A scheduling method, system and device of a concrete mixer truck and a storage medium

Abstract

The application provides a scheduling method and system of a concrete mixer truck, equipment and a storage medium, relates to the field of concrete logistics scheduling, and comprises the following steps: obtaining an order set and a vehicle set of a mixing station; screening a schedulable mixing truck corresponding to an order from the vehicle set; determining the schedulable mixing truck through multidimensional screening and transport capacity feasibility verification; distributing the schedulable mixing truck to all orders through a departure sequence optimization model to generate a target scheduling scheme, so that the mixing truck is accurately matched, the scheduling efficiency and resource utilization rate are improved, and the effective execution of orders is ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent concrete logistics scheduling, and in particular to a concrete mixer truck scheduling method and system, an electronic device, a computer readable storage medium and a computer product. BACKGROUND

[0002] As one of the most widely used building materials, the supply efficiency of concrete directly affects the progress and cost of construction projects.

[0003] Currently, the supply chain model of concrete can be simplified as a chain of "concrete mixing station (hereinafter referred to as mixing station) production → concrete mixer truck (hereinafter referred to as mixer truck) distribution → construction site (hereinafter referred to as construction site) reception".

[0004] The traditional collaborative process is mainly as follows: the construction site places an order with the mixing station, the mixing station schedules its own or rented mixer trucks according to the order quantity, the mixer truck is transported from the mixing station to the construction site after loading, and then returns or executes the next order after unloading. Among them, the mixing station is responsible for obtaining orders from the construction site and producing concrete; the mixer truck rental company (hereinafter referred to as vehicle rental company) provides the mixer truck.

[0005] Due to the lack of real-time data sharing of transport capacity between the mixing station and the vehicle rental company, the orders of the mixing station, the transport capacity of the vehicle rental company, and the road permit information belong to different systems, resulting in the coexistence of idle vehicles and insufficient transport capacity.

[0006] A concrete mixer truck scheduling method, system, electronic device, computer readable storage medium and computer product are provided. SUMMARY

[0007] The present application provides a concrete mixer truck scheduling method, system, electronic device, computer readable storage medium and computer product, which mainly solves the problems of unreasonable matching of mixer trucks and orders, inefficient dispatch sequence, and insufficient dynamic adjustment in traditional scheduling. The dispatchable vehicles are accurately matched through multi-dimensional (region, time period, demand, compliance) screening and transport capacity verification; the task is sorted and distributed according to the unloading time period by using the dispatch sequence optimization model, and an efficient scheduling scheme is generated; the task sequence and execution time are adjusted to adapt to the capacity change by dynamically predicting the route and unloading time based on real-time location, road conditions, vehicle conditions, weather, etc. The accuracy of vehicle and order matching is improved, the efficiency is improved by optimizing the dispatch sequence, the delay is reduced by dynamic adjustment, the effective window of the order is ensured, and the efficient use of resources and the flexibility of scheduling are realized.

[0008] The concrete mixer truck scheduling method provided by the present application adopts the following technical scheme, which comprises:

[0009] obtaining an order set and a vehicle set of the mixing station;

[0010] screening a dispatchable mixer truck corresponding to an order from the vehicle set; specifically, obtaining a current order from the order set; constructing a screening rule of the current order according to order information of the current order; judging whether an available mixer truck in the vehicle set meets the screening rule; if the available mixer truck meets all the screening rules, the available mixer truck is determined as a primary screening qualified mixer truck; performing a transport capacity feasibility verification on the primary screening qualified mixer truck, and determining the dispatchable mixer truck according to a transport capacity feasibility verification result;

[0011] allocating the dispatchable mixer truck to all the orders through a dispatch sequence optimization model to generate a target scheduling scheme, the target scheduling scheme including sequences of a plurality of task sheets.

[0012] Optionally, the constructing the screening rule of the current order according to the order information of the current order includes:

[0013] constructing a region screening rule of the current order according to the order information of the current order; and / or,

[0014] constructing a time period screening rule of the current order according to the order information of the current order; and / or,

[0015] constructing a demand screening rule of the current order according to the order information of the current order; and / or,

[0016] constructing a compliance screening rule of the current order according to the order information of the current order.

[0017] Optionally, the allocating the dispatchable mixer truck to all the orders through the dispatch sequence optimization model to generate the target scheduling scheme includes:

[0018] dividing the orders into batches according to unloading time periods;

[0019] sorting the orders in each batch;

[0020] determining, based on the sorting result, a task sheet corresponding to each order by the dispatch sequence optimization model.

[0021] Optionally, the determining, based on the sorting result, the task sheet corresponding to each order by the dispatch sequence optimization model includes:

[0022] allocating one or more dispatchable mixer trucks to the order from a set of dispatchable mixer trucks corresponding to the order to generate a plurality of allocation results;

[0023] constraining and verifying each allocation result according to a constraint strategy to screen a final allocation result;

[0024] The dispatchable mixer truck involved in the final allocation result is taken as a candidate mixer truck; a task list is established for each candidate mixer truck;

[0025] An execution time is allocated for each task list; the execution time meets the valid window of the order and the cycle period of the mixer truck.

[0026] Optionally, the method further comprises:

[0027] Based on the target scheduling scheme and / or the selection instruction of the user, a target mixer truck is determined;

[0028] The current task list and the unexecuted task list of the target mixer truck are obtained;

[0029] The travel time consumption and the unloading time consumption are predicted in combination with the actual state data; wherein, the basic time consumption of the target mixer truck is obtained in combination with the real-time position of the target mixer truck, the actual road condition data, the actual vehicle condition data and the actual vehicle model; the risk compensation parameter is calculated in combination with the actual equipment data and the actual weather data; the travel time consumption is predicted according to the basic time consumption and the risk compensation parameter; the unloading time consumption is predicted based on the actual transportation data;

[0030] The travel time consumption and the unloading time consumption are summarized to update the execution end time of the current task list and the execution start time of the unexecuted task list.

[0031] Optionally, the basic time consumption of the target mixer truck is obtained in combination with the real-time position of the target mixer truck, the actual road condition data, the actual vehicle condition data and the actual vehicle model, comprising:

[0032] The ideal driving time consumption is obtained according to the average designed speed and the real-time position;

[0033] The dynamic influence factor is calculated in combination with the actual road condition data and the actual vehicle condition data; specifically, the sudden braking influence factor is calculated according to the actual road condition data; the rotation speed influence factor is calculated according to the actual vehicle condition data; the dynamic influence factor is calculated in combination with the sudden braking influence factor and the rotation speed influence factor, wherein, the dynamic influence factor ; 、 The preset weighting coefficient is taken as the dynamic influence factor;

[0034] The static influence factor is associated according to the actual vehicle model;

[0035] The product of the ideal driving time consumption, the dynamic influence factor and the static influence factor is taken as the basic time consumption.

[0036] Optionally, the method further comprises:

[0037] The unexecuted task list is sequentially adjusted in combination with the mixing station state data and the urgency of the order; and / or,

[0038] calculate a construction site location concentration according to a construction site location of the task list, and adjust the order of the unexecuted task list according to the construction site location concentration; and / or,

[0039] predict a schedulable capacity of the mixing plant according to the mixing plant state data, and adjust the execution time of the unexecuted task list according to a decline ratio of the schedulable capacity.

[0040] The scheduling system of the concrete mixer truck provided in the application adopts the technical scheme as follows, comprising:

[0041] an information acquisition module, configured to acquire a set of orders and a set of vehicles of the mixing plant;

[0042] a vehicle screening module, configured to screen a schedulable mixer truck corresponding to an order from the set of vehicles;

[0043] a scheduling and distribution module, configured to distribute the schedulable mixer truck to all the orders through a dispatch sequence optimization model, and generate a target scheduling scheme, wherein the target scheduling scheme comprises a sequence of a plurality of task lists.

[0044] Optionally, the vehicle screening module comprises:

[0045] an order positioning submodule, configured to acquire a current order from the set of orders;

[0046] a rule construction submodule, configured to construct a screening rule of the current order according to order information of the current order;

[0047] a rule judgment submodule, configured to judge whether an available mixer truck in the set of vehicles meets the screening rule;

[0048] a first screening submodule, configured to identify the available mixer truck as a primary screening qualified mixer truck if the available mixer truck meets all the screening rules;

[0049] a second screening submodule, configured to perform a transport capacity feasibility verification on the primary screening qualified mixer truck, and determine the schedulable mixer truck according to a transport capacity feasibility verification result;

[0050] Optionally, the rule construction submodule comprises:

[0051] a first rule construction unit, configured to construct a regional screening rule of the current order according to order information of the current order;

[0052] a second rule construction unit, configured to construct a time period screening rule of the current order according to order information of the current order;

[0053] a third rule construction unit configured to construct a demand screening rule of the current order according to order information of the current order;

[0054] a fourth rule construction unit configured to construct a compliance screening rule of the current order according to order information of the current order.

[0055] Optionally, the scheduling and distribution module comprises:

[0056] a batch division sub-module configured to divide the orders into batches according to unloading time periods;

[0057] a sorting sub-module configured to sort the orders in each batch;

[0058] a distribution sub-module configured to determine, based on the sorting result, a task order corresponding to each order in sequence by using the dispatch sequence optimization model.

[0059] Optionally, the distribution sub-module comprises:

[0060] an initial distribution unit configured to distribute one or more schedulable mixers to the order from a schedulable mixer set corresponding to the order, to generate a plurality of distribution results;

[0061] a final distribution unit configured to perform constraint verification on each distribution result according to a constraint strategy, and to filter out a final distribution result;

[0062] a task order construction unit configured to take schedulable mixers involved in the final distribution result as candidate mixers, and to establish a task order for each candidate mixer;

[0063] a time distribution unit configured to assign an execution time to each task order; the execution time meets an effective window of the order and a cycle period of the mixer.

[0064] Optionally, the system further comprises:

[0065] a designation module configured to determine a target mixer based on the target scheduling scheme and / or a selection instruction of a user;

[0066] a task order acquisition module configured to acquire a current task order and an unexecuted task order of the target mixer;

[0067] a time consumption prediction module configured to predict a route time consumption and an unloading time consumption in combination with actual state data;

[0068] a real-time update module configured to aggregate the route time consumption and the unloading time consumption, and to update an execution end time of the current task order and an execution start time of the unexecuted task order.

[0069] Optionally, the time consumption prediction module comprises: a route time consumption submodule and a unloading time consumption submodule;

[0070] The route time consumption submodule comprises:

[0071] a basic time consumption calculation unit, configured to obtain a basic time consumption of the target mixer truck in combination with real-time position of the target mixer truck, actual road condition data, actual vehicle condition data and actual vehicle model;

[0072] a risk compensation parameter calculation unit, configured to calculate a risk compensation parameter in combination with actual equipment data and actual weather data;

[0073] a route time consumption prediction unit, configured to predict a route time consumption according to the basic time consumption and the risk compensation parameter;

[0074] The unloading time consumption submodule is configured to predict an unloading time consumption based on actual transportation data.

[0075] Optionally, the basic time consumption calculation unit comprises:

[0076] an ideal driving time consumption calculation subunit, configured to obtain an ideal driving time consumption according to an average design speed and the real-time position;

[0077] a dynamic influence factor calculation subunit, configured to calculate a dynamic influence factor in combination with the actual road condition data and the actual vehicle condition data; specifically, an emergency braking influence factor is calculated according to the actual road condition data; a rotation speed influence factor is calculated according to the actual vehicle condition data; and the dynamic influence factor is calculated in combination with the emergency braking influence factor and the rotation speed influence factor, wherein the dynamic influence factor ; 、 is a preset weighting coefficient;

[0078] a static influence factor calculation subunit, configured to associate a static influence factor according to the actual vehicle model;

[0079] a basic time consumption calculation subunit, configured to take a product of the ideal driving time consumption, the dynamic influence factor and the static influence factor as the basic time consumption.

[0080] Optionally, the real-time scheduling module further comprises:

[0081] The real-time scheduling module comprises:

[0082] a first scheduling submodule, configured to sequentially adjust the unexecuted task sheets in combination with the mixing station state data and an urgency degree of the order; and / or,

[0083] a second scheduling submodule, configured to calculate a construction site location concentration degree according to construction site locations of the task sheets, and sequentially adjust the unexecuted task sheets according to the construction site location concentration degree; and / or,

[0084] a third scheduling submodule, configured to predict a schedulable productivity of the mixing station according to the mixing station state data, and adjust an execution time of the unexecuted task list according to a descending proportion of the schedulable productivity.

[0085] The present specification also provides an electronic device, where the electronic device includes:

[0086] a processor; and

[0087] a memory storing computer-executable instructions that, when executed, cause the processor to perform any of the above methods.

[0088] The present specification also provides a computer-readable storage medium, where the computer-readable storage medium stores one or more programs that, when executed by a processor, implement any of the above methods.

[0089] The present specification also provides a computer program product, where the computer program product includes: computer programs / instructions that, when executed by a processor, implement any of the above methods.

[0090] In the present application, an order set and a vehicle set of a mixing station are acquired; schedulable mixers corresponding to orders are screened out from the vehicle set; the schedulable mixers are determined through multi-dimensional screening and transport capacity feasibility verification; all orders are allocated to schedulable mixers through a dispatch sequence optimization model to generate a target scheduling scheme, so as to realize accurate matching of mixers, improve scheduling efficiency and resource utilization, and ensure effective execution of orders. BRIEF DESCRIPTION OF DRAWINGS

[0091] Figure 1 A principle diagram of a scheduling method of a concrete mixer truck provided by an embodiment of the present specification;

[0092] Figure 2 A flowchart of a scheduling method of a concrete mixer truck provided by an embodiment of the present specification;

[0093] Figure 3 A structure diagram of a scheduling system of a concrete mixer truck provided by an embodiment of the present specification;

[0094] Figure 4 A structure diagram of an electronic device provided by an embodiment of the present specification;

[0095] Figure 5 A principle diagram of a computer-readable medium provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0096] The following description is provided so as to enable any person skilled in the art to practice the application. The preferred embodiments described herein are only examples of the application and the application is not limited to these embodiments. Various modifications to these embodiments can be made by those skilled in the art without departing from the spirit and scope of the application. Those skilled in the art will further appreciate that the application can be used with any assay method as long as an effective amount of the compound is delivered to the cells, tissues, or organs being treated.

[0097] Example embodiments of the present application will now be described more fully with reference to the accompanying drawings. Example embodiments of the present application, however, can be implemented in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these example embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the application to those skilled in the art, with the scope of the application being shown only by the claims. Like reference numerals refer to like elements throughout the several views of the drawings and similar components will not be described in detail for each of the illustrated embodiments.

[0098] In the case of specific embodiments described in accordance with the technical concept of the present application, features, structures, characteristics or other details described in a certain embodiment can not be excluded from being combined in one or more other embodiments in a suitable manner.

[0099] In the description of specific embodiments, features, structures, characteristics or other details described in the present application are intended to enable those skilled in the art to fully understand the embodiments. However, it is not excluded that one or more of the specific features, structures, characteristics or other details can not be practiced by those skilled in the art without the specific features, structures, characteristics or other details.

[0100] The flowcharts shown in the drawings are only exemplary illustrations and do not necessarily include all contents and operations / steps, nor are they necessarily executed in the order described. For example, some operations / steps can be further broken down, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to the actual situation.

[0101] The block diagrams shown in the drawings are only functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0102] The term "and / or" or "and / or" includes all combinations of one or more of the associated listed items.

[0103] Figure 1 A schematic diagram of the principle of a scheduling method of a concrete mixer truck is provided for the embodiments of the present application, and the method comprises:

[0104] S1 obtains a set of orders and a set of vehicles of a mixing plant;

[0105] S2 screens a dispatchable mixer truck corresponding to an order from the vehicle set; specifically, a current order is obtained from the order set; a screening rule of the current order is constructed according to order information of the current order; it is judged whether an available mixer truck in the vehicle set meets the screening rule; if the available mixer truck meets all the screening rules, the available mixer truck is determined as a primary screening qualified mixer truck; the primary screening qualified mixer truck is subjected to a transport capacity feasibility verification, and the dispatchable mixer truck is determined according to a transport capacity feasibility verification result;

[0106] S4 performs allocation of the dispatchable mixer truck for all the orders through a departure sequence optimization model, to generate a target scheduling scheme, the target scheduling scheme including a sequence of a plurality of task orders.

[0107] Figure 2 A flowchart of a scheduling method of a concrete mixer truck provided by an embodiment of the present specification, which specifically includes:

[0108] S1 obtains an order set and a vehicle set of a mixing station;

[0109] The order set of the mixing station includes a plurality of to-be-allocated original orders; the vehicle set includes a plurality of available mixer trucks;

[0110] S11 obtains orders and corresponding order information of a mixing station, and constructs an order set;

[0111] In an embodiment of the present specification, the orders of the current mixing station are automatically synchronized through a mixing station ERP interface.

[0112] In another embodiment of the present specification, the orders of the next day of the mixing station are uploaded by the mixing station.

[0113] The orders are summarized to construct an order set of the mixing station The order set includes a plurality of ordered orders . That is, the order set .

[0114] The order information of each order includes a construction site location , a concrete demand , an unloading time period , and a priority weight .

[0115] The orders are obtained through automatic synchronization or uploading to construct a complete order set, to provide an accurate data basis for subsequent scheduling. ​​​

[0116] S12 retrieves a list of available mixer trucks and constructs a vehicle set;

[0117] The vehicle rental company provides a list of available mixer trucks; available mixer trucks refer to mixer trucks that can be used for mixing.

[0118] The list of available concrete mixer trucks includes: basic information about the available concrete mixer trucks; and information based on the available concrete mixer trucks. Construct vehicle collection That is, vehicle collection .

[0119] By integrating information on available mixer trucks, a vehicle pool is constructed, providing a basic resource pool for subsequent screening.

[0120] S13 retrieves the vehicle information of the available mixer trucks based on their basic information;

[0121] A road permit database is pre-built; the road permit database includes a one-to-one correspondence between cement mixer trucks and road permit information.

[0122] After obtaining the basic information of the mixer truck, the corresponding road permit information is retrieved from the road permit database based on the license plate number in the basic information; the current status of the mixer truck is determined based on the vehicle's IoT sensors; and the vehicle information of available mixer trucks is obtained by summarizing the basic information, road permit information and current status.

[0123] cement mixer truck The vehicle information includes, but is not limited to: basic information, road permit information, and current status (available / busy). Basic information includes, but is not limited to: license plate number, vehicle type, tonnage, and maximum load capacity. Historical violation records. Road permit information includes, but is not limited to: the scope of the road permit. 1. Type of road permit (ordinary permit / special permit), validity period of road permit, permitted travel time (e.g., prohibited travel during morning and evening rush hours).

[0124] To mitigate the risk of violations, this manual incorporates strict safety driving constraints and automatically checks the compliance of road permits. If the unloading period of an order exceeds the validity period of the road permit, the permit is deemed non-compliant, and the corresponding available mixer truck is removed from the vehicle pool to avoid violations and ensure the legality of transportation.

[0125] S2 selects the dispatchable mixer trucks corresponding to the orders from the vehicle set;

[0126] S21 retrieves the current order from the order set;

[0127] S22 uses a filtering strategy to find the pre-screened qualified mixer truck corresponding to the current order from the vehicle set;

[0128] The screening strategy includes a plurality of screening rules.

[0129] S221 constructing a screening rule of the current order according to order information of the current order;

[0130] S222 judging whether an available mixer truck in the vehicle set meets the screening rule;

[0131] In an embodiment of the present specification, the screening strategy includes a regional screening rule.

[0132] S221-A constructing a regional screening rule of the current order according to order information of the current order;

[0133] The regional screening rule includes that the road license range of the available mixer truck and the target area range have an intersection.

[0134] The target area range is divided according to the current order information; as preferred, the target area range is the real-time expansion range of the construction area.

[0135] In actual implementation, the construction site position of the current order is found, the actual construction area is found at the construction site position, and the real-time expansion range is taken as the origin with the preset radius.

[0136] S222-A judging whether the available mixer truck meets the regional screening rule;

[0137] Specifically, it is judged whether the road license range of each available mixer truck and the target area range have a range intersection;

[0138] If the road license range The target area range It is determined that the road license range of the available mixer truck and the target area range have regional coverage, and the available mixer truck meets the regional screening rule.

[0139] If the road license range The target area range It is determined that the road license range of the available mixer truck and the target area range do not have regional coverage, and the available mixer truck does not meet the regional screening rule.

[0140] Through the intersection screening of the road license range and the target area, it is ensured that the mixer truck can enter the construction site legally and invalid scheduling is avoided.

[0141] In an embodiment of the present specification, the screening strategy includes a time period screening rule.

[0142] S221-B constructing a time period screening rule of the current order according to order information of the current order;

[0143] The time period screening rule includes: the allowed passing time period of the available mixer truck and the unloading time period of the current order have an intersection.

[0144] S222-B determines whether the available mixer truck meets the time period screening rule.

[0145] Specifically, it is determined whether the allowed passing time period of each available mixer truck and the unloading time period have an overlapping interval.

[0146] If the allowed passing time period The unloading time period It is determined that the allowed passing time period of the available mixer truck and the unloading time period have coverage, and the available mixer truck meets the time period screening rule.

[0147] If the allowed passing time period The unloading time period It is determined that the allowed passing time period of the available mixer truck and the unloading time period do not have coverage, and the available mixer truck does not meet the time period screening rule.

[0148] Through the intersection screening of the allowed passing time period and the unloading time period, it is ensured that the mixer truck arrives within the allowed time, and time period conflicts are avoided.

[0149] In an embodiment of the present specification, the screening strategy includes a time period screening rule.

[0150] S221-C constructs a demand screening rule of the current order according to order information of the current order.

[0151] The demand screening rule includes: the concrete demand of the current order ≤ the tonnage of the available mixer truck, and the concrete demand of the current order ≤ 80% of the maximum load of the available mixer truck.

[0152] S222-C determines whether the available mixer truck meets the demand screening rule.

[0153] If the concrete demand of the current order ≤ the tonnage of the available mixer truck, and the concrete demand of the current order ≤ 80% of the maximum load of the available mixer truck, it is determined that the available mixer truck meets the demand screening rule.

[0154] If the concrete demand of the current order > the tonnage of the available mixer truck, and / or the concrete demand of the current order > 80% of the maximum load of the available mixer truck, it is determined that the available mixer truck does not meet the demand screening rule.

[0155] Through the demand and vehicle tonnage / load matching screening, the risk of overloading is prevented, and transportation safety is ensured.

[0156] In an embodiment of the present specification, the screening strategy includes a compliance screening rule.

[0157] S221-D constructing a compliance screening rule of the current order according to order information of the current order;

[0158] The compliance screening rule comprises: a compliance score ≥ a preset compliance threshold.

[0159] S222-D judging whether the available mixer truck conforms to the compliance screening rule;

[0160] Specifically, the historical violation records of the available mixer truck are queried, and a compliance score is calculated based on the historical violation records; and it is judged whether the compliance score meets the requirements.

[0161] The historical violation records comprise all violation records within a preset detection period.

[0162] The violation records comprise violation items and corresponding severity. The violation items comprise, but are not limited to, overload and expired road permit. The violation items correspond to violation weights. The violation weight is a number, which is manually scored.

[0163] The compliance score = the number of violation times the violation severity.

[0164] The number of violation times is the number of violation records; the violation severity can be the maximum violation weight in the historical violation records; or a weighted average of all historical violation records is obtained, and the violation weight is taken as the violation severity. The preset compliance threshold is preferably 70.

[0165] If the compliance score of the mixer truck ≥ the preset compliance threshold, it is determined that the available mixer truck conforms to the compliance screening rule.

[0166] If the compliance score of the mixer truck < the preset compliance threshold, it is determined that the available mixer truck does not conform to the compliance screening rule.

[0167] The low-risk vehicle is screened through the compliance score, and the mixer truck with a good safety record is preferentially selected.

[0168] S223 if the available mixer truck conforms to all the screening rules, the available mixer truck is determined to be a primary screening qualified mixer truck;

[0169] In an embodiment of the present specification, the basic screening strategy comprises one or more of the following: a regional screening rule, a time screening rule, a demand screening rule, and a compliance screening rule.

[0170] When the available mixer truck conforms to all the screening rules, the available mixer truck is determined to be a primary screening qualified mixer truck.

[0171] The primary screening qualified vehicle is screened by comprehensively considering multiple rules, the subsequent verification range is narrowed, and the dispatching efficiency is improved.

[0172] The present specification realizes multi-dimensional screening of vehicles and orders through dynamic geo-fencing, time window and tonnage matching, and solves the limitations of static route verification. Through multi-dimensional screening rules (region, time period, demand, compliance), the mixer truck is preliminarily screened, the candidate range is narrowed, and the matching efficiency is improved.

[0173] S23 verifies the transport capacity feasibility of the preliminarily screened mixer truck, and determines the dispatchable mixer truck according to the transport capacity feasibility verification result;

[0174] The transport capacity feasibility is verified by single task time consumption and unloading time length, so as to ensure that the vehicle can complete the task on time and avoid scheduling conflicts.

[0175] S231 calculates the single task time consumption of each preliminarily screened mixer truck;

[0176] S231-1 calculates the round trip time of each preliminarily screened mixer truck to the construction site location;

[0177] The round trip time includes: the going time .

[0178] S231-2 estimates the construction site unloading time and the mixing station delivery time ;

[0179] S231-3 combines the round trip time, the construction site unloading time and the mixing station delivery time , and calculates the single task time consumption of the preliminarily screened mixer truck;

[0180] The single task time consumption .

[0181] S232 finds the dispatchable mixer truck by combining the single task time consumption and the unloading time, and constructs a set of dispatchable mixer trucks ;

[0182] According to the unloading time period of the current order, the unloading time of the current order is calculated. That is, the unloading time is the difference of the time window of the unloading time period.

[0183] If the single task time consumption is less than or equal to the unloading time of the current order, the preliminarily screened mixer truck is used as the dispatchable mixer truck for participating in the scheduling of the order.

[0184] All dispatchable mixer trucks are summarized to construct a set of dispatchable mixer trucks .

[0185] . Among them, .

[0186] The specification dynamically calculates the availability of the mixer truck between the mixing plant and the vehicle leasing company through the capacity matching algorithm under multiple constraints, comprehensively considering factors such as road conditions, vehicle attributes, construction site locations, and time windows.

[0187] S3 optimally marks the dispatchable mixer trucks in the set of dispatchable mixer trucks according to the capacity matching result between the mixing plant and the vehicle leasing company.

[0188] S31 obtains basic information of the mixing plant.

[0189] The basic information of the mixing plant includes the mixing plant capacity .

[0190] S32 calculates the maximum departure quantity according to the mixing plant capacity and the number of dispatchable mixer trucks.

[0191] In an embodiment of the specification, the maximum departure quantity .

[0192] S33 retrieves the single-task time consumption of the dispatchable mixer truck .

[0193] S34 determines the capacity contribution degree of each dispatchable mixer truck according to the ratio of the maximum load of each dispatchable mixer truck to the single-task time consumption .

[0194] That is, the capacity contribution degree .

[0195] S35 aggregates the capacity contribution degree of each dispatchable mixer truck in the set of dispatchable mixer trucks to obtain the available capacity.

[0196] That is, the available capacity .

[0197] S36 outputs the capacity matching result between the mixing plant and the vehicle leasing company, and optimally marks the dispatchable mixer trucks.

[0198] In an embodiment of the specification, the dispatchable mixer trucks are arranged in descending order according to the capacity contribution degree; the dispatchable mixer trucks within a preset ranking range are optimally marked; the preset ranking range can be set in advance according to actual needs, for example, the top xx or the top x%.

[0199] Of course, the setting of the preset ranking range can also be based on the available capacity.

[0200] By prioritizing and selecting appropriate concrete mixer trucks based on their contribution to transportation capacity, the vehicle selection strategy can be optimized to improve the utilization efficiency of transportation capacity resources at the mixing plant.

[0201] S4 uses a departure sequence optimization model to allocate schedulable mixer trucks to all orders and generates a target scheduling scheme, which includes a sequence of several task orders.

[0202] Get order collection Order collection The corresponding production capacity of the mixing plant The set of dispatchable mixer trucks corresponding to each order The target scheduling scheme is obtained through the departure sequence optimization model, and tasks are scientifically allocated and constraints are verified to maximize capacity utilization and shorten scheduling time.

[0203] S41 divides the orders into batches according to the unloading time period;

[0204] In one embodiment of this specification, each order is marked with a set of valid time windows. Specifically, 24 hours are divided into 24 time windows, each window being 1 hour, denoted as... , ,..., .

[0205] For each order Mark its effective time window set Among them, orders The unloading period is fully included in the first Time window Inside. Right now The delivery time period is A subset of.

[0206] All orders within the same time window are grouped into one batch.

[0207] S42 determines the maximum number of dispatchable mixer trucks. ;

[0208] Retrieve the time taken for a single task of a dispatchable concrete mixer truck Treat it as a single cycle period ;

[0209] Based on a single cycle period Calculate the dispatchable mixer truck The theoretical maximum number of tasks that can be completed within 24 hours. (Round down); that is, .

[0210] S43 sorts each batch of orders;

[0211] The batches are arranged in ascending order according to time; the orders in the same batch are arranged in ascending order according to the unloading time (unloading start time); if the unloading start time is the same, the orders are sorted according to the priority of the orders; if the priority of the orders is the same, the orders are sorted according to the site location of the orders.

[0212] S44, based on the sorting result, sequentially determines, by using the dispatch sequence optimization model, a task order corresponding to each order.

[0213] The target scheduling scheme includes a sequence of task orders. The task order includes a mixer truck, an executed order, and an execution period (execution start time and execution end time).

[0214] The target scheduling scheme arranges the task orders according to the execution start time.

[0215] S441, by using the dispatch sequence optimization model, sequentially determines a task order corresponding to each order;

[0216] The dispatch sequence optimization model is a constrained optimization problem model. The dispatch sequence optimization model includes a constraint strategy. The constraint strategy includes a plurality of constraint conditions.

[0217] S441-1, from the set of schedulable mixer trucks corresponding to the order, allocates one or more schedulable mixer trucks to the order, to generate a plurality of allocation results;

[0218] S441-2, according to the constraint strategy, performs constraint verification on each allocation result, and selects a final allocation result;

[0219] In an embodiment of the present specification, the constraint strategy includes a demand coverage constraint, a vehicle load constraint, a mixing station capacity constraint, and a time window constraint.

[0220] The demand coverage constraint includes maximizing the utilization rate of the mixer truck, that is, ;

[0221] The vehicle load constraint includes that the total load of the mixer truck on all orders does not exceed the rated load of the mixer truck . That is, .

[0222] The mixing station capacity constraint includes that the number of the mixer truck does not exceed the maximum dispatch quantity . That is, .

[0223] The time window constraint includes that the total execution time of each mixer truck does not exceed 24 hours. Specifically, the actual execution times and the single task time consumption are multiplied . That is, .

[0224] wherein, is a 0-1 variable, used to represent whether the mixer truck executes the order If the mixer truck executes the order , then , otherwise 0.

[0225] If the assignment result satisfies all the constraint conditions, it is determined that the constraint check of the assignment result is passed.

[0226] The assignment result that passes the constraint check is obtained, if the assignment result that passes the constraint check is one, the assignment result is taken as the final assignment result; if the assignment result that passes the constraint check is multiple, the assignment result with the most preferred marks is selected as the final assignment result.

[0227] If the assignment result does not satisfy all the constraint conditions, it is determined that the order is a key order;

[0228] The order is reordered; the key order is placed at the top, and step S44 is re-executed.

[0229] S441-3 takes the schedulable mixer truck involved in the final assignment result as a candidate mixer truck; a task sheet is established for each candidate mixer truck;

[0230] S441-4 assigns an execution time for each task sheet; the execution time meets the valid window of the order and the cycle period of the mixer truck.

[0231] S442 sorts the task sheets in ascending order according to the execution time, and obtains a target scheduling scheme.

[0232] The present specification generates a vehicle cycle scheduling plan based on time window and capacity constraints, maximizes the utilization rate of transport capacity. By reducing the idle rate of vehicles, the scheduling time is shortened to the minute level to improve efficiency; by reducing the empty running rate of the leasing company, the order satisfaction rate of the mixing station is improved, and cost optimization is realized.

[0233] In another embodiment of the present specification, a set of schedulable vehicles is pushed to the user (the team leader), and the user selects a candidate mixer truck from the schedulable mixer truck according to the transport capacity and the recommendation result.

[0234] The present specification performs transport capacity matching based on road permit matching and vehicle screening, optimizes the departure sequence under the constraints of time window and capacity, and realizes plan scheduling according to steps S1-S4. In order to improve the scheduling effect, when the task sheet is actually executed, real-time data sensing and intelligent decision-making are used to dynamically adjust the task sheet to deal with unexpected situations. Specifically:

[0235] S5 determines a target mixer truck based on the target scheduling scheme and / or the selection instruction of the user;

[0236] In an embodiment of the present specification, all orders of the current batch are acquired; a candidate mixer truck with the same execution time as the current time is found, which is taken as the target mixer truck.

[0237] In another embodiment of the present specification, the target mixer truck is determined by the user according to the transport capacity and the recommendation result.

[0238] The target mixer truck is the mixer truck that is actually successfully dispatched.

[0239] The driver of the target mixer truck signs in to obtain an electronic road permit and navigation. Based on the price corresponding to the target mixer truck, the rental fee is automatically calculated to realize quick settlement of the fee. Based on the actual use of the target mixer truck, a transport capacity analysis report is generated.

[0240] If the order is changed or the vehicle fails, local re-planning is triggered, the schedulable mixer truck that meets the constraint strategy is found in the schedulable vehicle set, and the replacement of the candidate mixer truck / target mixer truck is performed.

[0241] S6 acquires the current task order and unexecuted task order of the target mixer truck;

[0242] The task order of the target mixer truck is acquired as the current task order;

[0243] S7 combines the actual state data to predict the travel time and unloading time;

[0244] The actual state data includes: real-time position of the target mixer truck, actual road condition data, actual vehicle condition data, actual vehicle model, actual equipment data and actual weather data.

[0245] S71 combines the real-time position of the target mixer truck, the actual road condition data, the actual vehicle condition data and the actual vehicle model to obtain the basic time consumption of the target mixer truck;

[0246] S711 obtains ideal travel time consumption according to the average design speed and the real-time position;

[0247] S711-1 calculates the actual road distance according to the real-time position of the target mixer truck and the site position of the current task order ;

[0248] S711-2 acquires the average design speed ;

[0249] The average design speed is pre-set.

[0250] S711-3 obtains the actual road distance and the average design speed The ratio of the ideal driving time is used as the ideal driving time. That is, the ideal driving time. .

[0251] In one embodiment of this specification, the actual road distance of the planned path from the current location to the target location of the concrete mixer truck can be obtained by connecting to a map API. (Unit: km); Obtain the average design speed of this road segment or type of road (such as urban expressways, main roads, and construction access roads) under ideal (smooth traffic) conditions. (Unit: km / h), calculate ideal driving time.

[0252] S712 calculates dynamic impact factors by combining actual road condition data and actual vehicle condition data;

[0253] S712-1 acquires actual road condition data and actual vehicle condition data;

[0254] Actual road condition data includes: the number of emergency braking incidents within the preset monitoring range. Emergency braking is defined as an event in which the acceleration exceeds a preset threshold.

[0255] Actual vehicle condition data includes: the maximum abnormal tank rotation speed within the preset monitoring range. Mr. Qi, an abnormal value is defined as the rotation speed continuously exceeding the preset safe range or fluctuating drastically (high standard deviation).

[0256] The preset monitoring range can be a preset time range (near). The time period (or distance range, within N kilometers) is not specifically limited here.

[0257] S712-2 calculates the emergency braking impact factor based on actual road condition data;

[0258] Emergency braking influencing factors ;in, The emergency braking impact coefficient represents the percentage increase in time caused by each emergency braking action. It represents the number of emergency braking operations within the preset monitoring range; This is a preset upper limit for the impact of sudden braking, used to prevent excessive influence from a single indicator.

[0259] Specifically, based on the number of emergency braking operations. And the impact coefficient of emergency braking The accumulation of the impact index of emergency braking ; Obtain the preset upper limit of the impact of emergency braking ; Sudden braking will affect the base number Upper limit of impact of emergency braking The smaller result is used as the impact factor for emergency braking.

[0260] S712-3 calculates the rotation speed influence factor according to the actual vehicle condition data;

[0261] acquires the tank rotation speed information within the preset monitoring range; the tank rotation speed information includes but is not limited to: the standard deviation of the tank rotation speed , the average value of the tank rotation speed , and the rated tank rotation speed .

[0262] calculates the rotation speed influence coefficient according to the tank rotation speed information; in an embodiment of the present specification, the ratio of the standard deviation of the tank rotation speed to the rated tank rotation speed is taken as the rotation speed influence coefficient. In another embodiment of the present specification, the absolute difference between the average value of the tank rotation speed and the rated tank rotation speed is calculated; and the ratio of the absolute difference to the rated tank rotation speed is taken as the rotation speed influence coefficient.

[0263] takes the product of the rotation speed influence coefficient and the rotation speed anomaly influence coefficient as the rotation speed influence index ; acquires the preset rotation speed influence upper limit ; takes the smaller result between the rotation speed influence index and the rotation speed influence upper limit as the rotation speed influence factor.

[0264] The present application calculates the rotation speed influence factor based on the standard deviation of the tank rotation speed within the preset monitoring range (near M minutes / M kilometers) or the deviation thereof from the rated rotation speed . In an embodiment of the present specification, the rotation speed influence factor ; in another embodiment of the present specification, the rotation speed influence factor .

[0265] wherein, is the preset rotation speed anomaly influence coefficient; is the standard deviation of the tank rotation speed within the preset monitoring range; is the average value of the tank rotation speed within the preset monitoring range; is the rated tank rotation speed (smooth rotation value) corresponding to the actual vehicle type in the transportation state; characterizes the tank rotation speed anomaly influence upper limit (such as 0.3).

[0266] S712-4 calculates the dynamic influence factor by combining the sudden braking influence factor and the rotation speed influence factor;

[0267] The dynamic impact factor incorporates the number of emergency braking incidents and abnormal tank rotation speeds. Based on the amplification effect on travel time, the dynamic impact factor is ≥1.

[0268] Determine the emergency braking correction coefficient based on the emergency braking impact factor. The speed correction coefficient is determined based on the speed influence factor. ;in, , The weighting coefficients are preset. Considering that emergency braking more directly reflects road congestion / danger and driving behavior, and usually has a greater impact on braking time, therefore, they are preferred. .

[0269] The product of the emergency braking correction factor and the speed correction factor is used as the dynamic influence factor. That is, the dynamic influence factor. .

[0270] In the process of calculating dynamic impact factors, The function prevents the factor from being too large. The multiplicative relationship reflects that sudden braking and tank malfunctions may coexist and influence each other (e.g., a bumpy road surface causing tank malfunctions may also be accompanied by sudden braking). The form guarantees That is, in the absence of abnormalities (i.e. , )hour, =1.

[0271] S713 is based on static influence factors associated with actual vehicle models;

[0272] Different vehicle types (such as 8-cubic-meter, 10-cubic-meter, and 12-cubic-meter trucks) vary in acceleration performance, turning agility, and weight, affecting travel time in complex road conditions. Therefore, a corresponding vehicle type coefficient is associated with each vehicle type. This vehicle type coefficient is a preset value based on a vehicle type ID mapping, and it must be ≥1.

[0273] In one embodiment of this specification, the vehicle coefficient associated with small, flexible vehicles (e.g., 8 cubic meters) is 1.0; the vehicle coefficient associated with medium-sized vehicles (e.g., 10 cubic meters) is 1.1; and the vehicle coefficient associated with large vehicles (e.g., 12 cubic meters) is 1.2-1.3 (large turning radius, slow start, and longer travel time in congested or narrow roads). The vehicle coefficient can be set / adjusted based on actual needs.

[0274] Find the corresponding vehicle model coefficient based on the actual vehicle model; use the vehicle model coefficient as a static influencing factor. .

[0275] S714 calculates the basic travel time by combining ideal driving time, dynamic influence factor, and static influence factor.

[0276] The ideal driving time consumption , dynamic influence factor , static influence factor The product of the above is the basic time consumption. That is, .

[0277] S72 combines actual equipment data and actual weather data to calculate the risk compensation parameter;

[0278] S721 calculates the health degree coefficient according to the actual equipment data;

[0279] The actual equipment data includes: the abnormal rate of the key equipment , the metal fatigue index of the key equipment , the corrosion index of the key equipment .

[0280] The key equipment is a designated key sensor.

[0281] Wherein, the abnormal rate is the number of abnormal alarm times in the last 24 hours / total number of key equipment.

[0282] The metal fatigue index ; wherein, is a preset attenuation coefficient.

[0283] The corrosion index If , set 1.

[0284] The health degree coefficient ; wherein, , , is a weighting coefficient, .

[0285] The present application predicts the health degree coefficient by fusing three dimensions of real-time failure rate (abnormal rate), historical loss (metal fatigue index), and structural damage (corrosion index). , the closer to 0 indicates that the equipment state is worse, that is, higher risk compensation is needed.

[0286] In one embodiment of the present application, ; ; .

[0287] S722 calculates the weather coefficient according to the actual weather data;

[0288] The weather coefficient .

[0289] Wherein, is the precipitation intensity factor; as preferred, when there is no rain, ; light rain, ; moderate rain, ; heavy rain, .

[0290] is a temperature deviation coefficient; is a wind speed (m / s), which reduces the linear effect of high wind speed intervals by square root.

[0291] is a weight coefficient, specifically, is a precipitation weight coefficient; is a temperature weight coefficient; is a strong wind weight coefficient. Among them, . As preferred, default , default , default .

[0292] weather coefficient , the larger the value, the worse the weather.

[0293] The weather coefficient of the present application quantifies the combined effects of precipitation (slip risk), temperature (concrete setting speed), and strong wind (vehicle stability).

[0294] S723 combines the health coefficient and the weather coefficient to calculate the risk compensation parameter;

[0295] risk compensation parameter .

[0296] S73 predicts the journey time based on the base time consumption and the risk compensation parameter;

[0297] journey time .

[0298] The present specification dynamically adjusts the departure sequence by obtaining vehicle GPS data to adapt to dynamic construction scenes. Moreover, the outbound time and the return time of the target mixer truck are dynamically adjusted in combination with real-time traffic API, actual vehicle condition data, and actual weather data.

[0299] S74 predicts the unloading time consumption based on actual transportation data;

[0300] S741 constructs and trains an unloading time prediction model;

[0301] The present specification constructs an unloading time prediction model based on historical unloading data of the construction site (such as the unloading duration of different grade concrete).

[0302] S741-1 obtains historical unloading data to construct training data;

[0303] The historical unloading data comprises historical unloading information.

[0304] The historical unloading information comprises, but is not limited to, category information and unloading duration.

[0305] The category information comprises, but is not limited to, concrete grade (such as C30, C40), weather type (sunny / rainy / snowy), equipment type (pump truck / ground pump), and construction site type (high-rise engineering / foundation engineering).

[0306] The historical unloading information can be grouped according to the category information; preferably, the historical unloading information can be grouped according to one or more of the concrete grade, the weather type, the equipment type, and the construction site type.

[0307] The unloading duration average and the standard deviation are calculated for the historical unloading information in the same group.

[0308] In an embodiment of the present specification, the historical unloading information of the same concrete grade, the same weather type, the same equipment type, and the same construction site type is obtained, and the unloading result data is calculated. The unloading result data comprises the average unloading duration and the standard deviation.

[0309] The category information in the historical unloading data and the unloading result data are taken as a training data.

[0310] S741-2, a unloading time prediction model is constructed;

[0311] The unloading time prediction model is preferably a random forest regression model.

[0312] S741-3, the unloading time prediction model is trained using the training data;

[0313] The category information in the training data is taken as an input, and the unloading duration is predicted .

[0314] The unloading duration , wherein, is a tree weight, is the number of trees.

[0315] S742, real-time transportation data is obtained;

[0316] The real-time transportation data comprises the concrete grade, the construction site type, and the real-time weather data (such as rain / snow / temperature) of the current order. The real-time transportation data is standardized.

[0317] S743, the real-time transportation data is input into the unloading time prediction model, and the predicted unloading duration is obtained.

[0318] Based on historical data, a delivery time prediction model is constructed to replace the fixed time assumption, improving scheduling accuracy.

[0319] If the predicted value deviates from the historical average (delivery result data) by more than 20%, manual review is triggered (such as extreme weather or new site type).

[0320] In one specific application scenario of the present specification, due to heavy rain, the delivery time of site B is prolonged, and the system dynamically extends the task time window and re-plans the vehicle path.

[0321] By dynamically predicting the travel time and unloading time and adjusting the task time, the real-time road conditions and changes in road conditions are adapted, enhancing the flexibility and accuracy of the scheduling scheme.

[0322] S8 aggregates the travel time and unloading time, updates the execution end time of the current task sheet and the execution start time of the unexecuted task sheet;

[0323] In one embodiment of the present specification, for the current task sheet, the target mixer truck current time (or the last task end time) is taken as the starting point, and the travel time + unloading time is added to obtain the new execution end time.

[0324] For unexecuted task sheets, in the order of sorting, the execution end time of the previous task sheet is taken as the starting point, and the travel time (from the previous site to the current site) + unloading time of the current task is added in sequence to generate the new execution start time.

[0325] By aggregating the travel time and unloading time to update the task execution time, it ensures that the task sequence and time connection are reasonable (the next task starts after the previous task ends), avoiding conflicts.

[0326] S9 adjusts the execution time or execution order of the task sheet according to the mixing station state data and order scheduling data;

[0327] By combining the mixing station state and order urgency to adjust the task order, the global scheduling logic is optimized to ensure that high-priority orders are completed in time and reduce production waste.

[0328] S91 adjusts the order of the unexecuted task sheet in combination with the mixing station state data and the urgency of the order;

[0329] S911 obtains mixing station state data;

[0330] The mixing station state data includes: mixing station equipment state (such as whether the mixer is malfunctioning), raw material inventory, and real-time production rate (tons / hour).

[0331] S912 calculates scheduling feasibility according to the mixing station state data;

[0332] Scheduling feasibility

[0333] S913 Adjust the order of the task orders of the same batch according to the urgency of the orders.

[0334] If the difference between the current time and the pouring deadline is less than the critical threshold, it is determined that the order is close to the pouring deadline, and the urgency of the order is high. The task order corresponding to the order with high urgency is marked with high priority.

[0335] S914 Adjust the order of the task orders of the same batch in combination with the scheduling feasibility and the urgency of the orders.

[0336] If equipment failure leads to insufficient production capacity, automatically trigger the backup mixing station call or prioritize processing of emergency orders.

[0337] Adjust the task order according to the mixing station status (such as mixer failure) and the order urgency (such as the proximity of the pouring deadline), prioritize processing of emergency orders, and reduce the risk of pouring delay.

[0338] S92 Calculate the site location concentration according to the site location of the task orders, and adjust the order of the unexecuted task orders according to the site location concentration.

[0339] Obtain the site locations of the task orders of the same batch, and determine the site location concentration of each task order.

[0340] If the site location concentration is high, the task orders with high site location concentration are marked with the same association mark. Find the earliest execution time of the task orders with the same association mark. Adjust the execution time of the task orders with the same association mark to the earliest execution time to realize batch execution of the task orders with high concentration.

[0341] Adjust the task order according to the site location concentration, batch execute concentrated tasks (such as multiple sites in the same area), reduce empty running rate, and improve transportation efficiency.

[0342] S93 Predict the schedulable capacity of the mixing station according to the mixing station status data: adjust the execution time of the unexecuted task orders according to the decline rate of the schedulable capacity.

[0343] S931 Predict the schedulable capacity of the mixing station according to the mixing station status data:

[0344] Schedulable capacity = equipment normal rate × raw material inventory × real-time production rate.

[0345] Equipment normal rate = 1 - number of failed equipment / total number of equipment.

[0346] The raw material inventory needs to meet at least 120% of the required raw materials for completing the current order (buffer threshold). Among them, the order scheduling data is obtained, and the order scheduling data includes: the total amount of unexecuted task orders, the demand time window. According to the order scheduling data, the required raw materials for completing the order are predicted.

[0347] S932 adjusts the execution time of the task order according to the decline ratio of the schedulable capacity;

[0348] Every interval, calculate the current schedulable capacity ;

[0349] The difference between the last schedulable capacity and the current schedulable capacity is taken as the current capacity difference - ;

[0350] The ratio of the current capacity difference to the last schedulable capacity is taken as the decline ratio of the schedulable capacity, that is, the decline ratio of the schedulable capacity ;

[0351] If the decline ratio of the schedulable capacity is greater than the preset warning threshold, a warning is triggered and new order allocation is suspended. The preset warning threshold is preferably 20%.

[0352] In an embodiment of the present specification, the sensor data is polled every 5 minutes, and the schedulable capacity is updated. If the schedulable capacity decreases by more than 20%, a warning is triggered and new order allocation is suspended. The present specification dynamically adjusts the mixing station capacity constraint through real-time sensor data, solving the limitations of static capacity preset.

[0353] In a specific application scenario of the present specification, a mixing station A has a 30% decrease in capacity due to sudden equipment failure, and the system automatically adjusts its task allocation and prioritizes scheduling vehicles with road certificates nearby.

[0354] Adjust the task time according to the decline ratio of the mixing station capacity (such as capacity reduction due to equipment failure), avoid overloading scheduling, and ensure production stability (such as suspending new order allocation).

[0355] The present specification considers real-time production capacity fluctuation and demand uncertainty of the mixing station, and the vehicle leasing company cannot obtain effective information in time, resulting in insufficient utilization of vehicles. The present specification dynamically adjusts the scheduling scheme by combining real-time traffic, order changes, and vehicle state data, and schedules the concrete mixer truck through "dynamic time limit verification + real-time regulation data fusion + equipment health monitoring". Among them, combined with road certificate constraints, real-time orders, vehicle states, and construction site weights, scheduling is performed, and 24-hour demand automation generation and real-time adjustment of the mixing station can be realized to solve the problem of waste of transport capacity or insufficient utilization of dispatched vehicles caused by information asymmetry between the mixing station and the leasing company.

[0356] Figure 3 A structural diagram of a scheduling system of a concrete mixer truck is provided for the embodiments of the present specification, and the system comprises:

[0357] An information acquisition module 301 is configured to acquire an order set and a vehicle set of a mixing station;

[0358] A vehicle screening module 302 is configured to screen a schedulable mixer truck corresponding to an order from the vehicle set;

[0359] A scheduling and distribution module 304 is configured to distribute schedulable mixer trucks for all orders through a departure sequence optimization model to generate a target scheduling scheme, wherein the target scheduling scheme comprises a sequence of a plurality of task sheets.

[0360] Optionally, the vehicle screening module 302 comprises:

[0361] An order positioning sub-module is configured to acquire a current order from the order set;

[0362] A rule construction sub-module is configured to construct a screening rule of the current order according to order information of the current order;

[0363] A rule judgment sub-module is configured to judge whether an available mixer truck in the vehicle set meets the screening rule;

[0364] A first screening sub-module is configured to identify the available mixer truck as a primary screening qualified mixer truck if the available mixer truck meets all the screening rules;

[0365] A second screening sub-module is configured to perform a transport capacity feasibility verification on the primary screening qualified mixer truck, and determine the schedulable mixer truck according to the transport capacity feasibility verification result;

[0366] Optionally, the rule construction sub-module comprises:

[0367] A first rule construction unit is configured to construct a regional screening rule of the current order according to order information of the current order;

[0368] a second rule construction unit configured to construct a time period screening rule of the current order according to order information of the current order;

[0369] a third rule construction unit configured to construct a demand screening rule of the current order according to order information of the current order;

[0370] a fourth rule construction unit configured to construct a compliance screening rule of the current order according to order information of the current order.

[0371] Optionally, the scheduling and distribution module 304 comprises:

[0372] a batch division sub-module configured to divide the orders into batches according to unloading time periods;

[0373] a sorting sub-module configured to sort the orders in each batch;

[0374] a distribution sub-module configured to determine a task order corresponding to each order in sequence based on the sorting result by using the dispatch sequence optimization model.

[0375] Optionally, the distribution sub-module comprises:

[0376] an initial distribution unit configured to distribute one or more schedulable mixers to the orders from a schedulable mixer set corresponding to the orders to generate a plurality of distribution results;

[0377] a final distribution unit configured to perform constraint verification on each distribution result according to a constraint strategy to filter out a final distribution result;

[0378] a task order construction unit configured to take schedulable mixers involved in the final distribution result as candidate mixers, and establish a task order for each candidate mixer;

[0379] a time distribution unit configured to distribute an execution time for each task order; the execution time conforms to an effective window of the order and a cycle period of the mixer.

[0380] Optionally, the scheduling and distribution module 304 further comprises:

[0381] a designation module configured to determine a target mixer based on the target scheduling scheme and / or a selection instruction of a user;

[0382] a task order acquisition module configured to acquire a current task order and an unexecuted task order of the target mixer;

[0383] a time consumption prediction module configured to predict a route time consumption and an unloading time consumption in combination with actual state data;

[0384] A real-time updating module is configured to aggregate the travel time and the unloading time, update the end time of execution of the current task sheet, and update the start time of execution of the unexecuted task sheet.

[0385] Optionally, a time consumption prediction module includes a travel time consumption submodule and an unloading time consumption submodule.

[0386] The travel time consumption submodule includes:

[0387] A basic time consumption calculation unit is configured to obtain a basic time consumption of the target mixer truck in combination with real-time position of the target mixer truck, actual road condition data, actual vehicle condition data, and actual vehicle model.

[0388] A risk compensation parameter calculation unit is configured to calculate a risk compensation parameter in combination with actual equipment data and actual weather data.

[0389] A travel time consumption prediction unit is configured to predict a travel time consumption according to the basic time consumption and the risk compensation parameter.

[0390] The unloading time consumption submodule is configured to predict an unloading time consumption based on actual transportation data.

[0391] Optionally, the basic time consumption calculation unit includes:

[0392] An ideal travel time consumption calculation subunit is configured to obtain an ideal travel time consumption according to an average design speed and real-time position.

[0393] A dynamic influence factor calculation subunit is configured to calculate a dynamic influence factor in combination with actual road condition data and actual vehicle condition data. Specifically, an emergency braking influence factor is calculated according to the actual road condition data, a rotation speed influence factor is calculated according to the actual vehicle condition data, and the dynamic influence factor is calculated in combination with the emergency braking influence factor and the rotation speed influence factor, wherein the dynamic influence factor is a preset weighting coefficient.

[0394] A static influence factor calculation subunit is configured to associate a static influence factor according to an actual vehicle model.

[0395] A basic time consumption calculation subunit is configured to take a product of the ideal travel time consumption, the dynamic influence factor, and the static influence factor as the basic time consumption.

[0396] Optionally, the system further includes a real-time scheduling module.

[0397] The real-time scheduling module includes:

[0398] A first scheduling submodule is configured to sequentially adjust the unexecuted task sheet in combination with the mixing station state data and an urgency level of the order; and / or, ​​

[0399] a second scheduling sub-module configured to calculate a site location concentration according to site locations of the task sheets, and sequentially adjust the unexecuted task sheets according to the site location concentration; and / or,

[0400] a third scheduling sub-module configured to predict a schedulable capacity of the mixing station according to the mixing station state data, and adjust execution times of the unexecuted task sheets according to a descending ratio of the schedulable capacity.

[0401] The functions of the system of the embodiments of the present application have been described in the above-mentioned method embodiments, and thus the description of the present embodiments will not be elaborated on the unexplained parts, which can be referred to the relevant description in the foregoing embodiments.

[0402] Figure 4 The present application provides a structural schematic diagram of an electronic device, which comprises a memory 401 and a processor 402, the memory 401 is used for storing computer executable instructions, and the computer executable instructions can realize the steps of the above-mentioned method embodiments when executed by the processor 402.

[0403] Figure 5 The present application provides a structural schematic diagram of a computer readable storage medium, which stores one or more computer programs, and the one or more computer programs can realize the steps of the above-mentioned method embodiments when executed by a processor.

[0404] The present application also provides a computer program product, which comprises computer programs / computer executable instructions, and the computer programs / computer executable instructions can realize the steps of the above-mentioned method embodiments when executed by a processor.

[0405] Those skilled in the art can understand that all or part of the processes in the above-mentioned method embodiments can be completed by a computer program instructing related hardware, and the computer program can include the processes of the above-mentioned method embodiments when executed.

[0406] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A method for dispatching concrete mixer trucks, characterized in that, include: Obtain the order set and vehicle set of the mixing plant; Select the dispatchable mixer trucks corresponding to the orders from the vehicle set; The transport feasibility of the initially qualified mixer trucks is verified by the time taken for a single task and the unloading time. The dispatchable mixer trucks are determined based on the transport feasibility verification results. The dispatch sequence optimization model is used to allocate schedulable mixer trucks to all orders, generating a target scheduling scheme, which includes a sequence of several task orders. Based on the target scheduling scheme and / or the user's selection instructions, determine the target mixer truck; obtain the current task order and unexecuted task orders of the target mixer truck; Based on actual status data, predict travel time and unloading time to update the execution end time of the current task order and the execution start time of the unexecuted task orders; The prediction of travel time and unloading time based on actual status data includes: The ideal travel time is determined by the ratio of the actual road distance to the average design speed. The impact factor of emergency braking is calculated based on actual road condition data. ,in, This is the impact coefficient of emergency braking; It represents the number of emergency braking operations within the preset monitoring range; This is a preset upper limit for the impact of sudden braking, used to prevent excessive influence from a single indicator; Obtain tank rotation speed information within a preset monitoring range; calculate the rotation speed influence coefficient based on the tank rotation speed information; use the product of the rotation speed influence coefficient and the rotation speed anomaly influence coefficient as the rotation speed influence index; use the result that is smaller between the rotation speed influence index and the upper limit of the rotation speed influence as the rotation speed influence factor. ; Calculate the dynamic influence factor by combining the impact factors of emergency braking and speed. ; ; , These are the preset weighting coefficients; Based on the actual vehicle model and associated static influence factors, the product of ideal driving time, dynamic influence factors, and static influence factors is used as the base driving time. ; Health coefficient calculated based on actual equipment data. ; ;in, For the anomaly rate, This is the metal fatigue index. Corrosion index, , , These are weighting coefficients. ; Calculate the weather coefficient based on actual weather data. ;in, Precipitation intensity factor; This is the temperature deviation coefficient; Wind speed (m / s); These are the weighting coefficients. ; Calculate risk compensation parameters by combining health index and weather index. ; Predict travel time based on baseline travel time and risk compensation parameters. ; ; Predict unloading time based on actual transportation data.

2. The method for dispatching concrete mixer trucks as described in claim 1, characterized in that, The step of selecting dispatchable mixer trucks corresponding to the order from the vehicle set includes: Retrieve the current order from the order set; The current order is filtered based on its order information; wherein, a geographic filtering rule is constructed based on the current order's order information; and / or, a time-based filtering rule is constructed based on the current order's order information; and / or, a demand filtering rule is constructed based on the current order's order information; and / or, a compliance filtering rule is constructed based on the current order's order information. Determine whether the available mixer trucks in the vehicle set meet the screening rules; If the available mixer truck meets all the screening rules, then the available mixer truck is considered to be a qualified mixer truck in the initial screening.

3. The method for dispatching concrete mixer trucks as described in claim 1, characterized in that, The process of allocating schedulable mixer trucks to all orders using a departure sequence optimization model to generate a target scheduling scheme includes: The orders are divided into batches according to the unloading time period; Sort each batch of orders; Based on the sorting results, the task order corresponding to each order is determined sequentially using the departure sequence optimization model.

4. The method for dispatching concrete mixer trucks as described in claim 1, characterized in that, The step of determining the task order corresponding to each order sequentially through the departure sequence optimization model based on the sorting results includes: From the set of available mixer trucks corresponding to the order, allocate one or more available mixer trucks to the order, and generate several allocation results; Each allocation result is constrained and validated according to the constraint strategy, and the final allocation result is selected. The schedulable mixer trucks involved in the final allocation results are used as candidate mixer trucks; a task order is created for each candidate mixer truck. An execution time is allocated to each task order; the execution time conforms to the order's valid window and the mixer truck's cycle time.

5. The method for dispatching concrete mixer trucks as described in claim 1, characterized in that, The prediction of unloading time based on actual transportation data includes: Acquire historical unloading data and construct training data; historical unloading data includes: several historical unloading information entries; historical unloading information includes, but is not limited to: category information and unloading duration; A model for predicting unloading time is trained using training data; specifically, the category information in the training data is used as input to predict the unloading duration. Unloading time ;in, For tree weights, The number of trees; Obtain real-time transportation data; Real-time transportation data is input into the unloading time prediction model to obtain the predicted unloading time. .

6. The method for dispatching concrete mixer trucks as described in claim 1, characterized in that, Also includes: The order of unexecuted task orders is adjusted based on the batching plant status data and the urgency of the orders; And / or, Calculate the site location concentration based on the site locations in the task orders, and adjust the order of the unexecuted task orders according to the site location concentration; and / or, Predict the dispatchable capacity of the mixing plant based on the status data of the mixing plant; adjust the execution time of the unexecuted task orders according to the decrease rate of the dispatchable capacity.

7. The method for dispatching concrete mixer trucks as described in claim 6, characterized in that, The step of adjusting the order of unexecuted task orders by combining the batching plant status data and the urgency of the orders includes: Obtain the status data of the mixing plant; Calculate scheduling feasibility based on the status data of the mixing plant; Scheduling feasibility = ; The order of tasks within the same batch is adjusted according to their urgency. The order of tasks in the same batch is adjusted based on scheduling feasibility and the urgency of the orders.

8. A dispatching system for concrete mixer trucks, characterized in that, include: The information acquisition module is used to acquire the order set and vehicle set of the mixing plant; The vehicle screening module is used to screen out dispatchable mixer trucks corresponding to the orders from the vehicle set; The transport feasibility of the initially qualified mixer trucks is verified by the time taken for a single task and the unloading time. The dispatchable mixer trucks are determined based on the transport feasibility verification results. The scheduling and allocation module is used to allocate schedulable mixer trucks to all orders through a departure sequence optimization model and generate a target scheduling scheme, which includes a sequence of several task orders. Based on the target scheduling scheme and / or the user's selection instructions, determine the target mixer truck; obtain the current task order and unexecuted task orders of the target mixer truck; Based on actual status data, predict travel time and unloading time to update the execution end time of the current task order and the execution start time of the unexecuted task orders; The prediction of travel time and unloading time based on actual status data includes: The ideal travel time is determined by the ratio of the actual road distance to the average design speed. The impact factor of emergency braking is calculated based on actual road condition data. ,in, This is the impact coefficient of emergency braking; It represents the number of emergency braking operations within the preset monitoring range; This is a preset upper limit for the impact of sudden braking, used to prevent excessive influence from a single indicator; Obtain tank rotation speed information within a preset monitoring range; calculate the rotation speed influence coefficient based on the tank rotation speed information; use the product of the rotation speed influence coefficient and the rotation speed anomaly influence coefficient as the rotation speed influence index; use the result that is smaller between the rotation speed influence index and the upper limit of the rotation speed influence as the rotation speed influence factor. ; Calculate the dynamic influence factor by combining the impact factors of emergency braking and speed. ; ; , These are the preset weighting coefficients; Based on the actual vehicle model and associated static influence factors, the product of ideal driving time, dynamic influence factors, and static influence factors is used as the base driving time. ; Health coefficient calculated based on actual equipment data. ; ;in, For the anomaly rate, This is the metal fatigue index. Corrosion index, , , These are weighting coefficients. ; Calculate the weather coefficient based on actual weather data. ;in, Precipitation intensity factor; This is the temperature deviation coefficient; Wind speed (m / s); These are the weighting coefficients. ; Calculate risk compensation parameters by combining health index and weather index. ; Predict travel time based on baseline travel time and risk compensation parameters. ; ; Predict unloading time based on actual transportation data.

9. An electronic device, wherein, The electronic device includes: Processor; and, A memory storing computer-executable instructions, which, when executed, cause the processor to perform the method according to any one of claims 1-7.

10. A computer-readable storage medium, wherein, The computer-readable storage medium stores one or more programs that, when executed by a processor, implement the method of any one of claims 1-7.

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