CN116452095B

CN116452095B - Intelligent vehicle supervision and scheduling method, system and medium for digital factory

Info

Publication number: CN116452095B
Application number: CN202310716947.1A
Authority: CN
Inventors: 王毅; 李大利; 袁石安
Original assignee: Shenzhen Pfiter Information Technology Co ltd
Current assignee: Shenzhen Pfiter Information Technology Co ltd
Priority date: 2023-06-16
Filing date: 2023-06-16
Publication date: 2023-09-19
Anticipated expiration: 2043-06-16
Also published as: CN116452095A

Abstract

The application provides a vehicle intelligent supervision and scheduling method, a system and a medium for a digital factory. The method comprises the following steps: collecting cargo demand information, analyzing and generating a cargo in and out element list, analyzing task data to obtain matched vehicles, generating a transportation vehicle dispatching model organization tree, carrying out statistics on density data of cargo in and out tasks of the vehicles to generate a cargo in and out task feature image of the transportation vehicles, extracting cargo in and out task content data to process and obtain response data, generating a cargo in and out task instruction list in a combined mode, and generating a cargo in and out task dispatching list for dispatching cargo in and out tasks of the vehicles; and the batch in-and-out tasks are subjected to information acquisition and data processing based on the big data to obtain matched dispatching of vehicles, so that the matching dispatching of the vehicles according to the in-and-out demand information and the task data is realized, a list task instruction bar is generated, the vehicles are optimally dispatched according to the in-and-out demand and the task condition, and the accurate control of the dispatching of the vehicles in factories is improved.

Description

Intelligent vehicle supervision and scheduling method, system and medium for digital factory

Technical Field

The application relates to the technical field of big data and intelligent factory management, in particular to a vehicle intelligent supervision and scheduling method, a system and a medium of a digital factory.

Background

The digital factory is a current advanced manufacturing factory constructed according to production datamation, the production operation of the factory cannot be separated from the supply of upstream material raw materials and the supply of downstream products to customers, and the factory is difficult to make reasonable and effective management measures due to the differences and diversity of the quantity, the types, the transportation requirements, the goods in and out destinations and the travel of different raw material and product delivery in different raw material batches and product batches, so that the dispatching of transportation vehicles is difficult to make, and the prior art is difficult to carry out efficient and scientific goods in and out management and transportation vehicle dispatching according to the goods in and out requirements of batch raw material and product delivery and the related goods emergency degree of production, and lacks a suitable and scientific management means for the needs of different batches in and out of goods and vehicle dispatching tasks.

In view of the above problems, an effective technical solution is currently needed.

Disclosure of Invention

The application aims to provide a vehicle intelligent supervision and dispatch method, a system and a medium for a digital factory, which can acquire information and process data of batch in-and-out tasks through big data and vehicle intelligent matching and dispatch means to obtain dispatching of vehicles, realize vehicle matching and dispatch according to in-and-out demand information and task data and generate list task instruction bars, enable the vehicles to be optimally dispatched according to in-and-out demand and task conditions, and improve accurate control of vehicle dispatching of the factory.

The application also provides a vehicle intelligent supervision and scheduling method of the digital factory, which comprises the following steps:

collecting the information of the incoming demand and the information of the outgoing supply and demand of a factory in a preset time period, respectively carrying out incoming and outgoing data strip analysis on the information of the incoming demand and the information of the outgoing supply and demand to generate an incoming demand element list and an outgoing supply and demand element list, and synthesizing a customer outgoing supply and demand list;

carrying out data analysis on the delivery tasks of delivery according to the delivery demand element list and the customer delivery supply and demand list, matching the delivery vehicles of the category corresponding to the delivery tasks according to the delivery tasks of delivery, and generating a delivery vehicle scheduling model organization tree;

carrying out transport vehicle cargo in-and-out task density statistics according to the transport vehicle dispatching model organization tree, and generating transport vehicle cargo in-and-out task feature images according to transport vehicle cargo in-and-out task density data in a preset time period;

extracting the content data of the delivery vehicle delivery-in and delivery-out tasks according to the delivery vehicle delivery-in and delivery-out task feature images, and processing the content data of the delivery vehicle delivery-in and delivery-out tasks according to a preset delivery vehicle delivery-in and delivery-out task scheduling model to obtain response data of the delivery vehicle delivery-in and delivery-out tasks;

Generating a transport vehicle cargo inlet and outlet task instruction strip within a preset time period according to the transport vehicle cargo inlet and outlet task responsiveness data and preset association coefficients of cargo inlet and outlet of each batch;

and generating a transport vehicle cargo inlet and outlet task scheduling list according to the transport vehicle cargo inlet and outlet task instruction, and scheduling transport vehicle cargo inlet and outlet tasks.

Optionally, in the vehicle intelligent supervision and scheduling method of a digital factory, the collecting the incoming demand information and the outgoing supply and demand information of the factory in a preset time period, and analyzing incoming and outgoing data strips of the incoming demand information and the outgoing supply and demand information respectively to generate an incoming demand element list and an outgoing supply and demand element list, and synthesizing a customer outgoing supply and demand list, including:

respectively acquiring the information of the incoming demand and the information of the outgoing supply and demand of the factory in a preset time period;

extracting the commodity quantity information, commodity attribute category information, commodity supply place information and commodity demand degree information of commodities in each batch according to the commodity demand information;

extracting shipment quantity information, shipment material storage information, shipment material distribution place information and shipment material supply and demand emergency degree information of each batch of shipment according to the shipment supply and demand information;

Respectively analyzing the data strip of the incoming demand information and the outgoing supply and demand information to respectively obtain an incoming demand information data strip and an outgoing supply and demand information data strip;

generating a stock demand element list and a stock supply and demand element list according to the stock demand information data strip and the stock supply and demand information data strip respectively;

and synthesizing a customer shipment supply and demand list according to the corresponding shipment supply and demand element list of each shipment supply customer.

Optionally, in the vehicle intelligent supervision and dispatch method of a digital factory according to the present application, the step of analyzing the data of the in-going and out-going delivery tasks according to the in-going and out-going delivery task data and matching the class of transportation vehicles corresponding to the in-going and out-going delivery tasks according to the in-going and out-going task data, and generating a transportation vehicle dispatch model organization tree includes:

respectively extracting the incoming demand list data and the outgoing supply and demand list data according to the incoming demand element list and the outgoing supply and demand element list;

the goods-incoming demand list data comprise goods-incoming detail quantity data, goods-incoming attribute type data, goods-incoming supply position distance data and goods-incoming demand emergency degree coefficients of goods-incoming batches;

The shipment supply and demand list data comprises shipment detail quantity data, shipment material guarantee period limit data and shipment material distribution position distance data of all batches of shipment, and shipment supply and demand emergency coefficients;

respectively carrying out incoming task data analysis and outgoing task data analysis according to the incoming demand list data and the outgoing supply and demand list data, and respectively processing to obtain incoming task data and outgoing task data corresponding to the goods in and out of each batch;

aggregating the shipment task data of each shipment supply customer corresponding to the customer shipment supply and demand list to obtain customer shipment task package data;

respectively carrying out threshold comparison with a preset transportation vehicle task allocation threshold according to the cargo task data and the cargo task data, and carrying out corresponding allocation of transportation vehicle categories on the cargo inlet tasks and the cargo outlet tasks of each batch of cargo in and out according to the threshold comparison result range;

and according to the incoming demand list data and the incoming task data of the incoming cargoes of each batch and the outgoing supply and demand list data and the outgoing task data of the outgoing cargoes of each batch, integrating the corresponding distributed transport vehicle information to generate a transport vehicle dispatching model organization tree.

Optionally, in the vehicle intelligent supervision and dispatch method of a digital factory of the present application, the calculating the density of the delivery vehicle delivery task according to the delivery vehicle dispatch model organization tree, and generating the delivery vehicle delivery task feature image according to the delivery vehicle delivery task density data within a preset time period includes:

extracting first data of transport vehicle task items corresponding to the goods in each batch and second data of transport vehicle task items corresponding to the goods out each batch according to the transport vehicle dispatching model organization tree;

processing according to the first data of the transport vehicle task items, the position distance data of the goods delivery places of the goods, the emergency degree coefficient of the goods demand and the first quantity of the transport vehicles dispatching of each goods delivery task item and the first category coefficient of the transport vehicles, and obtaining the goods delivery task density data of the transport vehicles of each batch of goods delivery;

processing according to the second data of the transport vehicle task items, the position distance data of the delivery places of the delivery objects, the emergency degree coefficient of the delivery object supply and demand and the second quantity of transport vehicle dispatching of each delivery task item and the second class coefficient of the transport vehicle, and obtaining the delivery task density data of the transport vehicles delivering the goods in each batch;

And carrying out the aggregation of the density data of the whole-batch goods entering and exiting task according to the density data of the goods entering and exiting task of the transport vehicle and the density data of the goods exiting and exiting task of the transport vehicle in each batch in the preset time period, and generating a characteristic image of the goods entering and exiting task of the transport vehicle.

Optionally, in the vehicle intelligent supervision and dispatch method of a digital factory of the present application, the extracting transport vehicle in-out task content data according to the transport vehicle in-out task feature portrait, and processing the transport vehicle in-out task content data according to a preset transport vehicle in-out task dispatch model to obtain transport vehicle in-out task responsiveness data includes:

extracting the content data of the cargo-entering and-exiting tasks of the transport vehicle according to the cargo-entering and-exiting task feature portraits of the transport vehicle;

the transport vehicle cargo inlet and outlet task content data comprise transport vehicle task item entry data, transport vehicle loading and unloading time length data, transport vehicle task item travel data, transport travel road condition difficulty coefficients and transport vehicle allocation quantity of each cargo inlet and outlet task item of each cargo inlet and outlet batch;

and inputting the preset transportation vehicle cargo inlet and outlet task scheduling model according to the transportation vehicle task item data, the transportation vehicle loading and unloading time length data, the transportation vehicle task item travel data, the transportation travel road condition difficulty coefficient and the transportation vehicle allocation quantity for processing, and obtaining the transportation vehicle cargo inlet and outlet task responsiveness data of each batch of cargo inlet and outlet.

Optionally, in the vehicle intelligent supervision and dispatch method of a digital factory according to the present application, the generating a transport vehicle cargo in and out task command bar within a preset time period according to the transport vehicle cargo in and out task responsiveness data and in combination with the preset association coefficient of each lot of cargo in and out includes:

acquiring preset task category priority numbers corresponding to each batch of goods entering and each batch of goods exiting respectively;

according to the response data of the cargo entering and exiting tasks of the transport vehicles in each batch of cargo entering and exiting, carrying out aggregation processing by combining the corresponding task category priority numbers, and obtaining a cargo entering and exiting task scheduling index of the transport vehicles;

and respectively associating the dispatching index of the transport vehicle in-out task with the corresponding in-out demand element list and the corresponding out-out supply and demand element list to obtain a transport vehicle in-out task instruction strip within the preset time period.

Optionally, in the vehicle intelligent supervision and dispatch method of a digital factory of the present application, the generating a transportation vehicle in-out task dispatch list according to the transportation vehicle in-out task instruction and dispatching the transportation vehicle in-out task includes:

sorting the transport vehicle in-and-out task instruction strips corresponding to the transport vehicle in-and-out task instruction strips according to a preset sorting requirement according to the transport vehicle in-and-out task scheduling index in the preset time period to obtain a transport vehicle in-and-out task scheduling list;

Dispatching the transport vehicles according to the corresponding batch of goods entering and exiting tasks according to the transport vehicle goods entering and exiting task dispatching list;

and generating a task dispatching instruction for dispatching the transport vehicle corresponding to the transport vehicle in-and-out task dispatching list, and sending the task dispatching instruction to the dispatching transport vehicle.

In a second aspect, the present application provides a vehicle intelligent supervisory scheduling system for a digital plant, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a vehicle intelligent supervision and scheduling method of a digital factory, and the program of the vehicle intelligent supervision and scheduling method of the digital factory realizes the following steps when being executed by the processor:

Optionally, in the vehicle intelligent supervision and scheduling system of a digital factory according to the present application, the collecting the incoming demand information and the outgoing supply and demand information of the factory in a preset time period, and analyzing incoming and outgoing data strips of the incoming demand information and the outgoing supply and demand information respectively to generate an incoming demand element list and an outgoing supply and demand element list, and synthesizing a customer outgoing supply and demand list, includes:

In a third aspect, the present application also provides a computer readable storage medium, where a program of a vehicle intelligent supervision and scheduling method of a digital plant is included, and when the program of the vehicle intelligent supervision and scheduling method of the digital plant is executed by a processor, the steps of the vehicle intelligent supervision and scheduling method of the digital plant are implemented.

According to the intelligent supervision and scheduling method, system and medium for the vehicles in the digital factory, provided by the application, the information of the incoming and outgoing demands of the factory is collected and analyzed to generate an incoming and outgoing element list, the task data are analyzed to obtain matched transportation vehicles and generate a transportation vehicle scheduling model organization tree, then the transportation vehicle incoming and outgoing task density data are counted to generate transportation vehicle incoming and outgoing task feature images, the incoming and outgoing task content data are extracted and input into a scheduling model to be processed to obtain response data, and then task instruction bars for each batch of incoming and outgoing goods are generated in a combined mode, and a transportation vehicle incoming and outgoing task scheduling list is generated to schedule the vehicle incoming and outgoing tasks; the method comprises the steps of acquiring information and processing data of a batch of goods entering and exiting tasks based on big data and intelligent matching dispatching means of the vehicles to obtain dispatching of the vehicles, carrying out matching dispatching of the vehicles according to goods entering and exiting demand information and task data, generating list task instruction bars, optimizing dispatching of the vehicles according to goods entering and exiting demand and task conditions, and improving accurate control of dispatching of the vehicles in factories.

Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and drawings.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method for intelligent supervision and scheduling of a vehicle in a digital factory according to an embodiment of the present application;

FIG. 2 is a flowchart of a method for intelligently supervising and scheduling vehicles in a digital factory to generate a customer shipment supply and demand list according to an embodiment of the present application;

FIG. 3 is a flowchart of a method for generating a transport vehicle dispatch model organizational tree for a vehicle intelligent supervisory dispatch method for a digital plant according to an embodiment of the present application;

fig. 4 is a schematic structural diagram of a vehicle intelligent supervision and scheduling system of a digital factory according to an embodiment of the present application.

Detailed Description

The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the application, as presented in the figures, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by a person skilled in the art without making any inventive effort, are intended to be within the scope of the present application.

It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.

Referring to fig. 1, fig. 1 is a flowchart of a method for intelligent supervisory scheduling of vehicles in a digital plant according to some embodiments of the application. The intelligent vehicle supervision and scheduling method of the digital factory is used in terminal equipment, such as computers, mobile phone terminals and the like. The intelligent vehicle supervision and scheduling method of the digital factory comprises the following steps:

s101, collecting the information of the incoming demand and the information of the outgoing supply and demand of a factory in a preset time period, respectively analyzing incoming demand information and the information of the outgoing supply and demand by an incoming and outgoing data strip to generate an incoming demand element list and an outgoing supply and demand element list, and synthesizing a customer outgoing supply and demand list;

s102, carrying out data analysis on the delivery tasks of delivery according to the delivery demand element list and the customer delivery supply and demand list, matching the delivery vehicles of the types corresponding to the delivery tasks according to the delivery tasks of delivery, and generating a delivery vehicle scheduling model organization tree;

S103, carrying out transport vehicle cargo in-and-out task density statistics according to the transport vehicle dispatching model organization tree, and generating transport vehicle cargo in-and-out task feature images according to transport vehicle cargo in-and-out task density data in a preset time period;

s104, extracting the content data of the delivery vehicle delivery-in and delivery-out tasks according to the delivery vehicle delivery-in and delivery-out task feature images, and processing the content data of the delivery vehicle delivery-in and delivery-out tasks according to a preset delivery vehicle delivery-in and delivery-out task scheduling model to obtain response data of the delivery vehicle delivery-in and delivery-out tasks;

s105, generating a transport vehicle cargo inlet and outlet task instruction strip within a preset time period according to the transport vehicle cargo inlet and outlet task responsiveness data and preset association coefficients of all batches of cargo inlet and outlet;

s106, a transport vehicle in-out task scheduling list is generated according to the transport vehicle in-out task instruction bar, and the transport vehicle in-out task scheduling is performed.

In order to realize matching management and dispatch of transport vehicles according to the needs of raw material feeding and product discharging in the production process of a digital factory, so as to adapt to the supply chain needs of product production and product discharging required by customers, the matching management of transport vehicle dispatching and each batch of product feeding and discharging tasks is completed, firstly, the feeding demand information and the discharging supply and demand information of the factory in a certain preset time period, namely the upstream raw material feeding demand information and the supply and demand information of product discharging to downstream customers in the time period formulated according to the production needs, and the feeding and discharge data strip analysis is respectively carried out on the feeding and discharging information to generate a feeding demand element list and a discharging supply and demand element list, namely the feeding and discharging item detail analysis is carried out according to the feeding and discharging information to obtain corresponding data strips, and the element list of feeding demand and discharging supply and demand is generated, then analyzing and matching the task data of the list in-and-out cargo, namely matching the vehicles according to the in-and-out cargo tasks corresponding to the in-and-out cargo demands of the list, generating and generating a transport vehicle dispatching model organizing tree according to the related information of the matched vehicles so as to determine the details of the in-and-out cargo tasks and the vehicle resource dispatching, carrying out vehicle task density statistics according to the organizing tree so as to obtain the vehicle dispatching density condition of the task executed by the matched dispatching vehicles according to the task quantity demands in each batch of in-and-out cargo tasks, generating a transport vehicle in-and-out cargo task feature image according to the task density data set of the vehicle in-and-out cargo, wherein the image reflects the task condition data of each batch in-and-out cargo tasks, the in-and-out cargo position distance, the in-and-out cargo emergency degree, the vehicle dispatching quantity and the dispatching condition distribution of the task demand vehicles of the vehicle category, and then extracting vehicle cargo access task data and processing the vehicle cargo access task data through a dispatching model to obtain response data of vehicle cargo access tasks, namely, vehicle dispatching response conditions of cargo access task demands, so as to obtain the vehicle dispatching situation corresponding to the tasks through analyzing and identifying the vehicle dispatching demands of the cargo access tasks of each batch, then generating transport vehicle cargo access task instruction strips in a preset time period by combining preset association coefficients of the cargo access tasks of each batch, namely, carrying out priority evaluation of vehicle dispatching on the cargo access demands of each batch to generate corresponding task instructions, and then generating transport vehicle cargo access task dispatching lists according to the cargo access task instruction strips of each batch to carry out task dispatching on the transport vehicles, so that vehicle resources are matched, scientifically and orderly distributed and dispatched according to the demand situations of the cargo access tasks, and the optimal utilization of transport vehicle resources and the precision and the intellectualization of the management dispatching of transport vehicles in a digital factory are realized.

Referring to fig. 2, fig. 2 is a flowchart illustrating a method for intelligently supervising and scheduling a vehicle in a digital plant to generate a customer shipment supply and demand list according to some embodiments of the present application. According to the embodiment of the application, the collection factory acquires the incoming demand information and the outgoing supply and demand information in a preset time period, analyzes incoming and outgoing data strips of the incoming demand information and the outgoing supply and demand information respectively to generate an incoming demand element list and an outgoing supply and demand element list, and synthesizes the customer outgoing supply and demand list, specifically comprising the following steps:

s201, respectively acquiring the information of the incoming demand and the information of the outgoing supply and demand of a factory in a preset time period;

s202, extracting the information of the quantity of the goods in each batch, the information of the attribute type of the goods, the information of the supply place of the goods and the information of the demand degree of the goods according to the information of the demand;

s203, extracting shipment quantity information, shipment material storage information, shipment material distribution place information and shipment material supply and demand emergency degree information of each batch of shipment according to the shipment supply and demand information;

s204, respectively analyzing the incoming demand information and the outgoing supply and demand information to respectively obtain an incoming demand information data strip and an outgoing supply and demand information data strip;

S205, correspondingly generating a stock demand element list and a stock supply and demand element list according to the stock demand information data strip and the stock supply and demand information data strip respectively;

s206, synthesizing a customer shipment supply and demand list according to the corresponding shipment supply and demand element list of each shipment supply customer.

In order to achieve reasonable dispatching of vehicle resources of the in-out demand, firstly, the in-out demand condition of a factory in a preset time period is required to be clarified, namely, upstream raw material in-out demand information and supply and demand information of products to downstream customers in the preset time period is formulated according to production demand, then, the in-out quantity of in-out goods of each batch is extracted from the in-out demand information, the attribute types of in-out goods, such as solid-liquid-gas state, metal nonmetal, volatility, flammability, storage temperature, length weight, transportation demand and the like, the in-out goods supply place and factory production demand emergency condition information, and the out-out goods quantity, out-out goods guarantee period and storage demand, out-out goods distribution place and out-out goods supply and demand customer emergency condition information are extracted from the out-out goods supply and demand information, then, the incoming and outgoing information is respectively analyzed by an incoming and outgoing data strip, namely, the incoming and outgoing demand information of each batch is extracted to obtain corresponding information data strips, the information data of the incoming and outgoing goods, such as the quantity, the type, the storage temperature, the transportation demand, the demand urgency, the goods location and other item data, are reflected, the element list corresponding to the incoming and outgoing goods is generated according to the incoming demand information and the outgoing supply and demand information, wherein the element list comprises the element data of the incoming and outgoing demand of each batch, the goods data, the type, the storage transportation demand, the demand urgency, the goods location and the like, and meanwhile, the customer outgoing supply and demand list is synthesized according to the corresponding outgoing supply and demand element list of each outgoing supply and demand customer, so that the outgoing supply and demand condition of a single customer is clarified, and the method is convenient for tracking and identifying the shipment situation and progress of important clients.

Referring to fig. 3, fig. 3 is a flow chart of a method for generating a transport vehicle dispatch model organizational tree for intelligent supervisory dispatch of vehicles for a digital plant in some embodiments of the application. According to the embodiment of the application, the data analysis of the in-going and out-going delivery tasks is performed according to the in-going and out-going demand element list and the customer delivery supply and demand list, the transportation vehicles of the types corresponding to the in-going and out-going tasks are matched according to the in-going and out-going task data, and a transportation vehicle scheduling model organizing tree is generated, specifically:

s301, respectively extracting incoming demand list data and outgoing supply and demand list data according to the incoming demand element list and the outgoing supply and demand element list;

s302, the incoming demand list data comprise incoming detail quantity data, incoming attribute type data, incoming supply position distance data and incoming demand emergency coefficients of the incoming goods of each batch;

s303, the delivery supply and demand list data comprise delivery detail quantity data, delivery material warranty limit data, delivery position distance data and delivery supply and demand emergency coefficients of the delivery of each batch of delivery;

s304, carrying out cargo input task data analysis and cargo output task data analysis according to the cargo input demand list data and the cargo output supply and demand list data, and respectively processing to obtain cargo input task data and cargo output task data corresponding to each batch of cargo in and out;

S305, aggregating the shipment task data of each shipment supply customer corresponding to the customer shipment supply and demand list to obtain customer shipment task package data;

s306, respectively carrying out threshold comparison with a preset transportation vehicle task allocation threshold according to the cargo task data and the cargo task data, and carrying out corresponding allocation of transportation vehicle types on the cargo inlet tasks and the cargo outlet tasks of each batch of cargo in and out according to a threshold comparison result range;

s307, according to the incoming demand list data and the incoming task data of the incoming cargoes of each batch and the outgoing supply and demand list data and the outgoing task data of the outgoing cargoes of each batch, the corresponding distributed transport vehicle information is combined for aggregation, and a transport vehicle dispatching model organization tree is generated.

It should be noted that, in order to obtain the category of the transportation vehicle with the matching of the in-out demand, so as to determine the resource demand condition of the task scheduling vehicle corresponding to the in-out goods of each batch, list data of the in-out demand and the out-out demand are respectively extracted according to the element list of the in-out demand and the out-out supply demand, that is, the detail item data of the in-out goods task to the transportation vehicle scheduling demand list of each batch is processed according to the list data to obtain the in-out task data and the out-out task data corresponding to the in-out goods of each batch, the in-out task data reflects the evaluation data of the in-out goods task corresponding to the in-out goods of each batch, wherein the out-out task data of each out-out supply customer corresponding to the out-out supply demand list of the customer is aggregated into customer out-out task package data so as to be convenient for checking and knowing the detail condition of the overall out-out demand task of each customer in a preset time period, the method comprises the steps of respectively carrying out threshold comparison on the cargo-entering task data and a preset vehicle task allocation threshold value, and allocating transport vehicle types to the cargo-entering tasks according to the threshold value comparison result range, namely correspondingly allocating the vehicles or vehicle group types of the cargo-entering tasks according to the task data and the preset threshold value comparison result range, such as matching the transport vehicles of different types for different cargoes such as storage and transportation requirements, cargo specifications, cargo quantity and delivery places, wherein the transport vehicles are divided into a box-type temperature-regulating transport vehicle, two long-distance large-sized loading transport vehicles, three types of common intercity transport vehicles and four types of lengthened middle-distance dump trucks, the corresponding task allocation threshold value ranges of the four types of transport vehicles are respectively corresponding to one type (0.83,1 ], two types of corresponding (0.62,0.83), three types of corresponding (0.36,0.62), four types of corresponding [0 ], 0.36], if the comparison result of the threshold value of the shipment task data of a shipyard customer is 0.29, the matched transportation vehicles are four types of vehicles, and then the transportation vehicle scheduling model organizing tree is generated by combining the corresponding list data of the goods in and out of each batch and the corresponding allocated transportation vehicle information according to the goods in and out task data, and the organizing tree definitely confirms the requirement of the goods in and out task and the task vehicle matching information and the data detail of the corresponding vehicle resource;

The calculation formula of the commodity feeding task data is as follows:

；

wherein ,for the order task data, < >>For the item of stock detail quantity data, +.>Providing distance data for goods to be delivered, < >>For the item of merchandise attribute category data, +.>Emergency factor for the demand of goods for stock, +.>、/>、Is a preset characteristic coefficient;

the calculation formula of the commodity-feeding task data is as follows:

；

wherein ,for shipment task data, < >>For shipment detail quantity data, +.>Distribution position distance data for shipment, < >>For the shelf life limit data of the shipment material, +.>For the emergency factor of delivery of goods, < ->、/>、Is a preset characteristic coefficient (the characteristic coefficient is obtained through query of a preset factory production schedule management database).

According to the embodiment of the invention, the transport vehicle in-and-out task density statistics is performed according to the transport vehicle dispatching model organization tree, and the transport vehicle in-and-out task feature image is generated according to the transport vehicle in-and-out task density data within a preset time period, specifically:

It should be noted that, in order to evaluate the resource allocation situation of the dispatching transportation vehicles corresponding to the goods-in and goods-out tasks in the preset time, the density data of the goods-in and goods-out task frequency of the transportation vehicles of all batches is obtained for aggregation, the feature image reflecting the goods-in and goods-out task feature of the transportation vehicles in the preset time period is obtained, the image reflects the task situation data of the goods-in and goods-out tasks of each batch in the time period, the dispatching situation distribution of the corresponding goods-in and goods-out position distance, the goods-in and goods-out demand, the dispatching quantity of the vehicles and the class of the vehicles, the first data and the second data of the transportation vehicle task items corresponding to the goods-in and goods-out items of each batch are extracted according to the dispatching model organization tree of the transportation vehicles, and the first data or the second data of the transportation vehicle task items are processed according to the position distance of the goods-in and goods-out position, the emergency coefficient of the goods-in and goods-out, the dispatching quantity of the transportation vehicles of the goods-in and goods-out task items and the class coefficient are processed, respectively obtaining the density data of the goods entering and exiting task of the transport vehicle of each batch of goods entering and exiting, then carrying out aggregation on the density data of all batches of goods entering and exiting in a time period to obtain task feature images, realizing the statistics of the goods entering and exiting task density of the transport vehicle, reflecting the dispatching quantity change of the vehicle resources of the goods entering and exiting tasks of each batch and exiting task and the dispatching density status of the goods entering and exiting tasks of each batch, wherein the transport vehicle task item data are the detail data of goods entering and exiting sub-items in the goods entering and exiting tasks of each batch, the task items are a plurality of sub-items contained in the goods entering and exiting tasks of each batch, because one batch of goods entering and exiting sub-items are contained, for example, the steel raw materials entering a steel processing factory contain various steel materials such as steel coils, steel plates, steel tubes and the like, sub-projects of various steel material delivery tasks are dispatched and delivered through different numbers of transport vehicles at different just-delivered sites, so that the density data of the delivery tasks of the transport vehicles are the density conditions of the overall delivery dispatching vehicles for delivering and delivering the goods in and out of the batch;

The calculation formula of the delivery vehicle cargo-entering task density data is as follows:

；

wherein ,for the delivery vehicle order task density data, +.>For the transportation of the first data of the vehicle task item,supply location distance data for the item of interest of the ith item of interest, +.>For the urgency factor of the incoming demand,scheduling a first quantity for the transport vehicle of the ith order entry, the +.>A first category factor of the transport vehicle for the ith order entry, n being the number of order entries,/-for the order entry>、/>Is a preset characteristic coefficient;

the calculation formula of the delivery task density data of the transport vehicle is as follows:

；

wherein ,for delivery of a transport vehicle, density data +.>For the transportation of the second data of the vehicle task item,delivery location distance data for the delivery of the j-th delivery task item, +.>The emergency degree coefficient for the delivery of goods,scheduling a second quantity, for the transport vehicle of the j-th shipment task item,/for the transport vehicle of the j-th shipment task item>A second class coefficient of the transport vehicle for the j-th shipment task item, m being the shipment task item number,/->、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through query of a preset factory production schedule management database).

According to the embodiment of the invention, the content data of the delivery vehicle delivery-in and delivery-out task is extracted according to the delivery vehicle delivery-in and delivery-out task feature image, and the content data of the delivery vehicle delivery-in and delivery-out task is processed according to a preset delivery vehicle delivery-in and delivery-out task scheduling model, so as to obtain the response data of the delivery vehicle delivery-in and delivery-out task, specifically comprising the following steps:

It should be noted that, since the same batch of in-out and out-of-from tasks includes multiple task items, for example, the task items are multiple sub-items included in the batch of in-out and out-of-from tasks, in order to evaluate the task responsiveness of the batch of in-out and out-of-from tasks, to evaluate the priority and order of the dispatching vehicles of the batch of in-out and out-of-from tasks, the content data of the in-out and out-of-from tasks of the transportation vehicles, that is, the content data of the dispatching vehicles of the in-out and out-of tasks of the transportation vehicles is extracted according to the feature portraits of the in-out and in-from tasks of the transportation vehicles, wherein the content data includes the relevant data of in-out item details of in-out items, vehicle loading and unloading time, vehicle task item travel, travel condition difficulty coefficient and vehicle dispatching quantity, and the content data of each task is input into a preset dispatching model of the in-out-of the transportation vehicles to process the dispatching vehicles of the corresponding batch of in-out-of in-out tasks, that is, the priority data of dispatching response of the vehicles of dispatching response of the in-out of the dispatching vehicles reflecting the demands of the in-out-of the in-out and out tasks of the transportation vehicles is identified, so as to realize the optimization of dispatching management of the resources of the transportation vehicles;

The calculation formula of the response data of the delivery tasks of the transport vehicles is as follows:

；

wherein ,response data for delivery tasks of transport vehicles, < +.>Transport vehicle task item entry data for the kth shipment task item, +.>、/>、/>、/>The loading and unloading time length data of the transport vehicle, the travel data of the transport vehicle task item, the road condition difficulty coefficient of the transport travel and the allocation quantity of the transport vehicle are respectively the kth cargo inlet and outlet task item, t is the cargo inlet and outlet task item quantity of the batch cargo inlet and outlet, and +.>For the emergency degree coefficient of the incoming goods demand or the emergency degree coefficient of the outgoing goods supply and demand corresponding to the batch of incoming and outgoing goods, < ->、/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through query of a preset factory production schedule management database).

According to the embodiment of the invention, the transport vehicle cargo in-out task instruction strip within a preset time period is generated according to the transport vehicle cargo in-out task responsiveness data and the preset association coefficient of each batch of cargo in-out, specifically:

It should be noted that, the priority numbers of the attribute types of the preset tasks corresponding to the goods in and out of each batch are combined with the response data of the goods in and out tasks of the transport vehicle to obtain the dispatching index of the goods in and out tasks of the transport vehicle, namely, the source attribute and the task type of the goods in and out tasks are considered and compensated, so that the dispatching of the vehicles of the goods in and out tasks is more accurate, that is, the factors such as the priority of production process and production or the importance of customers or the special purpose of products are considered and compensated, so as to obtain more accurate dispatching index of the vehicles, then the dispatching index is respectively associated with the corresponding element list corresponding to the goods in and out to obtain the dispatching command strip of the goods in and out tasks of the transport vehicle in the preset time period, and the command strip and the dispatching index of the tasks reflect the mapping of the importance and the priority of dispatching the tasks of the transport vehicle, so that the importance or the necessity of dispatching demands of the transport vehicle can be screened and ordered, and the utilization rate of the transport vehicle resources is optimized.

According to the embodiment of the invention, a transportation vehicle in-out task scheduling list is generated according to the transportation vehicle in-out task instruction bar, and the transportation vehicle in-out task scheduling is performed, specifically:

In order to achieve response priority of the transport vehicle dispatching the in-out tasks, the in-out task instruction strips are sequenced according to a preset sequencing requirement and a transport vehicle in-out task dispatching index in a preset time period, a transport vehicle in-out task dispatching list is obtained, the sequencing of the dispatching list is the sequencing of the transport vehicle in-out task dispatching priority, the transport vehicle is dispatched according to the dispatching list according to the corresponding batch in-out tasks, task dispatching instructions are generated and sent to the corresponding vehicles, optimal dispatching and utilization of transport vehicle resources are achieved, and the accuracy of vehicle resource management is improved.

According to an embodiment of the present invention, further comprising:

carrying out dynamic self-checking on transport vehicles for executing the goods entering and exiting tasks of each batch, and acquiring task execution information and vehicle condition information of the transport vehicles;

analyzing and obtaining vehicle residual state data according to the task execution information and the vehicle condition information;

judging whether the vehicle residual state data meets a preset task demand threshold value of residual task demand data extracted by corresponding goods intake demand information or/and goods delivery supply and demand information;

if the threshold comparison result does not meet the requirement of the preset task requirement threshold, a recall instruction is sent to the transport vehicle for recall;

if the requirement of the preset task requirement threshold is met, the transportation vehicle is not recalled, and tracking and state monitoring are performed.

It should be noted that, in order to monitor the vehicle condition of the transport vehicle, to dynamically evaluate whether the remaining condition of the transport vehicle can smoothly execute the remaining task amount of the in-out task, the vehicle remaining state data obtained through the task execution information of the vehicle and the vehicle condition information is compared with the threshold value of the remaining task demand data extracted by the in-delivery demand information or/and the out-delivery supply demand information corresponding to the vehicle execution task, whether the transport vehicle is recalled or not is judged according to whether the threshold value comparison result of the vehicle remaining state data and the preset task demand threshold value meets the preset threshold value requirement, for example, if the vehicle remaining state data of a certain transport vehicle a is lower than the preset requirement of 85% of the preset task demand threshold value of the remaining task demand data, the transport vehicle a needs to be recalled.

As shown in fig. 4, the invention also discloses a vehicle intelligent supervision and scheduling system 4 of the digital factory, which comprises a memory 41 and a processor 42, wherein the memory comprises a vehicle intelligent supervision and scheduling method program of the digital factory, and the vehicle intelligent supervision and scheduling method program of the digital factory realizes the following steps when being executed by the processor:

According to the embodiment of the invention, the collection factory acquires the incoming demand information and the outgoing supply and demand information in a preset time period, analyzes incoming and outgoing data strips of the incoming demand information and the outgoing supply and demand information respectively to generate an incoming demand element list and an outgoing supply and demand element list, and synthesizes the customer outgoing supply and demand list, specifically comprising the following steps:

According to the embodiment of the invention, the data analysis of the in-going and out-going delivery tasks is performed according to the in-going and out-going demand element list and the customer delivery supply and demand list, the transportation vehicles of the types corresponding to the in-going and out-going tasks are matched according to the in-going and out-going task data, and a transportation vehicle scheduling model organizing tree is generated, specifically:

The calculation formula of the commodity feeding task data is as follows:

；

the calculation formula of the commodity-feeding task data is as follows:

；

wherein ,for delivery of a transport vehicle, density data +.>For the second data of the transport vehicle task item, +.>Delivery location distance data for the delivery of the j-th delivery task item, +.>For the emergency factor of delivery of goods, < ->Scheduling a second quantity, for the transport vehicle of the j-th shipment task item,/for the transport vehicle of the j-th shipment task item>A second class coefficient of the transport vehicle for the j-th shipment task item, m being the shipment task item number,/->、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained through query of a preset factory production schedule management database).

；

According to an embodiment of the present invention, further comprising:

A third aspect of the present invention provides a readable storage medium having embodied therein a vehicle intelligent supervisory scheduling method program for a digital plant, which when executed by a processor, implements the steps of the vehicle intelligent supervisory scheduling method for a digital plant as described in any one of the above.

The invention discloses a vehicle intelligent supervision and scheduling method, a system and a medium for a digital factory, which are characterized in that the information of the incoming and outgoing demands of the factory is collected and analyzed to generate an incoming and outgoing element list, the task data is analyzed to obtain matched transport vehicles and generate a transport vehicle scheduling model organization tree, then the transport vehicle incoming and outgoing task density data is counted to generate transport vehicle incoming and outgoing task feature images, the incoming and outgoing task content data is extracted and input into a scheduling model to be processed to obtain response data, and then task instruction strips of goods in and out of each batch are generated in a combined mode, and a transport vehicle incoming and outgoing task scheduling list is generated to schedule vehicle incoming and outgoing tasks; the method comprises the steps of acquiring information and processing data of a batch of goods entering and exiting tasks based on big data and intelligent matching dispatching means of the vehicles to obtain dispatching of the vehicles, carrying out matching dispatching of the vehicles according to goods entering and exiting demand information and task data, generating list task instruction bars, optimizing dispatching of the vehicles according to goods entering and exiting demand and task conditions, and improving accurate control of dispatching of the vehicles in factories.

In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.

The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk or an optical disk, or the like, which can store program codes.

Alternatively, the above-described integrated units of the present invention may be stored in a readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solution of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including 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 methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.

Claims

1. The intelligent vehicle supervision and scheduling method for the digital factory is characterized by comprising the following steps of:

generating a transport vehicle cargo inlet and outlet task scheduling list according to the transport vehicle cargo inlet and outlet task instruction bar, and scheduling transport vehicle cargo inlet and outlet tasks;

the step of analyzing the input and output task data according to the input demand element list and the client output supply and demand list, matching the transport vehicles of the category corresponding to the input and output task according to the input and output task data, and generating a transport vehicle scheduling model organization tree, comprising:

the delivery supply and demand list data comprises delivery detail quantity data, delivery material guarantee period limit data and delivery distribution position distance data of each batch of delivery, and delivery supply and demand emergency coefficients;

respectively carrying out threshold comparison with a preset transportation vehicle task allocation threshold according to the goods entering task data and the goods leaving task data, and carrying out corresponding allocation of transportation vehicle categories on the goods entering tasks and the goods leaving tasks of each batch according to a threshold comparison result range;

according to the incoming demand list data and incoming task data of the incoming cargoes of each batch and the outgoing supply and demand list data and outgoing task data of the outgoing cargoes of each batch, the incoming demand list data and the incoming task data and the outgoing supply and demand list data and the outgoing task data of the outgoing cargoes of each batch are combined with corresponding allocated transport vehicle information to be aggregated, and a transport vehicle dispatching model organization tree is generated;

the calculation formula of the commodity feeding task data is as follows:

；

wherein ,for the order task data, < >>For the item of stock detail quantity data, +.>Providing distance data for goods to be delivered, < > >For the item of merchandise attribute category data, +.>Emergency factor for the demand of goods for stock, +.>、/>、/>Is a preset characteristic coefficient;

the calculation formula of the shipment task data is as follows:

；

wherein ,for shipment task data, < >>For shipment detail quantity data, +.>Distribution position distance data for shipment, < >>For the shelf life limit data of the shipment material, +.>For the emergency factor of delivery of goods, < ->、/>、/>Is a preset characteristic coefficient.

2. The intelligent vehicle supervision and scheduling method of a digital factory according to claim 1, wherein the collecting the incoming demand information and the outgoing supply and demand information of the factory in a preset time period, and analyzing incoming and outgoing data strips of the incoming demand information and the outgoing supply and demand information respectively to generate an incoming demand element list and an outgoing supply and demand element list, and synthesizing a customer outgoing supply and demand list, comprises:

3. The method for intelligently supervising and dispatching vehicles in a digital factory according to claim 2, wherein the step of counting the density of the delivery tasks of the delivery vehicles according to the delivery vehicle dispatching model organization tree and generating the characteristic image of the delivery tasks of the delivery vehicles according to the density data of the delivery tasks of the delivery vehicles within a preset time period comprises the steps of:

4. The intelligent supervision and dispatch method for vehicles in a digital factory according to claim 3, wherein the extracting the content data of the delivery vehicle in-out tasks according to the characteristic figures of the delivery vehicle in-out tasks, and processing the content data of the delivery vehicle in-out tasks according to a preset delivery vehicle in-out task dispatch model, to obtain the response data of the delivery vehicle in-out tasks, comprises:

5. The method for intelligently supervising and dispatching vehicles in a digital factory according to claim 4, wherein the step of generating a transport vehicle in-out task command bar within a preset time period according to the transport vehicle in-out task responsiveness data and the preset association coefficients of the in-out cargoes of each batch comprises:

6. The method of claim 5, wherein generating a delivery vehicle in-out task schedule list based on the delivery vehicle in-out task command and scheduling delivery vehicle in-out tasks comprises:

7. An intelligent supervisory dispatch system for vehicles in a digital plant, the system comprising: the system comprises a memory and a processor, wherein the memory comprises a program of a vehicle intelligent supervision and scheduling method of a digital factory, and the program of the vehicle intelligent supervision and scheduling method of the digital factory realizes the following steps when being executed by the processor:

the calculation formula of the commodity feeding task data is as follows:

；

wherein ,for the order task data, < >>For the item of stock detail quantity data, +.>Providing distance data for goods to be delivered, < >>For the item of merchandise attribute category data, +.>Emergency factor for the demand of goods for stock, +.>、/>、/>Is a preset characteristic coefficient;

the calculation formula of the shipment task data is as follows:

；

wherein ,for shipment task data, < >>To go outGoods detail quantity data, < > >Distribution position distance data for shipment, < >>For the shelf life limit data of the shipment material, +.>For the emergency factor of delivery of goods, < ->、/>、/>Is a preset characteristic coefficient.

8. The intelligent supervisory dispatch system for vehicles in a digital factory according to claim 7, wherein the collecting the incoming demand information and the outgoing supply and demand information of the factory in a preset time period, and analyzing incoming and outgoing data strips of the incoming demand information and the outgoing supply and demand information respectively to generate an incoming demand element list and an outgoing supply and demand element list, and synthesizing a customer outgoing supply and demand list, comprises:

9. A computer-readable storage medium, wherein a vehicle intelligent supervisory scheduling method program of a digital plant is included in the computer-readable storage medium, and when the vehicle intelligent supervisory scheduling method program of the digital plant is executed by a processor, the steps of the vehicle intelligent supervisory scheduling method of the digital plant according to any one of claims 1 to 6 are implemented.