CN112418550A - Vehicle and goods matching method under various driver working states in logistics industry - Google Patents

Abstract

The invention provides a vehicle and goods matching method under various driver working states in the logistics industry, which can be used for screening vehicles, establishing vehicle role channels, classifying vehicles, calculating vehicle matching rate, sequencing vehicles and distributing multi-role vehicles according to the distribution rate among a plurality of drivers according to order information, thereby finding drivers meeting the order service and finally finishing vehicle dispatching. The matching method can meet the requirement of matching the best driver in accordance with the service judgment standard according to the service requirements under the condition of multiple driver roles, can also carry out role division and dispatching inclination according to service scenes, has larger universality and expansibility, and greatly improves the vehicle and goods matching efficiency in the logistics industry.

Description

Vehicle and goods matching method under various driver working states in logistics industry

Technical Field

The invention relates to the field of big data analysis, in particular to a vehicle and goods matching method under various driver working states in the logistics industry.

Background

With the rapid development of logistics industry in recent years, the market conditions that goods cannot be found in vehicles and goods cannot be found in vehicles are ubiquitous due to the fact that road transportation has serious resource waste, and therefore a plurality of internet vehicle and goods platforms are born, the goods sources and the vehicle source information are put on the platforms, matching of the vehicles and the goods sources is carried out through matching rules of the platforms, and the purpose that the goods can be found in the vehicles and the goods can be found in the vehicles is achieved.

In actual platform operation, the platform often classifies vehicle sources and goods sources according to the actual conditions of the service of the platform, the vehicle sources and the goods sources with various roles exist, and purposeful resource inclination is carried out in the vehicle-goods matching process, so that the vehicle-goods matching becomes complicated and important. There is a great need for those skilled in the art to solve the corresponding technical problems.

Disclosure of Invention

The invention aims to at least solve the technical problems in the prior art, and particularly creatively provides a vehicle and goods matching method under the working states of various drivers in the logistics industry.

In order to achieve the above purpose, the invention provides a vehicle and goods matching method under various driver working states in the logistics industry, which comprises the following steps:

s1, establishing role channels and classifying the roles through known configuration roles; vehicle role types, screening distances and weights occupied by different factors;

s2, calculating vehicle matching values in a single channel through preset parameters, sorting, and selecting a maximum value;

s3, selecting vehicles according to preset dispatch proportions of working years, driving license grades and accident rates of different drivers and proportion probabilities;

and S4, calculating and sequencing the vehicle matching scores, and performing directional order dispatching according to the proportion of the orders configured by the vehicle identities.

Preferably, the S1 includes:

s1-1, dividing the channels of the vehicles selected by the distance configuration, the vehicle requirements and the carrying state requirements according to the identity of the vehicles, and creating role channels meeting the working states of all drivers, namely one channel represents the vehicle of one role;

and S1-2, traversing all the screened driver working states in the step S1-1, corresponding the working years, the driving license grades and the accident rate of the drivers in the driver working states to the role channels reestablished in the step S1-1, and finishing the classification of the drivers according to the role channels.

Preferably, the S2 includes:

all vehicles are screened by configuring a parameter 'distance', and the vehicles meeting the area are screened by taking the longitude and latitude of a delivery place as the center of a circle and the configured distance as the radius.

Screening the screened vehicle type in the order for the second time, and screening the vehicle type required in the order;

and screening the vehicles meeting the distance and vehicle type conditions for the third time, and screening out the idle vehicles according to the carrying states of the vehicles.

Preferably, the S3 includes:

dividing channels of the screened vehicles according to the identities of the vehicles, creating channels meeting all the roles of drivers, and enabling one channel to represent the vehicle with one role; the driver roles are classified by lane.

Preferably, the S4 includes:

the full score of the vehicle matching value is 100, the weights occupied by different factors can be set through self configuration, and the sum of all the weights needs to be equal to the value 1.

Calculating the score of each driver working state factor according to a vehicle weight calculation rule, wherein each driver working state factor is subjected to matching calculation according to a specific service scene, and the single factor is divided into 100 vehicle weights;

accumulating the scores obtained under all the factors to obtain a matching value of the final vehicle according to the order;

and sequencing the vehicles in each channel according to the final matching value, so as to obtain the maximum value of the matching value in each channel.

Preferably, the S4 further includes:

and (3) making a list according to the working years, the driving license grades and the accident rate of the driver in the working state of the driver: the directional order dispatching is carried out according to the proportion of the order dispatched by the vehicle identity in the configuration, the configured proportion is a probability value, and the condition that the sum of the proportions of the orders dispatched by different vehicle roles is equal to a value 1 is required to be met.

In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:

there are many factors that influence the matching result in the matching process, and different platforms have different emphasis on the content, but are limited to time, position, role, vehicle type and other data types defined by the platform.

In conclusion, the invention aims at solving the problem of matching of vehicles and goods sources with multiple roles, matching the goods sources with the most satisfied vehicles and achieving the purpose of efficient distribution.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 is a general flow diagram of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.

The invention is explained in detail with reference to the attached figure 1, which mainly comprises the following steps:

s1, establishing role channels and classifying the roles through known configuration roles; vehicle role types, screening distances and weights occupied by different factors;

s1-1, dividing the channels of the vehicles selected by the distance configuration, the vehicle requirements and the carrying state requirements according to the identity of the vehicles, and creating role channels meeting the working states of all drivers, namely one channel represents the vehicle of one role;

and S1-2, traversing all the screened driver working states in the step S1-1, corresponding the working years, the driving license grades and the accident rate of the drivers in the driver working states to the role channels reestablished in the step S1-1, and finishing the classification of the drivers according to the role channels.

S2, calculating vehicle matching values in a single channel through preset parameters, sorting, and selecting a maximum value;

all vehicles are screened by configuring a parameter 'distance', and the vehicles meeting the area are screened by taking the longitude and latitude of a delivery place as the center of a circle and the configured distance as the radius.

Screening the screened vehicle type in the order for the second time, and screening the vehicle type required in the order;

and screening the vehicles meeting the distance and vehicle type conditions for the third time, and screening out the idle vehicles according to the carrying states of the vehicles.

S3, selecting vehicles according to preset dispatch proportions of working years, driving license grades and accident rates of different drivers and proportion probabilities;

dividing channels of the screened vehicles according to the identities of the vehicles, creating channels meeting all the roles of drivers, and enabling one channel to represent the vehicle with one role;

the driver roles are classified by lane.

S4, calculating and sorting the vehicle matching scores,

the full score of the vehicle matching value is 100, the weights occupied by different factors can be set through self configuration, and the sum of all the weights needs to be equal to the value 1.

Calculating the score of each driver working state factor according to a vehicle weight calculation rule, wherein each driver working state factor is subjected to matching calculation according to a specific service scene, and the single factor is divided into 100 vehicle weights;

accumulating the scores obtained under all the factors to obtain a matching value of the final vehicle according to the order;

and sequencing the vehicles in each channel according to the final matching value, so as to obtain the maximum value of the matching value in each channel.

S5, making an order according to the working years, the driving license grade and the accident rate of the driver in the working state of the driver: the directional order dispatching is carried out according to the proportion of the order dispatched by the vehicle identity in the configuration, the configured proportion is a probability value, and the condition that the sum of the proportions of the orders dispatched by different vehicle roles is equal to a value 1 is required to be met.

After the working state of a driver is determined, the vehicle and goods are matched, wherein the dispatching requires a working method for carrying out optimized dispatching on a manifest and a carrier vehicle, and the working method comprises the following steps:

in the process of carrying out the optimized scheduling of the manifest and the carrier vehicles, the steps of an intelligent automatic vehicle selection method based on flexible parameter setting are required to be carried out firstly, and the method comprises the following steps:

s1, configuring parameters based on the management control system;

s2, screening out proper vehicle groups from the vehicle data and constructing a vehicle type pool;

s3, matching a proper vehicle pool through a weight polling algorithm and selecting a final vehicle;

the vehicle type pool: in a proper vehicle group, different vehicle pools are divided according to the roles of vehicle drivers, and the vehicle pools are sorted according to the ascending order of vehicle distances.

Preferably, the S1 includes:

in a freight logistics order dispatching control system, a series of algorithms for automatically selecting vehicles are required to embody the calculation parameters of a scene environment; the calculation parameters are usually from preset;

the parameters are generally derived from several sources:

a. system configuration file

The configuration measures taken by different systems may differ, and the available configuration file formats are: xml, yml, text, json, properties, pom, conf, and some other valid format;

in a modular system, this is most commonly used; the dispatching parameters in the logistics production system are changed frequently, and the configuration file parameters can take effect only by restarting the service after the parameters are changed; therefore, the method of saving parameters by relying on the system configuration file lacks flexibility and is not generally adopted;

b. temporary caching

The cache can be divided into a program internal cache and a middleware cache; the data reading speed from the cache is high, and parameters can be lost if a server fails or the storage period is expired, so that the single use is not ideal; can be used with relational databases in general;

c. database persistence

The database persistence storage parameters are an ideal mode; the data stored in a persistent mode can be transmitted to a background for storage through a front-section configuration page of a source configuration system; the method can ensure that the data is available persistently, the parameters can be flexibly configured and changed, new parameters can be used without restarting the service, and meanwhile, the parameters cannot be lost due to server failure or time; if the data reading frequency and efficiency are considered, the data reading device can be used with a temporary cache; preferably, the S2 includes:

the suitable vehicle population: a suitable population of vehicles selected by the vehicle using any one or more of the following parameters:

a. vehicle type: the existing vehicle types are available in the market;

b. vehicle distance: the current straight line or actual distance between the vehicle and the starting point of the logistics order;

c. the vehicle state: whether the vehicle is unloaded currently;

d. vehicle load: matching degree of the vehicle load and the weight or volume parameter of the ordered goods;

e. driver type: determining the driver type by a logistics company, attaching the weight set by a control system to the driver type, and constructing different vehicle pools according to the driver type subsequently;

f. whether the driver has rejected the order;

g. driver reputation;

preferably, as shown in fig. 1, the S3 includes:

dividing the proper vehicle group of S2 into different vehicle pools according to driver types, wherein each vehicle pool has corresponding weight and index, and is stored by a cache system or a relational database; the corresponding vehicle pool can be found only by calculating the index, and the corresponding weight can be found at the same time;

the steps of the weighted round robin algorithm are as follows:

S-A: each vehicle pool corresponds to an index subscript, the last selected vehicle pool index subscript is recorded by a cache system or a relational database, and the initial index subscript is-1; here denoted currentIndex;

s1: calculating the greatest common divisor of the weights in all the vehicle pools, namely taking the greatest common divisor with every two weights and then taking the greatest common divisor with the next weight until all the weights are taken; denoted herein as maxDivisor;

s2: calculating the number of vehicle pools, namely, how many vehicle pools exist; here denoted by carCount;

s3: calculating the maximum weight of all weights, here denoted maxWeight;

s4: calculating a current dispatch weight, represented here by currentWeight;

s5: after the weighted polling algorithm obtains the vehicle pool, the first vehicle in the pool is selected, namely the vehicle with the most suitable order.

Preferably, the calculating the current dispatching weight comprises the following steps:

S-A: initializing the current dispatch weight currentWeight to 0:

S-B: adding 1 to the last vehicle pool index subscript, dividing the obtained product by the number of the vehicle pools to obtain a remainder, and then assigning the remainder to the index subscript currentIndex;

S-C: if the index subscript currentIndex is 0, the value of the current dispatching weight is the current dispatching weight obtained in the last time minus the greatest common divisor; otherwise, executing the step S-D;

S-D: if the current dispatching weight currentWeight of the calculation result is less than or equal to 0, assigning the maximum weight to the current dispatching weight; otherwise, skipping to execute the step S-B;

S-E: obtaining the corresponding vehicle pool Weight according to the index subscript currentIndex calculated by S-B, and comparing the vehicle pool Weight with the current dispatch Weight currentWeight calculated by S-C or S-D;

S-F: if the corresponding Weight is more than or equal to the current dispatch Weight currentWeight, the vehicle pool corresponding to the selection index currentIndex is valid, and currentIndex is output;

if the corresponding Weight is less than the current dispatch Weight currentWeight, steps S-B through S-F continue to be repeated until a valid vehicle pool is found.

The following describes in detail an implementation example of the present invention, and both the "distribution of goods sources" in the step 1 and the "uploading of truck driver GPS position" in the step 2 are premised on using the corresponding app of smart logistics; and a set of management control system is applied to the parameter configuration in the step 2 to control the parameter configuration.

The invention provides an intelligent automatic vehicle selection method based on flexible parameter setting, which can flexibly configure vehicle selection parameters, screen out the remaining groups of suitable vehicles from a large number of vehicles, divide a vehicle pool, and finally acquire the vehicle pool according to an authority ratio polling algorithm so as to select the optimal vehicle.

The invention is explained in detail, which mainly comprises the following steps:

pre-step 1: and the owner publishes the goods source by using the related app and uploads the GPS position information of the goods source.

A step 2 is carried out in advance: the truck driver uses the relevant app to upload this GPS information.

Step 1: starting;

step 2: the specific parameters of the corresponding parameters required in the management control system configuration method are as follows:

a. vehicle type: the vehicle type is the type of the existing vehicle on the market;

b. vehicle distance: the current linear distance between the vehicle and the starting point of the logistics order;

c. the vehicle state: whether the vehicle is unloaded currently;

d. vehicle load: matching degree of vehicle load and fixed point;

e. driver type: determining the driver type by a logistics company, attaching the weight proportion to the driver type, and constructing different vehicle pools by the driver types subsequently;

f. whether the driver has rejected the order;

g. driver reputation;

and step 3: through the configuration parameters, the remaining vehicle groups meeting the goods source conditions can be obtained from a large number of vehicle groups, and the vehicles can be divided into different vehicle pools according to driver classification.

And 4, step 4: calculating the current most suitable vehicle pool according to the configured order dispatching weight values of the vehicle pools and a weight ratio polling algorithm; and sorting the vehicle distances of the vehicles in the vehicle pool.

And 5: according to the step 4, the current vehicle pool which should be selected is obtained, and the first driver which is the most suitable driver is selected from the vehicle pool.

Step 6: and (6) ending.

In the description of the present specification, the reference to the terms "source", "order" each refers to a source order in the logistics industry; the term "driver classification" is based on a specific scene classification, the company platform classifies drivers as hired drivers, purchased drivers, and out-board drivers, and different scene classifications do not affect the use of the method.

The invention provides a vehicle and goods matching method under various driver working states, the scheduling requirement is the process of carrying out optimized scheduling on a manifest and a carrier vehicle, and the method specifically comprises the following steps:

a, respectively generating a manifest information set, a carrier vehicle information set and a vehicle scheduling requirement information set according to manifest information, carrier vehicle information and vehicle scheduling requirement information;

b, extracting manifest information from the manifest information set, planning a map path, generating a map path planning information set, and performing scheduling judgment on the map path planning information set;

and C, carrying out cargo matching transportation on the manifest information, the carrying vehicle information and the vehicle scheduling requirement information which meet the scheduling judgment requirement, and carrying out re-matching on the manifest information, the carrying vehicle information and the vehicle scheduling requirement information which do not meet the scheduling judgment requirement.

S1, inputting a manifest information set comprising a manifest number, a starting place, a destination, a cargo weight and a cargo volume; a carrier vehicle information set comprising driver name, contact number, license plate number, vehicle load and vehicle capacity; the scheduling requirement information set comprises a scheduling loading rate, a scheduling travel distance, a scheduling average loading and unloading duration and a scheduling route preference;

s1-1, when acquiring the manifest information set, forming the manifest information attribution function U (L)_iI C) for classifying the manifest information in different attributes and identifying each manifest information attribute with a function L_iThe values of the order are arranged from large to small and are respectively used as a granularity C in i manifest information attribution functions to form the manifest information attribution function, wherein i is more than or equal to 1; wherein the acquaintance function

c_iTo initially identical information values, d_iTo the destination of the same information value, e_iFor initially different information values, f_iFor the different information values of the destination,

the different attributes are, for example, the same origin of the manifest information is an attribute, the same destination of the manifest information is an attribute, the different origin of the manifest information is an attribute, the different destination of the manifest information is an attribute,

s1-2, refining the cargo weight information j and the cargo volume information k in the manifest information set to form a transportation preparation objective function in the manifest information set,

m≠l，a_jktransportation costs for cargo weight information j and cargo volume information k, b_jkThe carrying distance, q, for the cargo weight information j and the cargo volume information k_jkThe probability of losing the cargo weight information j and the cargo volume information k is obtained; wherein maybejk is the manifest shipment judgment value,

a judgment value of (d);

s1-3, matching and judging the vehicle load and the vehicle capacity in the carrier vehicle information set,

the attribution function of the information set of the carrier vehicle is V (W)_h| D) for dividing the carrier vehicle information into the vehicle load and the vehicle capacity, and identifying the vehicle load and the vehicle capacity by a function W for identifying each carrier vehicle information_hThe values of the vehicle information sets are arranged from large to small and respectively used as a granularity D in h carrier vehicle information set attribution functions to form the carrier vehicle information set attribution function, wherein h is more than or equal to 1; wherein the acquaintance function

sh is the value of load attribute of the carrier vehicle, t_hIn order to carry the vehicle capacity attribute value,

by matching expressions of correlation functions

S1-4, matching the dispatching requirement information set with the manifest information set and the carrier vehicle information set, matching the vehicle load and the vehicle capacity of the origin and the destination in the manifest information through the carrier vehicle information set, in the dispatching process, for the manifest information of the origin according to the vehicle set D_vehicle＝{D₁,D₂,...,D_nMatching the vehicle load and the vehicle capacity, acquiring a destination after matching, calculating a scheduling driving distance, performing scheduling average loading and unloading duration and scheduling route preference setting on an order, and recommending a plurality of planning paths to select M-N under the constraint condition of specified time_h,N_s,N_c,N_oNh is a scheduling route cost value of the whole-course high-speed, Ns is a scheduling route cost value of the whole-course province, Nc is a cost value with the lowest cost, and No is a cost value closest to the planned route, wherein the cost values include oil cost, high-speed cost, server consumption, packaging cost, labor cost or parking cost; generating manifest information under a plurality of planned pathsThe list is used for calculating the vehicle driving mileage, the cost of using oil products and whether high-speed toll exists;

cost pre-judgment through scheduling matching model

J(D_vehicle|M·W_p) For matching models of vehicle sets and planned paths in the most costly transport dispatch, W_pIn order to match the weight value at a high cost,

to calculate the matching model objective function, J (D)_vehicle|M·R_p) Matching models for vehicle set and planned path in least costly transportation scheduling, R_pFor low cost matching weights, max (M | K)_p) For maximum scheduling cost overhead, K_pMin (M | S) as the maximum cost overhead weight_p) For minimum scheduling cost overhead, S_pIs the minimum cost overhead weight, p is a positive integer;

s2, planning the travel distance and the time consumption of the manifest and the map truck path according to the judgment function of the scheduling requirement information set;

s3, screening out a satisfactory manifest set B and a unsatisfactory manifest set A according to the manifest travel distance and the scheduling travel distance;

s4, if the manifest set B is empty, ending the dispatching and outputting the result; the information combination of the vehicle information of the carrier and the empty carrier forms a waybill information set, wherein the waybill information set comprises a waybill number, a starting place, a destination, a driver name, a contact telephone, a license plate number, a vehicle loading rate, a vehicle driving distance and a driving time;

s5, screening out a satisfactory waybill set F and a unsatisfactory waybill set E according to the vehicle loading rate and the dispatching loading rate;

s6, ending the output of the scheduling result when the waybill set E is empty; if not, screening out a full freight note set G and an incomplete freight note set H according to the vehicle loading rate and the scheduling loading rate;

s7, if the full-load manifest set G is not empty, determining a vehicle loading manifest set, a loaded manifest set and a scheduled vehicle set according to the scheduling route preference; if the current transport list is empty, continuing to schedule the non-full transport list set H;

s8, judging whether the manifest is loaded or not according to the loaded manifest set and the manifest set meeting the requirements or judging whether the vehicle is used up or not according to the scheduled vehicle set and the carrier vehicle information set, finishing scheduling when the manifest is loaded or the vehicle is used up, and outputting a scheduling result; otherwise, continuously scheduling the non-full freight bill set, and excluding the loaded freight bill and the used vehicle set;

s9, performing vehicle 1 st waybill scheduling on the rest vehicle set according to the unloaded waybill set, the scheduling route preference and the maximum loading rate to obtain a vehicle loading waybill, a rest loading rate and a rest travel distance, and executing S8, wherein the loaded waybill set and the used vehicle set are excluded;

s10, according to the remaining loading rate of the vehicle, the remaining driving distance, the unfilled freight bill set and the scheduling route preference, repeating the scheduling of 2-N loading freight bills of the vehicle to obtain a vehicle loading freight bill, wherein the loaded freight bill set and the used vehicle set need to be eliminated, executing S8, and after the vehicle remaining loading is completed, repeatedly executing S9 and S10.

While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (6)

1. A vehicle and goods matching method under various driver working states in the logistics industry is characterized by comprising the following steps:

s1, establishing role channels and classifying the roles through known configuration roles; vehicle role types, screening distances and weights occupied by different factors;

s2, calculating vehicle matching values in a single channel through preset parameters, sorting, and selecting a maximum value;

s3, selecting vehicles according to preset dispatch proportions of working years, driving license grades and accident rates of different drivers and proportion probabilities;

and S4, calculating and sequencing the vehicle matching scores, and performing directional order dispatching according to the proportion of the orders configured by the vehicle identities.

2. The vehicle and cargo matching method under multiple driver operating states in logistics industry of claim 1, wherein the S1 comprises:

s1-1, dividing the channels of the vehicles selected by the distance configuration, the vehicle requirements and the carrying state requirements according to the identity of the vehicles, and creating role channels meeting the working states of all drivers, namely one channel represents the vehicle of one role;

and S1-2, traversing all the screened driver working states in the step S1-1, corresponding the working years, the driving license grades and the accident rate of the drivers in the driver working states to the role channels reestablished in the step S1-1, and finishing the classification of the drivers according to the role channels.

3. The vehicle and cargo matching method under multiple driver operating states in logistics industry of claim 1, wherein the S2 comprises:

all vehicles are screened by configuring a parameter 'distance', and the vehicles meeting the area are screened by taking the longitude and latitude of a delivery place as the center of a circle and the configured distance as the radius.

Screening the screened vehicle type in the order for the second time, and screening the vehicle type required in the order;

and screening the vehicles meeting the distance and vehicle type conditions for the third time, and screening out the idle vehicles according to the carrying states of the vehicles.

4. The vehicle and cargo matching method under multiple driver operating states in logistics industry of claim 1, wherein the S3 comprises:

dividing channels of the screened vehicles according to the identities of the vehicles, creating channels meeting all the roles of drivers, and enabling one channel to represent the vehicle with one role; the driver roles are classified by lane.

5. The vehicle and cargo matching method under multiple driver operating states in logistics industry of claim 1, wherein the S4 comprises:

the full score of the vehicle matching value is 100, the weights occupied by different factors can be set through self configuration, and the sum of all the weights needs to be equal to the value 1.

Calculating the score of each driver working state factor according to a vehicle weight calculation rule, wherein each driver working state factor is subjected to matching calculation according to a specific service scene, and the single factor is divided into 100 vehicle weights;

accumulating the scores obtained under all the factors to obtain a matching value of the final vehicle according to the order;

and sequencing the vehicles in each channel according to the final matching value, so as to obtain the maximum value of the matching value in each channel.

6. The vehicle and cargo matching method under multiple driver operating states in logistics industry of claim 5, wherein the S4 further comprises:

and (3) making a list according to the working years, the driving license grades and the accident rate of the driver in the working state of the driver: the directional order dispatching is carried out according to the proportion of the order dispatched by the vehicle identity in the configuration, the configured proportion is a probability value, and the condition that the sum of the proportions of the orders dispatched by different vehicle roles is equal to a value 1 is required to be met.

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