Carbon Emission Calculation Method, Device and Computer Equipment for Shared Bicycle Scheduling

Abstract

The present application relates to a method, apparatus, and computer device for calculating carbon emissions of shared bicycle scheduling. The method includes: obtaining historical riding data of shared bicycles within a service scope; obtaining historical scheduling records of shared bicycles according to the historical riding data; obtaining historical scheduling waves of shared bicycles according to the historical scheduling records; performing spatial geographical clustering on the historical scheduling waves to obtain a plurality of clustering groups; calculating the minimum necessary scheduling distance of shared bicycles within each clustering group according to the historical scheduling waves of the plurality of clustering groups; and calculating the carbon emissions of shared bicycle scheduling according to the carbon emission intensity factor per unit driving distance of a scheduling vehicle and the minimum necessary scheduling distance, thereby improving the calculation accuracy of the carbon emissions of shared bicycle scheduling and improving the accuracy and comprehensiveness of the environmental benefit assessment of the entire shared bicycle service project.

Description

Technical Field

[0001] The present invention relates to the field of transportation means, and particularly to a method, device and computer equipment for calculating carbon emissions of shared bicycle scheduling. Background Art

[0002] During the operation and maintenance process of shared bicycles, the scheduling work is another key link that generates carbon emissions in the shared bicycle industry besides the production link. In the operation and maintenance process of shared bicycles, dispatchers need to use dispatch vehicles to batch dispatch shared bicycles to balance the number of bicycles between stations, and the carbon emissions generated in this process cannot be ignored.

[0003] Currently, in the carbon emission reduction verification methods of existing shared bicycle service projects, only the carbon emission reduction promoted by the riding end through replacing other transportation modes is considered, while the energy consumption and environmental impact generated by using dispatch vehicles for bicycle scheduling during the system service period at the operation and maintenance end are ignored.

[0004] Since it is not easy to obtain the historical dispatch distance and energy consumption statistical data of current shared bicycles, it is difficult to carry out the accounting work of carbon emissions in this process, resulting in the carbon emission reduction benefit accounting framework of existing shared bicycle service projects only considering the emission reduction situation at the riding end and ignoring the carbon emissions at the operation and maintenance end, leading to an incomplete assessment of the environmental benefits of the entire shared bicycle service project and the problem of overestimating carbon emission reduction. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a method, device and computer equipment for calculating carbon emissions of shared bicycle scheduling, which can improve the accuracy of calculating carbon emissions of shared bicycle scheduling.

[0006] According to the first aspect of the embodiments of the present application, a method for calculating carbon emissions of shared bicycle scheduling is provided, including the following steps:

[0007] Obtain historical riding data of shared bicycles within the service range;

[0008] Obtain the historical dispatch records of shared bicycles according to the historical riding data;

[0009] Obtain the historical dispatch waves of shared bicycles according to the historical dispatch records;

[0010] Perform spatial geographical clustering on the historical dispatch waves to obtain several clustering groups;

[0011] Calculate the minimum necessary dispatch distance of shared bicycles within each clustering group according to the historical dispatch waves of several clustering groups;

[0012] Calculate the carbon emissions of shared bicycle scheduling according to the carbon emission intensity factor per unit driving distance of the dispatch vehicle and the minimum necessary dispatch distance.

[0013] According to a second aspect of the embodiments of the present application, a carbon emission calculation device for shared bicycle scheduling is provided, including:

[0014] A riding data acquisition module, configured to acquire historical riding data of shared bicycles within the service range;

[0015] A scheduling record obtaining module, configured to obtain the historical scheduling records of shared bicycles according to the historical riding data;

[0016] A scheduling wave obtaining module, configured to obtain the historical scheduling waves of shared bicycles according to the historical scheduling records;

[0017] A clustering group obtaining module, configured to perform spatial geographical clustering on the historical scheduling waves to obtain a plurality of clustering groups;

[0018] A scheduling distance calculation module, configured to calculate the minimum necessary scheduling distance of shared bicycles within each clustering group according to the historical scheduling waves of the plurality of clustering groups;

[0019] A carbon emission calculation module, configured to calculate the carbon emissions of shared bicycle scheduling according to the carbon emission intensity factor per unit driving distance of the scheduling vehicle and the minimum necessary scheduling distance.

[0020] According to a third aspect of the embodiments of the present application, a computer device is provided, including: a processor and a memory; wherein, the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the carbon emission calculation method for shared bicycle scheduling as described in any one of the above.

[0021] In the embodiments of the present application, by acquiring the historical riding data of shared bicycles within the service range; obtaining the historical scheduling records of shared bicycles according to the historical riding data; obtaining the historical scheduling waves of shared bicycles according to the historical scheduling records; performing spatial geographical clustering on the historical scheduling waves to obtain a plurality of clustering groups; calculating the minimum necessary scheduling distance of shared bicycles within each clustering group according to the historical scheduling waves of the plurality of clustering groups; and calculating the carbon emissions of shared bicycle scheduling according to the carbon emission intensity factor per unit driving distance of the scheduling vehicle and the minimum necessary scheduling distance, the calculation accuracy of the carbon emissions of shared bicycle scheduling is improved, and the accuracy and comprehensiveness of the environmental benefit assessment of the entire shared bicycle service project are improved.

[0022] It should be understood that the above general description and subsequent detailed description are only exemplary and explanatory, and cannot limit the present application.

[0023] For better understanding and implementation, the present invention will be described in detail below with reference to the accompanying drawings. Description of the Drawings

[0024] Figure 1 Flow diagram of the method for calculating carbon emissions of shared bicycle scheduling provided by an embodiment of the present application;

[0025] Figure 2 Flow diagram of step S20 of the method for calculating carbon emissions of shared bicycle scheduling provided by an embodiment of the present application;

[0026] Figure 3 Flow diagram of step S30 of the method for calculating carbon emissions of shared bicycle scheduling provided by an embodiment of the present application;

[0027] Figure 4 Flow diagram of step S33 of the method for calculating carbon emissions of shared bicycle scheduling provided by an embodiment of the present application;

[0028] Figure 5 Flow diagram of step S40 of the method for calculating carbon emissions of shared bicycle scheduling provided by an embodiment of the present application;

[0029] Figure 6 Flow diagram of step S50 of the method for calculating carbon emissions of shared bicycle scheduling provided by an embodiment of the present application;

[0030] Figure 7 Structure block diagram of the device for calculating carbon emissions of shared bicycle scheduling provided by an embodiment of the present application. Detailed implementation manners

[0031] To make the objectives, technical solutions and advantages of the present application clearer, the embodiments of the present application will be further described in detail below with reference to the accompanying drawings.

[0032] It should be clear that the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the scope of protection of the present application.

[0033] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a", "the" and "said" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0034] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0035] In addition, in the description of the present application, unless otherwise specified, "a plurality of" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. The character " / " generally represents an "or" relationship between the associated objects before and after.

[0036] Please refer to Figure 1 , which is a schematic flowchart of a method for calculating the carbon emissions of shared bicycle scheduling provided by an embodiment of the present application. The method for calculating the carbon emissions of shared bicycle scheduling provided by the embodiment of the present application includes the following steps:

[0037] S10: Obtain the historical riding data of shared bicycles within the service range.

[0038] In the embodiment of the present application, the service range refers to the target area where shared bicycle services can be provided. Specifically, it can be the placement points of shared bicycles and the surrounding geographical space areas. The historical riding data refers to the riding data of all shared bicycles in each time period within the service range, including the riding order number, the riding bicycle number, the riding start time, the riding end time, the riding start station number, the riding end station number, the longitude and latitude of the riding start station, and the longitude and latitude of the riding end station.

[0039] S20: Obtain the historical scheduling records of shared bicycles according to the historical riding data.

[0040] In the embodiment of the present application, the historical scheduling record refers to the scheduling information of shared bicycles being scheduled from one place to another in space. The scheduling process can be by using a scheduling vehicle, or the operator can motivate riding users to transfer the bicycles by walking or riding through other means. The historical scheduling record includes the scheduling start station number, the scheduling start time, the scheduling end station number, the scheduling end time, and the scheduling bicycle number.

[0041] The historical riding data records the time and location information of unlocking and locking each shared bicycle. In the record, the station sequences of the same shared bicycle are usually continuous. If the ending station number of the previous riding order is different from the starting station number of the next riding order among two adjacent riding orders, it indicates that there has been at least one shared bicycle scheduling between these two riding orders.

[0042] S30: Obtain the historical scheduling waves of shared bicycles according to the historical scheduling records.

[0043] In the embodiments of the present application, the historical scheduling records further include a scheduling chain and a scheduling time window. The scheduling chain is a chain formed by connecting the starting station number and the ending station number of the scheduling with a hyphen "-". The scheduling time window is a time interval with the starting scheduling time as the left endpoint and the ending scheduling time as the right endpoint.

[0044] The historical scheduling waves include either a single shared bicycle being an independent scheduling wave or multiple shared bicycles being combined into one scheduling wave. The shared bicycles in the same scheduling wave are transferred from one spatial geographical location to another using the same scheduling method within the same time range. The scheduling wave includes a scheduling chain, a starting station number of the scheduling, an ending station number of the scheduling, the longitude and latitude of the starting station of the scheduling, the longitude and latitude of the ending station of the scheduling, the number of scheduled bicycles, and the scheduling time window.

[0045] If the historical scheduling record of a single shared bicycle is an independent scheduling wave, the scheduling time window of the scheduling wave is the scheduling time window corresponding to the historical scheduling record of the single shared bicycle. If the historical scheduling records of multiple shared bicycles are combined into one scheduling wave, the scheduling time window of the scheduling wave is the maximum overlapping time interval of the scheduling time windows corresponding to the historical scheduling records of all the combined shared bicycles.

[0046] S40: Perform spatial geographical clustering on the historical scheduling waves to obtain several clustering groups.

[0047] In the embodiments of the present application, considering that the operator usually assigns only one scheduling vehicle to complete the task of the shared bicycle scheduling wave concentrated within a certain spatial geographical range, that is, the scheduling waves with relatively concentrated geographical locations are usually completed by the same scheduling vehicle. Performing spatial geographical clustering on the historical scheduling waves enables the same historical scheduling wave and the historical scheduling waves with close spatial distances to be assigned to the same clustering group, and all the scheduling waves in the same clustering group are completed by the same scheduling vehicle.

[0048] S50: Calculate the minimum necessary scheduling distance of the shared bicycles within each clustering group according to the historical scheduling waves of the several clustering groups.

[0049] The vehicle routing problem (VRP) refers to a situation where a certain number of customers each have different demands for the quantity of goods. A distribution center supplies goods to the customers, and a fleet of vehicles is responsible for delivering the goods. An appropriate driving route needs to be organized. The goal is to meet the customers' demands and, under certain constraints, achieve objectives such as the shortest distance, the lowest cost, and the least time consumption. An integer programming model means that the variables (all or part) in the programming are restricted to integers. In the embodiments of the present application, all historical dispatching waves within each clustering group are input into the vehicle routing problem mixed integer programming model to obtain the minimum necessary dispatching distance of the shared bicycles within each clustering group. Among them, the minimum necessary dispatching distance refers to the minimum value of the total route distance traveled by the dispatched dispatching vehicles on the premise of completing all historical dispatching wave tasks within the clustering group at one time.

[0050] S60: Calculate the carbon emissions of the shared bicycle dispatching according to the carbon emission intensity factor per unit driving distance of the dispatching vehicle and the minimum necessary dispatching distance.

[0051] In the embodiments of the present application, the dispatching vehicle is a tool for dispatching shared bicycles, such as a truck. The carbon emission intensity factor per unit driving distance of the dispatching vehicle is easy to obtain and standardize. Specifically, it can be determined according to the evaluation criteria of the service scope where the shared bicycle project is located. For example, the carbon emission intensity factor of a certain truck is 0.0008 tons of carbon dioxide equivalent per kilometer. Given the carbon emission intensity factor per unit driving distance of the dispatching vehicle and the minimum necessary dispatching distance of the dispatching vehicle, the carbon emissions of the shared bicycle dispatching can be calculated automatically and quickly.

[0052] Applying the embodiments of the present application, by obtaining the historical riding data of the shared bicycles within the service scope; obtaining the historical dispatching records of the shared bicycles according to the historical riding data; obtaining the historical dispatching waves of the shared bicycles according to the historical dispatching records; performing spatial geographical clustering on the historical dispatching waves to obtain several clustering groups; calculating the minimum necessary dispatching distance of the shared bicycles within each clustering group according to the historical dispatching waves of the several clustering groups; calculating the carbon emissions of the shared bicycle dispatching according to the carbon emission intensity factor per unit driving distance of the dispatching vehicle and the minimum necessary dispatching distance, thereby improving the calculation accuracy of the carbon emissions of the shared bicycle dispatching and enhancing the accuracy and comprehensiveness of the environmental benefit evaluation of the entire shared bicycle service project.

[0053] In an alternative embodiment, please refer to Figure 2 , the step S20 includes steps S21 to S23, which are specifically as follows:

[0054] S21: Group the historical riding data according to the riding bicycle number to obtain several bicycle groups; each bicycle group includes several riding orders.

[0055] The riding bicycle number is used to uniquely identify a shared bicycle, and the riding bicycle numbers corresponding to each shared bicycle are different. Group the historical riding data by the riding bicycle number to obtain several bicycle groups. Each bicycle group can be identified by the riding bicycle number or by a newly created bicycle group number. A riding order includes the riding start time, riding end time, riding start station number, and riding end station number of the shared bicycle. A riding order can be identified by a riding order number. Generally, one riding bicycle number corresponds to multiple riding order numbers, that is, each shared bicycle generates multiple riding orders.

[0056] S22: Sort all the riding orders of each bicycle group in ascending order according to the riding start time to obtain several riding order sequences.

[0057] Ascending order means arranging from smallest to largest. Each bicycle group includes several riding orders, and these riding orders are disorderly. Obtain the riding start time of each riding order, and arrange all the riding orders of each bicycle group in ascending order according to the riding start time to obtain several riding order sequences, which is convenient for subsequent analysis of each riding order sequence.

[0058] S23: Traverse each riding order in each riding order sequence. If the riding end station number of the current riding order is different from the start station number of the next riding order, use the riding end station number of the current riding order as the dispatching start station number of the historical dispatching record, use the riding end time of the current riding order as the dispatching start time of the historical dispatching record, use the riding start station number of the next riding order as the dispatching end station number of the historical dispatching record, use the riding start time of the next riding order as the dispatching end time of the historical dispatching record, and use the bicycle number of the next riding order as the dispatching bicycle number of the historical dispatching record to obtain the historical dispatching record of the shared bicycle.

[0059] In the riding order sequence of each bicycle group, the current riding order and the next riding order are two adjacent riding orders. If the riding end station number of the current riding order is different from the start station number of the next riding order, it means that the geographical location of the shared bicycle has changed, that is, there is a dispatching behavior. If there is only one riding order in the riding order sequence, skip this riding order sequence and perform the above process on the next riding order sequence. By judging all adjacent two riding orders in the riding order sequence of each bicycle group through the above process, all historical dispatching records of each bicycle group can be obtained automatically and quickly.

[0060] In an alternative embodiment, refer to Figure 3 , the historical scheduling record includes a scheduling start site number and a scheduling end site number, and the scheduling start site number and the scheduling end site number form a scheduling chain. The step S30 includes steps S31 to S37, which are specifically as follows:

[0061] S31: Group the historical scheduling records according to the scheduling chain to obtain several scheduling chain groups.

[0062] Taking 5 historical scheduling records as an example for illustration, historical scheduling record 1, historical scheduling record 3, and historical scheduling record 4 all include a scheduling start site number A and a scheduling end site number B, that is, they all include the scheduling chain A - B. Historical scheduling record 3 and historical scheduling record 5 both include a scheduling start site number C and a scheduling end site number D, that is, they all include the scheduling chain C - D. Group historical scheduling record 1, historical scheduling record 3, and historical scheduling record 4 into the scheduling chain group A - B, and group historical scheduling record 3 and historical scheduling record 5 into the scheduling chain group C - D.

[0063] S32: Arrange all the historical scheduling records of each scheduling chain group in ascending order according to the scheduling start time and the scheduling end time, and obtain the maximum value of the scheduling start time and the minimum value of the scheduling end time of each scheduling chain group.

[0064] Arrange all the historical scheduling records of each scheduling chain group in ascending order according to the scheduling start time, and then arrange the historical scheduling records with the same scheduling start time in ascending order according to the scheduling end time. Denote the maximum value of the scheduling start time of each scheduling chain group as maxST, and the minimum value of the scheduling end time of each scheduling chain group as minET.

[0065] S33: Calculate the necessary scheduling time of the scheduling chain for each scheduling chain group.

[0066] Each scheduling chain group corresponds to a unique scheduling chain. For example, the scheduling chain group A - B corresponds to the scheduling chain A - B. The necessary scheduling time of the scheduling chain refers to the transportation time required for the scheduling vehicle to run from the scheduling start site to the scheduling end site, denoted as nT.

[0067] S34: Traverse each of the scheduling chain groups. If the minimum value of the scheduling end time of the current scheduling chain group is less than the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, split the current scheduling chain group. Classify the historical scheduling records in the current scheduling chain group with a scheduling start time less than the maximum value of the scheduling start time into a preset first scheduling chain group, and classify the historical scheduling records in the current scheduling chain group with a scheduling start time greater than or equal to the maximum value of the scheduling start time into a preset second scheduling chain group.

[0068] For each scheduling chain group, determine whether the minimum value of the scheduling end time of the current scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, that is, determine whether minET≥maxST + nT holds. If so, merge all the historical scheduling records of the current scheduling chain group into a historical scheduling wave. The scheduling time window of the merged historical scheduling wave is [maxST, minET], and the number of single vehicles scheduled in the merged historical scheduling wave is the total number of historical scheduling records. For example, the current scheduling chain group includes 3 historical scheduling records, namely historical scheduling record a, historical scheduling record b, and historical scheduling record c. The scheduling start time of historical scheduling record a is ST1, the scheduling end time is ET1, the scheduling start time of historical scheduling record b is ST2, the scheduling end time is ET2, the scheduling start time of historical scheduling record c is ST3, the scheduling end time is ET3, ST1 < ST2 < ST3, ET1 < ET2 < ET3, maxST is ST3, minET is ET1, and ET1≥ST3 + nT. Then the scheduling time window of the merged historical scheduling wave is [ST3, ET1], and the number of single vehicles in the merged historical scheduling wave is 3.

[0069] If not, split the current scheduling chain group. Specifically, classify the historical scheduling records in the current scheduling chain group with a scheduling start time less than the maximum value maxST of the scheduling start time into a preset first scheduling chain group, and classify the historical scheduling records in the current scheduling chain group with a scheduling start time greater than or equal to the maximum value maxST of the scheduling start time into a preset second scheduling chain group.

[0070] S35: Obtain the maximum value of the scheduling start time and the minimum value of the scheduling end time of the first scheduling chain group. If the minimum value of the scheduling end time of the first scheduling chain group is less than the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, split the first scheduling chain group, and classify the historical scheduling records in the first scheduling chain group whose scheduling start time is greater than or equal to the maximum value of the scheduling start time into the preset second scheduling chain group until the minimum value of the scheduling end time of the first scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain. Then, merge the historical scheduling records of the first scheduling chain group into one historical scheduling wave.

[0071] Perform the same operation on the first scheduling chain group as on the aforementioned current scheduling chain group, that is, determine whether the minimum value of the scheduling end time of the first scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain. If so, merge the historical scheduling records of the first scheduling chain group into one historical scheduling wave. The process of merging the historical scheduling records of the first scheduling chain group is the same as that of the aforementioned current scheduling chain group and will not be elaborated here.

[0072] If not, split the first scheduling chain group until the minimum value of the scheduling end time of the split first scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, so as to determine the historical scheduling wave of the first scheduling chain group.

[0073] S36: Obtain the maximum value of the scheduling start time and the minimum value of the scheduling end time of the second scheduling chain group. If the minimum value of the scheduling end time of the second scheduling chain group is less than the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, split the second scheduling chain group until the minimum value of the scheduling end time of the second scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain. Then, merge the historical scheduling records of the second scheduling chain group into one historical scheduling wave until all the historical scheduling waves of the current scheduling chain group are determined.

[0074] Perform the same operation on the second scheduling chain group as on the aforementioned current scheduling chain group or the first scheduling chain group, that is, determine whether the minimum value of the scheduling end time of the second scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain. If so, merge the historical scheduling records of the second scheduling chain group into one historical scheduling wave. The process of merging the historical scheduling records of the second scheduling chain group is the same as that of the aforementioned current scheduling chain group and will not be elaborated here.

[0075] Otherwise, split the second dispatching chain group until the minimum value of the dispatching end time of the second dispatching chain group is greater than or equal to the sum of the maximum value of the dispatching start time and the necessary dispatching time of the dispatching chain, so as to determine the historical dispatching wave of the second dispatching chain group. After determining the historical dispatching wave of the first dispatching chain group and the historical dispatching wave of the second dispatching chain group, the historical dispatching wave of the current dispatching chain group is obtained.

[0076] S37: Traverse each of the dispatching chain groups until the historical dispatching waves of all the dispatching chain groups are determined, and obtain the historical dispatching wave of the shared bicycles.

[0077] Repeat steps S34 to S36 for each dispatching chain group until the historical dispatching waves of all the dispatching chain groups are determined, and obtain the historical dispatching wave of the shared bicycles.

[0078] In an alternative embodiment, please refer to Figure 4 , step S33 includes steps S331 to S332, specifically as follows:

[0079] S331: Obtain the actual shortest road network distance of the dispatching chain corresponding to each of the dispatching chain groups and the average speed of the dispatching vehicle;

[0080] S332: Take the ratio of the actual shortest road network distance of the dispatching chain to the average speed of the dispatching vehicle as the necessary dispatching time of the dispatching chain for each of the dispatching chain groups.

[0081] In the embodiment of the present application, the dispatching chain corresponding to each dispatching chain group includes a dispatching start station number and a dispatching end station number. The dispatching start station number is used to uniquely identify the dispatching start station, and the dispatching end station number is used to uniquely identify the dispatching end station. By using the road network analysis technology to obtain the urban road network information, the actual shortest road network distance between the dispatching start station and the dispatching end station can be obtained. The average speed of the dispatching vehicle can be set according to the specific situation of the application scenario. After obtaining the actual shortest road network distance of the dispatching chain corresponding to each dispatching chain group and the average speed of the dispatching vehicle, the necessary dispatching time of the dispatching chain for each dispatching chain group can be calculated automatically and quickly.

[0082] In an alternative embodiment, the method for calculating the carbon emission of the shared bicycle dispatching further includes step S38, specifically as follows:

[0083] S38: Traverse the number of single dispatching vehicles in each historical dispatching wave. If the number of single dispatching vehicles in the current historical dispatching wave is greater than the maximum capacity of the dispatching vehicle, split the current historical dispatching wave into several dispatching waves; the number of single dispatching vehicles in any one of the several dispatching waves from the first dispatching wave to the penultimate dispatching wave is the maximum capacity of the dispatching vehicle, and the number of single dispatching vehicles in the last dispatching wave is the difference between the number of single dispatching vehicles in the current dispatching wave and the sum of the number of single dispatching vehicles in the remaining dispatching waves among the several dispatching waves.

[0084] In the embodiments of the present application, considering that in reality, operators tend to complete the dispatching tasks of historical dispatching waves as much as possible at one time, and there are fewer cases where the same historical dispatching wave task is completed by two or more dispatching vehicles, unless the maximum capacity of the dispatching vehicle cannot meet the number of single dispatching vehicles in the historical dispatching wave.

[0085] For the case where the number of single dispatching vehicles in the historical dispatching wave exceeds the maximum capacity of the dispatching vehicle in reality, the following corrections are made: First, set the maximum capacity of the dispatching vehicle according to the actual situation, obtain the historical dispatching waves where the number of single dispatching vehicles exceeds the maximum capacity of the dispatching vehicle, and disassemble the historical dispatching waves. The disassembling process is as follows: Divide the number of single dispatching vehicles by the maximum capacity of the dispatching vehicle, and then round up the quotient. The integer obtained is recorded as m, and m is the number of new dispatching waves that the historical dispatching wave needs to be disassembled into. Then, except for the modification of the number of single dispatching vehicles, other information of each newly established dispatching wave is the same as that of the original historical dispatching wave. Other information includes the dispatching start site number, the dispatching end site number, the dispatching start time, and the dispatching end time. The sum of the number of single dispatching vehicles in the m disassembled dispatching waves is equal to the number of single dispatching vehicles in the original historical dispatching wave, and the number of single dispatching vehicles in the first m - 1 of the m dispatching waves is the maximum capacity of the dispatching vehicle, and the number of single dispatching vehicles in the last dispatching wave is the difference between the number of single dispatching vehicles in the original historical dispatching wave and the sum of the number of single dispatching vehicles in the first m - 1 dispatching waves.

[0086] In an alternative embodiment, please refer to Figure 5 , step S40 includes steps S41 to S42, specifically as follows:

[0087] S41: Obtain the longitude and latitude of the dispatching start site and the longitude and latitude of the dispatching end site for each historical dispatching wave;

[0088] S42: Input the longitude and latitude of the dispatching start site and the longitude and latitude of the dispatching end site into the spatial geographic clustering mixed integer programming model to obtain the clustering groups corresponding to each historical dispatching wave.

[0089] In the embodiment of the present application, the spatial geographical clustering mixed integer programming model is based on k-means mean clustering, and the paired relationship of OD sites (O refers to the "origin site", Origin, D refers to the "destination site", Destination) in historical scheduling waves is added for constraint improvement to ensure that the scheduling start site and the scheduling end site in the same historical scheduling wave both belong to the same clustering group, that is, they are both accessed by the same scheduling vehicle. The specific construction process of the spatial geographical clustering mixed integer programming model is as follows:

[0090] Step a: Take the latitudes and longitudes of the scheduling start sites and the scheduling end sites of each historical scheduling wave as the input quantities of the model, set the parameter r value according to practical experience, and determine the number of clustering groups according to the formula m = α / r. Where α is the total number of sites involved in the historical scheduling wave, and the sites involved in the historical scheduling wave can be represented by V i , i ∈ {1, 2,..., α}, V i and V i+α / 2 (i ∈ {1, 2,..., α / 2}) are the start site and the end site of the same historical scheduling wave respectively. r is the specified upper limit number of wave sites covered by each clustering group, m is the number of clustering groups, and since the historical scheduling waves of each clustering group are completed by one scheduling vehicle, m is also the number of times the scheduling vehicle is dispatched.

[0091] Step b: Set the number of clustering process trials T, numbered by t. For example, T = 1000, t = 1, 2,......, 1000.

[0092] Step c: Start the t-th clustering test. Arbitrarily select m sites from all the scheduling start sites as the initial centroids K. The initial centroids K are the set of geometric centers of all clustering groups, numbered by k. Solve the Euclidean distance matrix from the initial centroids K to the site V i , i ∈ {1, 2,..., α}

[0093] Step d: The site clustering assignment situation y ik and the optimal transportation distance value obj are solved through the following clustering mixed integer programming:

[0094] min obj = ∑ i∈V ∑ k∈K dist ik y ik y ik ∈ {0, 1}, i ∈ V, k ∈ K (1)

[0095] ∑ k∈K y ik = 1 i ∈ V (2)

[0096]

[0097]

[0098]

[0099] Among them, formula (1) stipulates that the objective function is to minimize the sum of the Euclidean distances from all stations to the centroid of the cluster group to which they are assigned. Formula (2) means that each station is assigned to one and only one cluster group. When station V i belongs to cluster k, y ik is 1, otherwise y ik is 0. Formula (3) requires that the start station and the end station of the same historical scheduling wave should be assigned to the same cluster group, that is, a complete historical scheduling wave is completed by only one scheduling vehicle. Formula (4) ensures that all cluster groups are not empty sets. Formula (5) means that each cluster group does not exceed the specified r stations, that is, each cluster group serves no more than r / 2 scheduling waves at a time.

[0100] Step e: Obtain the new centroid K’ of each cluster group as the set of geometric centers of the new cluster group, repeat step S4 to obtain the new clustering scheme y ik ’, and the new objective value obj’, and calculate the change rate of the objective value, that is, the absolute value of the change rate of obj’ relative to obj.

[0101] Step f: When the change rate of the objective value is greater than 0.01%, then assign y ik ’ to y ik , assign obj’ to obj, and K, to K, and repeat step e until the change rate of the objective value is less than or equal to 0.01%, then proceed to the next step.

[0102] Step g: Obtain the result result t′ of the t-th clustering test, that is, (obj, y ik , K) as result t′ .

[0103] Step h: Repeat steps c to g T times, and select the result t′ with the smallest obj as the optimal clustering result.

[0104] In an alternative embodiment, please refer to Figure 6 , step S50 includes steps S51 to S52, which are specifically as follows:

[0105] S51: Obtain the information of the warehouse points where the specified parked scheduling vehicles are located within the service range;

[0106] S52: Input the warehouse point information and the historical scheduling waves of several of the clustering groups into the vehicle routing problem mixed integer programming model to obtain the minimum necessary scheduling distance of the shared bicycles within each clustering group.

[0107] In the embodiment of the present application, the warehouse point information includes the warehouse site number, warehouse longitude, and warehouse latitude. The expression of the vehicle routing problem mixed integer programming model is as follows:

[0108] min ∑ i∈V，j∈V Rdist ij x ij (6)

[0109]

[0110]

[0111] x n+1，n+2 =0 (9)

[0112]

[0113]

[0114]

[0115]

[0116] U i -U j +(n + 1)·x ij ≤n,

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123] x ij ∈{0, 1} (20)

[0124] d i ∈Z (21)

[0125]

[0126] Among them, formula (6) is the objective function, which requires that the total route distance traveled by the scheduling vehicle is minimized on the premise of completing all scheduling wave tasks. Among them, Rdist ij represents the shortest actual road network distance from station V i to station V j . V is the set of all stations, identified by the serial numbers i and j, where i, j ∈ {1, 2,..., n, n + 1, n + 2}, including all stations involved in all scheduling waves and the warehouse points where the scheduling vehicles are parked. V n+1 and V n+2 are the starting point and the ending point of the scheduling route respectively. These two points may be the same station in reality, that is, the warehouse center. Formulas (7) and (8) ensure that the scheduling vehicle does not start from the end point (warehouse point) or end at the starting point (warehouse point). Among them, x ij = 1 means that the scheduling vehicle passes through the arc arc(i, j) between two stations i and j, and x ij = 0 means that the scheduling vehicle does not pass through the arc arc(i, j). Formula (9) ensures that the scheduling vehicle does not directly drive from the starting point (warehouse point) to the end point (warehouse point). Formulas (10) and (11) ensure that the vehicle starts from the starting point (warehouse point) and ends at the end point (warehouse point). Formulas (12) and (13) mean that all stations (excluding warehouse points) are visited exactly once, that is, the scheduling vehicle enters the station (excluding warehouse points) exactly once and exits the station (excluding warehouse points) exactly once. Among them, V\{n + 1} represents the remaining stations after removing V n+1 from all stations, and V\{n + 2} represents the remaining stations after removing V n+2 from all stations. Formulas (14) and (15) determine that the path of the scheduling vehicle is a Hamiltonian cycle, eliminating sub-cycles. Among them, U i represents the order in which station V i is visited, which is an auxiliary variable to assist in eliminating sub-cycles, and U i is greater than zero and is a real number. Formula (16) requires that the number of single bicycles carried by the scheduling vehicle does not exceed the capacity limit of the scheduling vehicle, and the number of single bicycles carried is not negative. Among them, C represents the capacity limit of the scheduling vehicle. w ij represents the number of single bicycles loaded by the scheduling vehicle during the journey from station V i to station V j . w ij is an integer, and w ij is greater than or equal to 0. Formula (17) means that the change in the single-bicycle load of the scheduling vehicle before and after entering the station is equal to the single-bicycle scheduling demand of the station. Formulas (18) and (19) mean that the scheduling vehicle will not take single bicycles out of or into the warehouse center, that is, the initial and final single-bicycle loads of the vehicle are both 0. Among them, V\{n + 1, n + 2} represents the remaining stations after removing Vn+1 and V n+2 for the remaining stations. Formulas (20) and (21) ensure that the section discrimination variable x ij is a 0-1 binary variable, ensuring that the single-bike demand for station scheduling is an integer variable, where d i represents the single-bike scheduling demand for station V i and d i is an integer. There is no single-bike scheduling demand at the warehouse center, that is, d n+1 and d n+2 are both 0. d i greater than 0 indicates that the station is the start station of the scheduling wave in the scheduling wave, and bikes need to be removed. d i being 0 indicates that the station has no scheduling demand, and other stations with such scheduling demands have been removed except for the warehouse center. d i less than 0 indicates that the station is the end station of the scheduling wave in the scheduling wave, and bikes need to be brought in. Formula (22) ensures that the scheduling vehicle needs to visit the start station (i.e., the bike-out station) of the scheduling wave before visiting the end station (i.e., the bike-in station) of the scheduling wave.

[0127] The solution process of the vehicle routing problem mixed integer programming model can be completed by using a planning solver to obtain the minimum necessary scheduling distance of shared bikes within each cluster.

[0128] In an alternative embodiment, the step S60 includes step S61, which is specifically as follows:

[0129] S61: Multiply the carbon emission intensity factor per unit driving distance of the scheduling vehicle by the minimum necessary scheduling distance, and use the product result as the carbon emissions of the shared bikes.

[0130] In the embodiments of the present application, the carbon emission intensity factor per unit driving distance of the scheduling vehicle is multiplied by the minimum necessary scheduling distance traveled by the scheduling vehicle when completing all historical scheduling waves within the cluster to obtain a product result, and the product result is the carbon emissions of the shared bikes.

[0131] The following is an embodiment of the device of the present application, which can be used to execute the content of the method in Embodiment 1 of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the content of the method in Embodiment 1 of the present application.

[0132] Please refer to Figure 7 , which shows a schematic structural diagram of a carbon emissions calculation device for shared bike scheduling provided by the embodiments of the present application. The carbon emissions calculation device 7 for shared bike scheduling provided by the embodiments of the present application includes:

[0133] A riding data acquisition module 71, configured to acquire historical riding data of shared bikes within the service range;

[0134] A scheduling record acquisition module 72, configured to obtain a historical scheduling record of a shared bicycle according to the historical riding data;

[0135] A scheduling wave acquisition module 73, configured to obtain a historical scheduling wave of a shared bicycle according to the historical scheduling record;

[0136] A clustering group acquisition module 74, configured to perform spatial geographical clustering on the historical scheduling waves to obtain a plurality of clustering groups;

[0137] A scheduling distance calculation module 75, configured to calculate the minimum necessary scheduling distance of the shared bicycles within each clustering group according to the historical scheduling waves of the plurality of clustering groups;

[0138] A carbon emission calculation module 76, configured to calculate the carbon emissions of the shared bicycle scheduling according to the carbon emission intensity factor per unit driving distance of the scheduling vehicle and the minimum necessary scheduling distance.

[0139] By applying the embodiment of the present application, by obtaining the historical riding data of the shared bicycle within the service range; obtaining the historical scheduling record of the shared bicycle according to the historical riding data; obtaining the historical scheduling wave of the shared bicycle according to the historical scheduling record; performing spatial geographical clustering on the historical scheduling waves to obtain a plurality of clustering groups; calculating the minimum necessary scheduling distance of the shared bicycles within each clustering group according to the historical scheduling waves of the plurality of clustering groups; and calculating the carbon emissions of the shared bicycle scheduling according to the carbon emission intensity factor per unit driving distance of the scheduling vehicle and the minimum necessary scheduling distance, the calculation accuracy of the carbon emissions of the shared bicycle scheduling is improved, and the accuracy and comprehensiveness of the environmental benefit evaluation of the entire shared bicycle service project are improved.

[0140] In an embodiment of the present application, the scheduling record acquisition module 72 includes:

[0141] A bicycle group acquisition unit, configured to group the historical riding data according to the riding bicycle numbers to obtain a plurality of bicycle groups; each bicycle group includes a plurality of riding orders;

[0142] A riding order sequence acquisition unit, configured to sort all the riding orders of each bicycle group in ascending order according to the riding start time to obtain a plurality of riding order sequences;

[0143] A scheduling record acquisition unit is used to traverse each ride order in each ride order sequence. If the ride end station number of the current ride order is different from the start station number of the next ride order, the ride end station number of the current ride order is used as the scheduling start station number of the historical scheduling record, the ride end time of the current ride order is used as the scheduling start time of the historical scheduling record, the ride start station number of the next ride order is used as the scheduling end station number of the historical scheduling record, the ride start time of the next ride order is used as the scheduling end time of the historical scheduling record, and the bike number of the next ride order is used as the scheduling bike number of the historical scheduling record, so as to obtain the historical scheduling record of the shared bike.

[0144] In an embodiment of the present application, the scheduling wave acquisition module 73 includes:

[0145] A scheduling chain group acquisition module is used to group the historical scheduling records according to the scheduling chain to obtain several scheduling chain groups;

[0146] A scheduling record sorting unit is used to sort all the historical scheduling records of each scheduling chain group in ascending order according to the scheduling start time and the scheduling end time, and obtain the maximum value of the scheduling start time and the minimum value of the scheduling end time of each scheduling chain group;

[0147] A scheduling time calculation unit is used to calculate the necessary scheduling time of the scheduling chain for each scheduling chain group;

[0148] A first scheduling record classification unit is used to traverse each scheduling chain group. If the minimum value of the scheduling end time of the current scheduling chain group is less than the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, the current scheduling chain group is split, and the historical scheduling records in the current scheduling chain group with a scheduling start time less than the maximum value of the scheduling start time are classified into a preset first scheduling chain group, and the historical scheduling records in the current scheduling chain group with a scheduling start time greater than or equal to the maximum value of the scheduling start time are classified into a preset second scheduling chain group;

[0149] The second scheduling record classification unit is used to obtain the maximum value of the scheduling start time and the minimum value of the scheduling end time of the first scheduling chain group. If the minimum value of the scheduling end time of the first scheduling chain group is less than the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, the first scheduling chain group is split. The historical scheduling records in the first scheduling chain group with a scheduling start time greater than or equal to the maximum value of the scheduling start time are classified into the preset second scheduling chain group until the minimum value of the scheduling end time of the first scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain. Then, the historical scheduling records of the first scheduling chain group are merged into a historical scheduling wave.

[0150] The scheduling chain group splitting unit is used to obtain the maximum value of the scheduling start time and the minimum value of the scheduling end time of the second scheduling chain group. If the minimum value of the scheduling end time of the second scheduling chain group is less than the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, the second scheduling chain group is split until the minimum value of the scheduling end time of the second scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain. Then, the historical scheduling records of the second scheduling chain group are merged into a historical scheduling wave until all the historical scheduling waves of the current scheduling chain group are determined.

[0151] The scheduling wave obtaining unit is used to traverse each scheduling chain group until all the historical scheduling waves of all the scheduling chain groups are determined, and obtain the historical scheduling waves of the shared bicycles.

[0152] In an embodiment of the present application, the scheduling time calculation unit includes:

[0153] The shortest distance unit is used to obtain the actual road network shortest distance of the scheduling chain corresponding to each scheduling chain group and the average speed of the scheduling vehicle.

[0154] The scheduling time obtaining unit is used to use the ratio of the actual road network shortest distance of the scheduling chain to the average speed of the scheduling vehicle as the necessary scheduling time of the scheduling chain for each scheduling chain group.

[0155] In an embodiment of the present application, the carbon emission calculation device 7 for shared bicycle scheduling includes:

[0156] The scheduling wave splitting module is used to traverse the number of shared bikes scheduled in each historical scheduling wave. If the number of shared bikes scheduled in the current historical scheduling wave is greater than the maximum capacity of the scheduling vehicle, the current historical scheduling wave is split into several scheduling waves; the number of shared bikes scheduled in any one of the scheduling waves from the first scheduling wave to the penultimate scheduling wave among the several scheduling waves is the maximum capacity of the scheduling vehicle, and the number of shared bikes scheduled in the last scheduling wave is the difference between the number of shared bikes scheduled in the current scheduling wave and the sum of the number of shared bikes scheduled in the remaining scheduling waves among the several scheduling waves.

[0157] In an embodiment of the present application, the clustering group obtaining module includes:

[0158] The site longitude and latitude obtaining unit is used to obtain the longitude and latitude of the scheduling start site and the longitude and latitude of the scheduling end site of each historical scheduling wave;

[0159] The clustering group obtaining unit is used to input the longitude and latitude of the scheduling start site and the longitude and latitude of the scheduling end site into the spatial geographical clustering mixed integer programming model to obtain the clustering group corresponding to each historical scheduling wave.

[0160] In an embodiment of the present application, the scheduling distance calculation module includes:

[0161] The warehouse point information obtaining unit is used to obtain the information of the warehouse points where the scheduling vehicles are parked within the service range;

[0162] The scheduling distance obtaining unit is used to input the warehouse point information and the historical scheduling waves of several clustering groups into the vehicle routing problem mixed integer programming model to obtain the minimum necessary scheduling distance of the shared bikes within each clustering group.

[0163] In an embodiment of the present application, the carbon emission calculation module includes:

[0164] The carbon emission obtaining unit is used to multiply the carbon emission intensity factor per unit driving distance of the scheduling vehicle by the minimum necessary scheduling distance, and the product result is used as the carbon emission of the shared bike.

[0165] The present application also provides an electronic device, including: a processor and a memory; wherein, the memory stores a computer program, and the computer program is adapted to be loaded and executed by the processor to perform the method steps of the above embodiments.

[0166] The above embodiments only represent several implementation manners of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and the present invention also intends to include these modifications and improvements.

Claims

1. A method for calculating the carbon emissions of shared bicycle scheduling, characterized in that, it includes the following steps: Obtain the historical riding data of shared bicycles within the service range; Obtain the historical scheduling records of shared bicycles according to the historical riding data; Obtain the historical scheduling waves of shared bicycles according to the historical scheduling records; Perform spatial geographical clustering on the historical scheduling waves to obtain several clustering groups; Calculate the minimum necessary scheduling distance of shared bicycles within each clustering group according to the historical scheduling waves of several clustering groups; wherein, obtain the warehouse point information of the designated parking and scheduling vehicles within the service range; input the warehouse point information and the historical scheduling waves of several clustering groups into the vehicle routing problem mixed integer programming model to obtain the minimum necessary scheduling distance of shared bicycles within each clustering group; Calculate the carbon emissions of shared bicycle scheduling according to the carbon emission intensity factor per unit driving distance of the scheduling vehicle and the minimum necessary scheduling distance; wherein, multiply the carbon emission intensity factor per unit driving distance of the scheduling vehicle by the minimum necessary scheduling distance, and the product result is used as the carbon emissions of shared bicycle scheduling.

2. The method for calculating the carbon emissions of shared bicycle scheduling according to claim 1, characterized in that, the historical riding data includes the riding bicycle number; the step of obtaining the historical scheduling records of shared bicycles according to the historical riding data includes: Group the historical riding data according to the riding bicycle number to obtain several bicycle groups; each bicycle group includes several riding orders; Arrange all the riding orders of each bicycle group in ascending order according to the riding start time to obtain several riding order sequences; Traverse each riding order in each riding order sequence. If the riding end station number of the current riding order is different from the start station number of the next riding order, use the riding end station number of the current riding order as the scheduling start station number of the historical scheduling record, use the riding end time of the current riding order as the scheduling start time of the historical scheduling record, use the riding start station number of the next riding order as the scheduling end station number of the historical scheduling record, use the riding start time of the next riding order as the scheduling end time of the historical scheduling record, and use the bicycle number of the next riding order as the scheduling bicycle number of the historical scheduling record to obtain the historical scheduling records of shared bicycles.

3. The method for calculating the carbon emissions of shared bicycle scheduling according to claim 1, characterized in that, the historical scheduling record includes a scheduling start station number and a scheduling end station number, and the scheduling start station number and the scheduling end station number form a scheduling chain; the step of obtaining the historical scheduling waves of shared bicycles according to the historical scheduling record includes: Group the historical scheduling records according to the scheduling chain to obtain several scheduling chain groups; Arrange all historical scheduling records of each of the said scheduling chain groups in ascending order according to the scheduling start time and the scheduling end time, and obtain the maximum value of the scheduling start time and the minimum value of the scheduling end time for each of the said scheduling chain groups; Calculate the necessary scheduling time of the scheduling chain for each of the said scheduling chain groups; Traverse each of the said scheduling chain groups. If the minimum value of the scheduling end time of the current scheduling chain group is less than the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, split the said current scheduling chain group. Classify the historical scheduling records in the current scheduling chain group with a scheduling start time less than the maximum value of the scheduling start time into a preset first scheduling chain group, and classify the historical scheduling records in the current scheduling chain group with a scheduling start time greater than or equal to the maximum value of the scheduling start time into a preset second scheduling chain group; Obtain the maximum value of the scheduling start time and the minimum value of the scheduling end time of the first scheduling chain group. If the minimum value of the scheduling end time of the first scheduling chain group is less than the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, split the first scheduling chain group. Classify the historical scheduling records in the first scheduling chain group with a scheduling start time greater than or equal to the maximum value of the scheduling start time into the preset second scheduling chain group until the minimum value of the scheduling end time of the first scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain. Then merge the historical scheduling records of the first scheduling chain group into one historical scheduling wave; Obtain the maximum value of the scheduling start time and the minimum value of the scheduling end time of the second scheduling chain group. If the minimum value of the scheduling end time of the second scheduling chain group is less than the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain, split the second scheduling chain group until the minimum value of the scheduling end time of the second scheduling chain group is greater than or equal to the sum of the maximum value of the scheduling start time and the necessary scheduling time of the scheduling chain. Then merge the historical scheduling records of the second scheduling chain group into one historical scheduling wave until all historical scheduling waves of the said current scheduling chain group are determined; Traverse each of the said scheduling chain groups until all historical scheduling waves of all scheduling chain groups are determined to obtain the historical scheduling waves of the shared bicycles.

4. The method for calculating the carbon emissions of shared bicycle scheduling according to claim 3, characterized in that, the step of calculating the necessary scheduling time of the scheduling chain for each of the said scheduling chain groups includes: Obtain the actual shortest road network distance of the scheduling chain corresponding to each of the said scheduling chain groups and the average speed of the scheduling vehicle; Take the ratio of the actual shortest road network distance of the scheduling chain to the average speed of the scheduling vehicle as the necessary scheduling time of the scheduling chain for each of the said scheduling chain groups.

5. The method for calculating the carbon emissions of shared bicycle scheduling according to claim 3, characterized in that, further comprising: Traverse the number of single bikes scheduled in each historical scheduling wave. If the number of single bikes scheduled in the current historical scheduling wave is greater than the maximum capacity of the scheduling vehicle, split the current historical scheduling wave into several scheduling waves; the number of single bikes scheduled in any one of the several scheduling waves from the first scheduling wave to the second-to-last scheduling wave is the maximum capacity of the scheduling vehicle, and the number of single bikes scheduled in the last scheduling wave is the difference between the number of single bikes scheduled in the current scheduling wave and the sum of the number of single bikes scheduled in the remaining scheduling waves among the several scheduling waves.

6. The method for calculating carbon emissions of shared bike scheduling according to claim 1, characterized in that the step of performing spatial geographical clustering on the historical scheduling waves to obtain several clustering groups includes: obtain the longitude and latitude of the scheduling start site and the longitude and latitude of the scheduling end site of each historical scheduling wave; input the longitude and latitude of the scheduling start site and the longitude and latitude of the scheduling end site into the spatial geographical clustering mixed integer programming model to obtain the clustering group corresponding to each historical scheduling wave.

7. A device for calculating carbon emissions of shared bike scheduling, characterized in that it includes: a riding data acquisition module for acquiring historical riding data of shared bikes within the service range; a scheduling record acquisition module for obtaining the historical scheduling records of shared bikes according to the historical riding data; a scheduling wave acquisition module for obtaining the historical scheduling waves of shared bikes according to the historical scheduling records; a clustering group acquisition module for performing spatial geographical clustering on the historical scheduling waves to obtain several clustering groups; a scheduling distance calculation module for calculating the minimum necessary scheduling distance of shared bikes within each clustering group according to the historical scheduling waves of the several clustering groups; wherein, obtain the information of the warehouse point for parking the scheduling vehicle specified within the service range; input the warehouse point information and the historical scheduling waves of the several clustering groups into the vehicle routing problem mixed integer programming model to obtain the minimum necessary scheduling distance of shared bikes within each clustering group; a carbon emissions calculation module for calculating the carbon emissions of shared bike scheduling according to the carbon emission intensity factor per unit driving distance of the scheduling vehicle and the minimum necessary scheduling distance; wherein, multiply the carbon emission intensity factor per unit driving distance of the scheduling vehicle by the minimum necessary scheduling distance, and the product result is used as the carbon emissions of shared bike scheduling.

8. A computer device, including: a processor, a memory, and a computer program stored in the memory and executable on the processor, characterized in that when the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

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