualization means, the relationship between unloading efficiency and various factors is identified.

[0077] 402. Analyze the unloading efficiency of real-time information on unloaded vehicles with overtime according to the correlation, and obtain the key factor analysis result. The key factor analysis result is one of the problems of the operation efficiency of the sorting center and the vehicle scheduling problem.

[0078] In this embodiment, statistical test methods are used to verify whether the relationship between different factors and unloading efficiency is significant. Regression analysis methods are used to establish a mathematical model between unloading efficiency and multiple factors. The unloading efficiency of new tasks is predicted through the trained model analysis, and the key factor analysis result is obtained. The key factor analysis result is one of the problems of the operation efficiency of the sorting center and the vehicle scheduling problem.

[0079] 403. When the key factor analysis result is the problem of the operation efficiency of the sorting center, the process mining algorithm is used to analyze the operation process of the sorting center, obtain the operation process analysis result, and identify the bottleneck links in the operation process analysis result.

[0080] In this embodiment, when the key factor analysis result is the problem of the operation efficiency of the sorting center, the process mining algorithm is used to analyze the operation process of the sorting center, obtain the operation process analysis result, and identify the bottleneck links in the operation process analysis result. Specifically, by analyzing the execution time of each operation link, the links with too long time consumption are identified, and by analyzing the frequency of each link being triggered, the links that repeatedly appear in multiple tasks and have an impact on the overall process are found.

[0081] 404. Generate operation process optimization suggestions according to the bottleneck links. The operation process optimization suggestions include adjusting the operation order and adding operation equipment.

[0082] In this embodiment, operation process optimization suggestions are generated according to the bottleneck links. The operation process optimization suggestions include adjusting the operation order and adding operation equipment. When there may be a shortage of equipment quantity in some links (such as the use of equipment such as forklifts and transport vehicles), resulting in equipment queuing and delaying the operation progress, add operation equipment. When the process is stagnant due to over-reliance on a certain link or the delay is caused by too long waiting or queuing time between links, adjust the operation order.

[0083] 405. When the analysis result of the key factors is a vehicle scheduling problem, the genetic algorithm is used and combined with the real-time vehicle position information, the waiting time for unloading the vehicle, and the cargo weight information to generate optimization suggestions for vehicle scheduling;

[0084] In this embodiment, when the analysis result of the key factors is a vehicle scheduling problem, the genetic algorithm is used and combined with the real-time vehicle position information, the waiting time for unloading the vehicle, and the cargo weight information to generate optimization suggestions for vehicle scheduling. Through real-time GPS positioning, the scheduling order of the vehicles is optimized, the empty driving time is reduced. According to the cargo weight, type, and unloading requirements of each vehicle, the unloading order is adjusted in real time to ensure that the heavily loaded vehicles can be unloaded in time and the waiting time is reduced. Considering the weight of the cargo, the unloading order and time arrangement are adjusted to avoid the problem of slow unloading of overweight vehicles.

[0085] 406. Send the optimization suggestion strategy to the management terminal;

[0086] In this embodiment, standard communication protocols such as HTTP, MQTT, WebSocket, etc. are adopted to ensure stable and efficient data transmission between systems. There is a clear interface design between the management terminal and the system, and the optimization suggestion strategy is sent to the management terminal through the API interface.

[0087] 407. Receive the information on the implementation status of the strategy sent by the management terminal and analyze the implementation effect of the information on the implementation status of the strategy;

[0088] In this embodiment, the management terminal will feedback the information on the implementation status of the strategy back to the optimization system, aiming to let the system understand the implementation effect of each vehicle task. The system receives the information on the implementation status of the strategy sent by the management terminal and analyzes the implementation effect of the information on the implementation status of the strategy.

[0089] 408. Encrypt the implementation effect to obtain the encrypted information on the implementation effect, and upload the encrypted information on the implementation effect to the blockchain;

[0090] In this embodiment, a suitable encryption algorithm is selected to encrypt the implementation effect to obtain the encrypted information on the implementation effect, and the encrypted information on the implementation effect is uploaded to the blockchain. When uploading the encrypted information on the implementation effect to the blockchain, the system will generate a transaction, including the encrypted implementation effect data. These transactions will be packaged into blocks and verified and broadcast through the blockchain network.

[0091] In the embodiments of the present invention, through mining historical data and key factor analysis, the accurate identification of the problem of unloaded vehicles exceeding the time limit is realized, which improves the accuracy and pertinence of decision-making. For the operation efficiency of the distribution center and vehicle scheduling problems, suggestions for process optimization or scheduling optimization are automatically generated to reduce manual intervention and improve efficiency. Combining the process mining algorithm and genetic algorithm, personalized solutions are provided for different problems, which helps to improve the accuracy of problem-solving and optimization effect. The information on the execution situation of the strategy is received through the management terminal and the effect is analyzed in real time to ensure that the feedback of the strategy execution is timely and effective and can be continuously optimized. By encrypting and uploading to the blockchain technology, the security and transparency of the execution effect information are ensured, and the credibility and reliability of the system are enhanced.

[0092] The optimization method for the unloading efficiency of logistics vehicles in the embodiments of the present invention has been described above. Next, the optimization device for the unloading efficiency of logistics vehicles in the embodiments of the present invention will be described. Please refer to Figure 5 , an embodiment of the optimization device for the unloading efficiency of logistics vehicles in the embodiments of the present invention includes:

[0093] An acquisition processing module 501, configured to obtain the data related to the unloading of the vehicle in real time from the handover order service system, and preprocess the data related to the unloading to obtain preprocessed data related to the unloading;

[0094] A calibration calculation module 502, configured to perform time calibration on the preprocessed data related to the unloading by using a preset time series processing model, and calculate the preprocessed data related to the unloading by using a sliding window algorithm based on a preset index of unloaded vehicles exceeding the time limit to obtain real-time information on unloaded vehicles exceeding the time limit;

[0095] A loading mapping module 503, configured to load a visualization mapping template, and map the real-time information on unloaded vehicles exceeding the time limit into the visualization mapping template to obtain visualization information on unloaded vehicles exceeding the time limit;

[0096] A construction display module 504, configured to construct a multi-dimensional query page for the unloading situation by using a front-end framework, and display the visualization information on unloaded vehicles exceeding the time limit on the multi-dimensional query page for the unloading situation in real time through data binding technology;

[0097] An analysis generation module 505, configured to perform unloading efficiency analysis on the real-time information on unloaded vehicles exceeding the time limit to obtain the analysis result of key factors, and generate an optimization suggestion strategy according to the analysis result of key factors.

[0098] In this embodiment, relevant data on the unloading of vehicles is obtained in real time from the handover note service system, enabling real-time monitoring of the unloading efficiency of logistics vehicles. The relevant data on unloading is preprocessed, and a preset time series processing model is used to calibrate the time of the preprocessed data related to unloading, improving the timeliness and accuracy of the data. Based on the preset index of unloaded vehicles overdue, a sliding window algorithm is used to calculate the preprocessed data related to unloading, obtaining real-time information on unloaded vehicles overdue. The real-time information on unloaded vehicles overdue is mapped to a visual mapping template to obtain visual information on unloaded vehicles overdue. A multi-dimensional query page for the unloading situation is constructed using a front-end framework, and the visual information on unloaded vehicles overdue is displayed in real time on the multi-dimensional query page for the unloading situation through data binding technology, providing a multi-dimensional data query function to help management personnel accurately locate problem areas and vehicles. An analysis of the unloading efficiency is performed on the real-time information on unloaded vehicles overdue to obtain the analysis results of key factors, and an optimization suggestion strategy is generated based on the analysis results of key factors. The unloading process can be dynamically adjusted according to the optimization suggestion strategy, improving the unloading efficiency, reducing the operating cost, and enhancing the competitiveness of logistics services.

[0099] Please refer to Figure 6 , another embodiment of the device for optimizing the unloading efficiency of logistics vehicles in the embodiment of the present invention includes:

[0100] An acquisition and processing module 501, configured to obtain in real time relevant data on the unloading of vehicles from the handover note service system, preprocess the relevant data on unloading, and obtain preprocessed data related to unloading;

[0101] A calibration and calculation module 502, configured to use a preset time series processing model to calibrate the time of the preprocessed data related to unloading, and based on the preset index of unloaded vehicles overdue, use a sliding window algorithm to calculate the preprocessed data related to unloading to obtain real-time information on unloaded vehicles overdue;

[0102] A loading and mapping module 503, configured to load a visual mapping template, map the real-time information on unloaded vehicles overdue to the visual mapping template, and obtain visual information on unloaded vehicles overdue;

[0103] A construction and display module 504, configured to construct a multi-dimensional query page for the unloading situation using a front-end framework, and display the visual information on unloaded vehicles overdue in real time on the multi-dimensional query page for the unloading situation through data binding technology;

[0104] An analysis and generation module 505, configured to perform an analysis of the unloading efficiency on the real-time information on unloaded vehicles overdue to obtain the analysis results of key factors, and generate an optimization suggestion strategy based on the analysis results of key factors;

[0105] In this embodiment, the acquisition processing module 501 includes: an acquisition unit 5011, configured to acquire the data related to the unloading of the vehicle in real time from the handover order service system; a cleaning unit 5012, configured to perform data cleaning on the data related to the unloading of the vehicle to obtain the cleaned data related to the unloading of the vehicle; a filling unit 5013, configured to fill in the missing values in the cleaned data related to the unloading of the vehicle to obtain the filled data related to the unloading of the vehicle; and a conversion unit 5014, configured to perform format conversion on the filled data related to the unloading of the vehicle based on a preset target standard format to obtain the preprocessed data related to the unloading of the vehicle.

[0106] In this embodiment, the calibration calculation module 502 includes: a calibration unit 5021, configured to perform time calibration on the preprocessed data related to the unloading of the vehicle by using a preset time series processing model; a calculation unit 5022, configured to calculate the real-time information on vehicles that have not been unloaded on time based on a preset index of vehicles that have not been unloaded on time by using a sliding window algorithm for the preprocessed data related to the unloading of the vehicle; and a saving unit 5023, configured to save the real-time information on vehicles that have not been unloaded on time into a database.

[0107] In this embodiment, the loading and mapping module 503 includes: a loading unit 5031, configured to load a corresponding visual mapping template from a template library according to the information type of the real-time information on vehicles that have not been unloaded on time; an identification unit 5032, configured to identify the visual elements of the visual mapping template; and a mapping unit 5033, configured to map the real-time information on vehicles that have not been unloaded on time to the visual elements of the visual mapping template to obtain the visual information on vehicles that have not been unloaded on time.

[0108] In this embodiment, the construction and display module 504 includes: a construction unit 5041, configured to construct a multi-dimensional query page for the unloading situation by using a front-end framework; a display establishment unit 5042, configured to display the visual information on vehicles that have not been unloaded on time in real time on the multi-dimensional query page for the unloading situation by using a data binding technology, and establish a real-time communication connection with the multi-dimensional query page for the unloading situation by using a WebSocket technology; a monitoring and pushing unit 5043, configured to monitor the data change information, and when the data change information is monitored, push the latest data to the multi-dimensional query page for the unloading situation; and a setting and updating unit 5044, configured to set a data cache on the multi-dimensional query page for the unloading situation, and when the latest data is received, update the cached data by using the latest data.

[0109] In this embodiment, the analysis and generation module 505 includes: an acquisition and mining unit 5051, configured to acquire historical information on unloaded vehicles with overdue time, and mine the correlation between the unloading efficiency and different factors in the historical information on unloaded vehicles with overdue time; an analysis unit 5052, configured to perform unloading efficiency analysis on the real-time information on unloaded vehicles with overdue time according to the correlation, and obtain a key factor analysis result, where the key factor analysis result is one of the operation efficiency problem of the distribution center and the vehicle scheduling problem; an analysis and identification unit 5053, configured to, when the key factor analysis result is the operation efficiency problem of the distribution center, analyze the operation process of the distribution center using a process mining algorithm, obtain an operation process analysis result, and identify the bottleneck link in the operation process analysis result; a first generation unit 5054, configured to generate an operation process optimization suggestion according to the bottleneck link, where the operation process optimization suggestion includes adjusting the operation sequence and adding operation equipment; a second generation unit 5055, configured to, when the key factor analysis result is the vehicle scheduling problem, use a genetic algorithm and combine the real-time vehicle position information, the information on the duration of waiting for unloading, and the cargo weight information to generate a vehicle scheduling optimization suggestion.

[0110] In this embodiment, it further includes: a sending module 506, configured to send the optimization suggestion strategy to the management terminal; a receiving and analysis module 507, configured to receive the strategy execution situation information sent by the management terminal and analyze the execution effect of the strategy execution situation information; an encryption and upload module 508, configured to encrypt the execution effect to obtain encrypted execution effect information and upload the encrypted execution effect information to the blockchain.

[0111] Above Figure 5 And Figure 6 The optimization device for the unloading efficiency of logistics vehicles in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Next, the optimization equipment for the unloading efficiency of logistics vehicles in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0112] Figure 7FIG. 0 is a schematic structural diagram of an equipment for optimizing the unloading efficiency of a logistics vehicle provided by an embodiment of the present invention. The equipment 600 for optimizing the unloading efficiency of a logistics vehicle may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 610 (for example, one or more processors) and a memory 620, and one or more storage media 630 (for example, one or more mass storage devices) storing application programs 633 or data 632. Among them, the memory 620 and the storage media 630 may be transient storage or persistent storage. The programs stored in the storage media 630 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the equipment 600 for optimizing the unloading efficiency of a logistics vehicle. Further, the processor 610 may be configured to communicate with the storage media 630 and execute a series of instruction operations in the storage media 630 on the equipment 600 for optimizing the unloading efficiency of a logistics vehicle, so as to implement the steps of the method for optimizing the unloading efficiency of a logistics vehicle provided in the above method embodiments.

[0113] The equipment 600 for optimizing the unloading efficiency of a logistics vehicle may further include one or more power supplies 640, one or more wired or wireless network interfaces 650, one or more input / output interfaces 660, and / or one or more operating systems 631, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art can understand that Figure 7 the shown structural diagram of the equipment for optimizing the unloading efficiency of a logistics vehicle does not constitute a limitation on the equipment for optimizing the unloading efficiency of a logistics vehicle, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0114] The present invention further provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is enabled to execute the steps of the method for optimizing the unloading efficiency of a logistics vehicle.

[0115] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, or units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0116] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0117] Finally, it should be noted that the above are only preferred examples of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. An optimization method for the unloading efficiency of a logistics vehicle, characterized in that, Including: Real-time obtain the unloading-related data of the vehicle from the handover note service system, preprocess the unloading-related data to obtain preprocessed unloading-related data; Use a preset time series processing model to perform time calibration on the preprocessed unloading-related data, and based on a preset timeout-unloaded vehicle indicator, use a sliding window algorithm to calculate the preprocessed unloading-related data to obtain real-time timeout-unloaded vehicle information; Load a visualization mapping template, map the real-time timeout-unloaded vehicle information into the visualization mapping template to obtain timeout-unloaded vehicle visualization information; Use a front-end framework to construct a multi-dimensional query page for the unloading situation, and use data binding technology to display the timeout-unloaded vehicle visualization information in real time on the multi-dimensional query page for the unloading situation; Perform unloading efficiency analysis on the real-time timeout-unloaded vehicle information to obtain a key factor analysis result, and generate an optimization suggestion strategy according to the key factor analysis result.

2. The method for optimizing the unloading efficiency of a logistics vehicle according to claim 1, characterized in that The real-time obtaining the unloading-related data of the vehicle from the handover note service system, preprocessing the unloading-related data to obtain preprocessed unloading-related data includes: Real-time obtain the unloading-related data of the vehicle from the handover note service system; Perform data cleaning on the unloading-related data to obtain cleaned unloading-related data; Fill in missing values for the cleaned unloading-related data to obtain filled unloading-related data; Based on a preset target standard format, perform format conversion on the filled unloading-related data to obtain preprocessed unloading-related data.

3. The method for optimizing the unloading efficiency of a logistics vehicle according to claim 1, characterized in that The using a preset time series processing model to perform time calibration on the preprocessed unloading-related data, and based on a preset timeout-unloaded vehicle indicator, using a sliding window algorithm to calculate the preprocessed unloading-related data to obtain real-time timeout-unloaded vehicle information includes: Use a preset time series processing model to perform time calibration on the preprocessed unloading-related data; Based on a preset timeout-unloaded vehicle indicator, use a sliding window algorithm to calculate the preprocessed unloading-related data to obtain real-time timeout-unloaded vehicle information; Save the real-time timeout-unloaded vehicle information to a database.

4. The method for optimizing the unloading efficiency of a logistics vehicle according to claim 1, wherein, The loading a visualization mapping template, mapping the real-time timeout-unloaded vehicle information into the visualization mapping template to obtain timeout-unloaded vehicle visualization information includes: Load a corresponding visualization mapping template from the template library according to the information type of the real-time timeout-unloaded vehicle information; Identify the visual elements of the visualization mapping template; Map the real-time timeout-unloaded vehicle information into the visual elements of the visualization mapping template to obtain timeout-unloaded vehicle visualization information.

5. The method for optimizing the unloading efficiency of a logistics vehicle according to claim 1, wherein The using a front-end framework to construct a multi-dimensional query page for the unloading situation, and using data binding technology to display the timeout-unloaded vehicle visualization information in real time on the multi-dimensional query page for the unloading situation includes: Use a front-end framework to construct a multi-dimensional query page for the unloading situation; Use data binding technology to display the timeout-unloaded vehicle visualization information in real time on the multi-dimensional query page for the unloading situation, and establish a real-time communication connection with the multi-dimensional query page for the unloading situation using WebSocket technology; Monitor the data change information, and when the data change information is monitored, push the latest data to the multi-dimensional query page of the truck unloading situation; Set data caching on the multi-dimensional query page of the truck unloading situation. When the latest data is received, use the latest data to update the cached data.

6. The method for optimizing the unloading efficiency of a logistics vehicle according to claim 1, wherein, Perform truck unloading efficiency analysis on the real-time information of trucks that have not been unloaded beyond the time limit, and obtain the key factor analysis result. Generate an optimization suggestion strategy according to the key factor analysis result, including: Obtain historical information of trucks that have not been unloaded beyond the time limit, and mine the correlation between the truck unloading efficiency and different factors in the historical information of trucks that have not been unloaded beyond the time limit; Perform truck unloading efficiency analysis on the real-time information of trucks that have not been unloaded beyond the time limit according to the correlation, and obtain the key factor analysis result. The key factor analysis result is one of the operation efficiency problem of the distribution center and the vehicle scheduling problem; When the key factor analysis result is the operation efficiency problem of the distribution center, use the process mining algorithm to analyze the operation process of the distribution center, obtain the operation process analysis result, and identify the bottleneck link in the operation process analysis result; Generate operation process optimization suggestions according to the bottleneck link. The operation process optimization suggestions include adjusting the operation order and adding operation equipment; When the key factor analysis result is the vehicle scheduling problem, use the genetic algorithm and combine the real-time vehicle location information, the duration information of trucks waiting to be unloaded, and the cargo weight information to generate vehicle scheduling optimization suggestions.

7. The method for optimizing the unloading efficiency of a logistics vehicle according to claim 1, characterized in that After performing truck unloading efficiency analysis on the real-time information of trucks that have not been unloaded beyond the time limit, obtaining the key factor analysis result, and generating an optimization suggestion strategy according to the key factor analysis result, it further includes: Send the optimization suggestion strategy to the management terminal; Receive the strategy execution situation information sent by the management terminal, and analyze the execution effect of the strategy execution situation information; Encrypt the execution effect to obtain the encrypted execution effect information, and upload the encrypted execution effect information to the blockchain.

8. An unloading efficiency optimization device for a logistics vehicle, characterized in that, It includes: An acquisition and processing module, used to obtain the truck unloading related data of the vehicle from the handover order service system in real time, and preprocess the truck unloading related data to obtain preprocessed truck unloading related data; A calibration calculation module, used to perform time calibration on the preprocessed truck unloading related data by using a preset time series processing model, and calculate the preprocessed truck unloading related data by using a sliding window algorithm based on a preset index of trucks that have not been unloaded beyond the time limit to obtain real-time information of trucks that have not been unloaded beyond the time limit; A loading and mapping module, used to load a visualization mapping template, and map the real-time information of trucks that have not been unloaded beyond the time limit into the visualization mapping template to obtain visualization information of trucks that have not been unloaded beyond the time limit; A construction and display module, used to construct a multi-dimensional query page of the truck unloading situation by using a front-end framework, and display the visualization information of trucks that have not been unloaded beyond the time limit on the multi-dimensional query page of the truck unloading situation in real time through data binding technology; An analysis and generation module, used to perform truck unloading efficiency analysis on the real-time information of trucks that have not been unloaded beyond the time limit, obtain the key factor analysis result, and generate an optimization suggestion strategy according to the key factor analysis result.

9. An equipment for optimizing the unloading efficiency of a logistics vehicle, characterized in that, The equipment for optimizing the unloading efficiency of logistics vehicles includes: a memory and at least one processor, wherein instructions are stored in the memory; At least one of the processors calls the instructions in the memory so that the equipment for optimizing the unloading efficiency of logistics vehicles executes each step of the method for optimizing the unloading efficiency of logistics vehicles according to any one of claims 1-7.

10. A computer-readable storage medium, on which instructions are stored, characterized in that, When the instructions are executed by the processor, each step of the method for optimizing the unloading efficiency of logistics vehicles according to any one of claims 1-7 is implemented.

Images