Logistics vehicle transportation efficiency index calculation method, device, electronic equipment and storage medium

Abstract

The present invention relates to the technical field of processing systems or methods for prediction purposes, and in particular to a method, device, electronic device and storage medium for calculating the transportation efficiency index of a logistics vehicle, wherein the method comprises: obtaining the transportation data of at least one target logistics vehicle; using the transportation data to cut the transportation itinerary of at least one target logistics vehicle to obtain multiple main itineraries and the stop data in each main itinerary; inputting the stop data into a pre-built stop type model to obtain the type of each stop point of at least one target logistics vehicle, and calculating the transportation efficiency index of at least one target logistics vehicle based on the type of each stop point. Thus, the technical problems in the related art that the manual statistical tasks are heavy, the labor cost is high, the POI point statistical rules are easily interfered by GPS errors, the misjudgment rate is high, and it is difficult to ensure the accuracy of the statistical data are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of processing systems or methods for prediction purposes, and in particular to a method, device, electronic equipment and storage medium for calculating a transportation efficiency index of a logistics vehicle. Background Art

[0002] Logistics and transportation are important pillars of my country's economic development. Their relevant indicators can directly reflect the development level of the transportation industry and provide important decision-making references for government departments to formulate relevant policies.

[0003] In the related technologies, the statistical methods of logistics vehicle transportation efficiency indicators (haul distance, empty load rate, etc.) are mainly divided into the following two types: manual statistics and POI (Point of Interest) statistics. Although manual statistics are intuitive, the task is heavy and the process is cumbersome. The POI statistical method is to obtain the surrounding geographic information by matching the vehicle's longitude and latitude information with the road network data to determine the status of the logistics vehicle and calculate related indicators. However, this method is easily affected by GPS (Global Positioning System) errors, has a high misjudgment rate, and is difficult to ensure the accuracy of statistical data.

[0004] In summary, in the related technologies, manual statistical tasks are arduous and labor costs are high. The POI point statistical rules are easily disturbed by GPS errors, the misjudgment rate is high, and it is difficult to ensure the accuracy of statistical data, which needs to be improved. Summary of the invention

[0005] The present invention provides a method, device, electronic device and storage medium for calculating the transport efficiency index of a logistics vehicle, so as to solve the technical problems in the related technology that the manual statistical task is heavy, the labor cost is high, the POI point statistical rule is easily disturbed by GPS errors, the misjudgment rate is high, and it is difficult to ensure the accuracy of the statistical data.

[0006] The first aspect of the embodiment of the present invention provides a method for calculating the transportation efficiency index of a logistics vehicle, comprising the following steps: obtaining transportation data of at least one target logistics vehicle; using the transportation data to cut the transportation itinerary of the at least one target logistics vehicle to obtain multiple main itineraries and stop data in each main itinerary; inputting the stop data into a pre-constructed stop type model to obtain the type of each stop point of the at least one target logistics vehicle, and calculating the transportation efficiency index of the at least one target logistics vehicle based on the type of each stop point.

[0007] Optionally, in one embodiment of the present invention, the use of the transportation data to cut the transportation itinerary of the at least one target logistics vehicle to obtain multiple main itineraries and stop data in each main itinerary includes: defining stop points and type classifications of the stop points based on preset standards; cutting the transportation itinerary of the at least one target logistics vehicle based on the definition of the stop points, the type classification and the transportation data to obtain the multiple main itineraries; and extracting the stop data from each main itinerary based on the definition of the stop points and the type classification.

[0008] Optionally, in one embodiment of the present invention, before inputting the stay data into a pre-constructed stay type model, it also includes: acquiring fleet transportation data of a logistics fleet that meets preset conditions, and obtaining a corresponding sample data set based on the fleet transportation data; obtaining an initial state probability matrix, a state transition probability matrix, and a stay duration-stay point type distribution matrix based on the sample data set; generating an observation probability matrix based on the stay duration-stay point type distribution matrix; and constructing the stay type model by combining the observation probability matrix, the initial state probability matrix, and the state transition probability matrix.

[0009] Optionally, in one embodiment of the present invention, the expression of the stay type model is:

[0010] ,

[0011] in, The first stop in the main itinerary is of stop type The probability of For stopover t For stay type The corrected probability of i,j The state transition probability matrix is ​​composed of state dwell types Transfer to stay type The probability of For stopover t For stay type The observation probability of For stopover t -1 is the stay type The probability of correction.

[0012] Optionally, in one embodiment of the present invention, the transportation efficiency index includes the transportation distance and empty load rate of the at least one target logistics vehicle.

[0013] Optionally, in one embodiment of the present invention, the transportation distance of the at least one target logistics vehicle is calculated based on the type of each stop point, and the empty load rate of the at least one target logistics vehicle is calculated based on the transportation distance, including: extracting the stop points whose stop point types are loading points and unloading points in all main trips; marking the process from the loading point to the unloading point as a transportation process, and marking the mileage of the transportation process as the transportation distance; and calculating the empty load rate based on the transportation distance and the total mileage of the logistics vehicle.

[0014] The second aspect of the embodiment of the present invention provides a device for measuring the transportation efficiency index of a logistics vehicle, including: a first acquisition module, used to obtain the transportation data of at least one target logistics vehicle; a cutting module, used to use the transportation data to cut the transportation itinerary of the at least one target logistics vehicle to obtain multiple main itineraries and stop data in each main itinerary; a first calculation module, used to input the stop data into a pre-constructed stop type model to obtain the type of each stop point of the at least one target logistics vehicle, and calculate the transportation efficiency index of the at least one target logistics vehicle based on the type of each stop point.

[0015] Optionally, in one embodiment of the present invention, the cutting module includes: a definition unit, used to define stay points and type classifications of the stay points based on preset standards; a cutting unit, used to cut the transportation itinerary of the at least one target logistics vehicle based on the definition of the stay points, the type classifications and the transportation data to obtain the multiple main itineraries; a first extraction unit, used to extract the stay data from each main itinerary based on the definition of the stay points and the type classifications.

[0016] Optionally, in one embodiment of the present invention, it also includes: a second acquisition module, used to acquire the fleet transportation data of the logistics fleet that meets the preset conditions, and obtain the corresponding sample data set based on the fleet transportation data; a second calculation module, used to obtain the initial state probability matrix, the state transition probability matrix and the stay duration-stay point type distribution matrix based on the sample data set; a generation module, used to generate an observation probability matrix based on the stay duration-stay point type distribution matrix; a construction module, used to combine the observation probability matrix, the initial state probability matrix and the state transition probability matrix to construct the stay type model.

[0017] Optionally, in one embodiment of the present invention, the expression of the stay type model is:

[0018] ,

[0019] in, The first stop in the main itinerary is of stop type The probability of For stopover t For stay type The corrected probability of i,j The state transition probability matrix is ​​composed of state dwell types Transfer to stay type The probability of For stopover t For stay type The observation probability of For stopover t -1 is the stay type The probability of correction.

[0020] Optionally, in one embodiment of the present invention, the transportation efficiency index includes the transportation distance and empty load rate of the at least one target logistics vehicle.

[0021] Optionally, in one embodiment of the present invention, the first calculation module includes: a second extraction unit, used to extract all stop points in the main trip whose stop point types are loading points and unloading points; a marking unit, used to mark the process from the loading point to the unloading point as a transportation process, and mark the mileage of the transportation process as the transportation distance; a calculation unit, used to calculate the empty load rate based on the transportation distance and the total mileage of the logistics vehicle.

[0022] A third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for calculating the transportation efficiency index of a logistics vehicle as described in the above embodiment.

[0023] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the method for calculating the transportation efficiency index of a logistics vehicle as described in the above embodiment.

[0024] A fifth aspect of the present invention provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for calculating the transportation efficiency index of a logistics vehicle.

[0025] The embodiment of the present invention can cut the transportation according to the transportation data of at least one target logistics vehicle, obtain multiple main trips of each target logistics vehicle and the stop data in each main trip, input the stop data into the pre-built stop type model, output the type of each stop point, and calculate the transportation efficiency index of at least one target logistics vehicle based on the type of each stop point. By statistically analyzing the transportation characteristics of the logistics vehicle and combining the actual operation data of the vehicle, the transportation status of the logistics vehicle can be accurately identified, and then the transportation efficiency index of the logistics vehicle can be effectively calculated, saving labor costs and facilitating promotion and application. Therefore, the technical problems in the related technology that the manual statistical tasks are heavy, the labor costs are high, the POI point statistical rules are easily interfered by GPS errors, the misjudgment rate is high, and it is difficult to ensure the accuracy of statistical data are solved.

[0026] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The above and / or additional aspects and advantages of the present invention will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0028] Figure 1 A flow chart of a method for calculating a transportation efficiency index of a logistics vehicle provided according to an embodiment of the present invention;

[0029] Figure 2 A flowchart of a method for calculating a transportation efficiency index of a logistics vehicle according to an embodiment of the present invention;

[0030] Figure 3 A schematic diagram of the distribution of transportation distances of logistics vehicles provided according to an embodiment of the present invention;

[0031] Figure 4 A schematic diagram of the structure of a transport efficiency index calculation device for a logistics vehicle according to an embodiment of the present invention;

[0032] Figure 5 It is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0033] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0034] The following describes the method, device, electronic device and storage medium for calculating the transportation efficiency index of a logistics vehicle of an embodiment of the present invention with reference to the accompanying drawings. In view of the technical problems that the manual statistical tasks are heavy, the labor costs are high, the POI point statistical rules are easily disturbed by GPS errors, the misjudgment rate is high, and it is difficult to ensure the accuracy of statistical data in the related technologies mentioned in the above background technology, the present invention provides a method for calculating the transportation efficiency index of a logistics vehicle, in which the transportation data of at least one target logistics vehicle can be cut according to the transportation data, and multiple main trips of each target logistics vehicle and the stay data in each main trip are obtained, the stay data are input into a pre-built stay type model, the type of each stay point is output, and the transportation efficiency index of at least one target logistics vehicle is calculated based on the type of each stay point, by statistically analyzing the transportation characteristics of the logistics vehicle, and combining the actual operation data of the vehicle, the transportation status of the logistics vehicle can be accurately identified, and then the transportation efficiency index of the logistics vehicle can be effectively calculated, saving labor costs and facilitating promotion and application. Thus, the technical problems that the manual statistical tasks are heavy, the labor costs are high, the POI point statistical rules are easily disturbed by GPS errors, the misjudgment rate is high, and it is difficult to ensure the accuracy of statistical data in the related technologies are solved.

[0035] Specifically, Figure 1 A flow chart of a method for calculating a transportation efficiency index of a logistics vehicle provided in an embodiment of the present invention.

[0036] like Figure 1 As shown, the method for calculating the transportation efficiency index of the logistics vehicle includes the following steps:

[0037] In step S101, transportation data of at least one target logistics vehicle is obtained.

[0038] During the actual execution process, the transportation data may include the actual driving data and stop data of the target logistics vehicle.

[0039] The actual driving data may include time, longitude, latitude, and vehicle speed, while the stop data may include the stop point, stop duration, and the like.

[0040] In step S102, the transportation itinerary of at least one target logistics vehicle is segmented using the transportation data to obtain a plurality of main itineraries and stop data in each main itinerary.

[0041] Furthermore, the embodiment of the present invention can pre-process the transportation data, divide the transportation of the target logistics vehicle into multiple main trips, and extract the stop points in each main trip to obtain the stop data, such as the stop duration and longitude and latitude of each stop point.

[0042] Optionally, in one embodiment of the present invention, the transport itinerary of at least one target logistics vehicle is cut using transportation data to obtain multiple main itineraries and stop data in each main itinerary, including: defining stop points and type classifications of stop points based on preset standards; cutting the transport itinerary of at least one target logistics vehicle based on the definition, type classification and transportation data of the stop points to obtain multiple main itineraries; and extracting stop data from each main itinerary based on the definition and type classification of the stop points.

[0043] In some embodiments, the embodiments of the present invention may first define the stop points and types of the stop points of the logistics vehicle, so as to subsequently extract the corresponding data content from the transportation data.

[0044] For example, when the moving distance of a logistics vehicle within a time period of 10 minutes or more is less than 0.5 kilometers, an embodiment of the present invention can identify the corresponding geographic range and data range as a stop point and stop data based on the latitude and longitude data of the logistics vehicle within the time period.

[0045] In addition, the starting point and the end point in the transportation process are also regarded as stop points. In the embodiment of the present invention, the stop points of the logistics vehicle can be divided into the following five states, namely:

[0046] S1: rest point; S2: rest point during loading; S3: loading point; S4: rest point during transportation; S5: unloading point.

[0047] In step S103, the stop data is input into a pre-constructed stop type model to obtain the type of each stop point of at least one target logistics vehicle, and the transportation efficiency index of at least one target logistics vehicle is calculated based on the type of each stop point.

[0048] The embodiment of the present invention can input the stay data into a pre-built stay type model, thereby outputting the type of each stay point, and thus obtaining a corresponding transportation efficiency index.

[0049] For example, the embodiment of the present invention can determine whether the logistics vehicle is in an empty state between two adjacent stop points according to the types of the two adjacent stop points, thereby determining the transportation efficiency.

[0050] Optionally, in one embodiment of the present invention, before inputting the stay data into the pre-built stay type model, it also includes: obtaining the fleet transportation data of the logistics fleet that meets the preset conditions, and obtaining the corresponding sample data set based on the fleet transportation data; obtaining the initial state probability matrix, the state transition probability matrix and the stay duration-stay point type distribution matrix based on the sample data set; generating the observation probability matrix based on the stay duration-stay point type distribution matrix; and building the stay type model by combining the observation probability matrix, the initial state probability matrix and the state transition probability matrix. The expression of the stay type model is:

[0051] ,

[0052] in, The first stop in the main itinerary is of stop type The probability of For stopover t For stay type The corrected probability of i,j The state transition probability matrix is ​​composed of state dwell types Transfer to stay type The probability of For stopover t For stay type The observation probability of For stopover t -1 is the stay type The probability of correction.

[0053] In the actual implementation process, the embodiment of the present invention can form a logistics fleet for providing sample data to record the driving conditions of the logistics fleet, wherein the preset conditions may be that the feedback data of the logistics vehicles in the logistics fleet are valid, the logistics vehicles do not have temporary suspension of operation and other working conditions, etc., and the specific settings can be made by technical personnel in this field according to actual needs, and no specific restrictions are made here.

[0054] If a logistics vehicle in a logistics fleet moves less than 1 kilometer for 24 consecutive hours or more, it is considered to have completed a main journey.

[0055] The embodiment of the present invention can count the status and duration of the stop points in each main itinerary to form a sample data set.

[0056] According to the sample data set, the embodiment of the present invention can count the state of the first stop point of each main trip to obtain the initial state probability matrix x ,in, x represents the probability of the vehicle being in each state at the first stop in the main trip. x iRepresents the first point in the main process as state S i probability.

[0057] .

[0058] Based on the sample data set, the embodiment of the present invention can also count the number of times the vehicle transitions between different stop point states to obtain a big data state transition probability matrix A.

[0059] A ,

[0060] Among them, the matrix elements a i,j Indicates the state S i Transfer to state S j The probability of a 12 Represents the probability that the stop state changes from rest to a rest point during loading.

[0061] Based on the stay duration of the stay point and the stay duration-stay point type distribution matrix, the embodiment of the present invention can generate an observation probability matrix B:

[0062] ,

[0063] ,

[0064] in, B t For stopover t The observation matrix of b j (O t ) Indicates a stop point t For stay type S j The observation probability of p i,j is an element in the stay duration-stay point type distribution matrix, representing the stay point duration in the first i When the stay time interval is S j probability.

[0065] The embodiment of the present invention can calculate the distribution probability of the constructed stay type of each stay point based on the state transition probability matrix and the observation matrix, find the type with the largest distribution probability of each stay point, obtain the optimal type sequence, and thus obtain the type of each stay point. The recursive formula is as follows:

[0066] ,

[0067] in, For stopover t For stay type The probability of correction.

[0068] Optionally, in one embodiment of the present invention, the transportation efficiency index includes the transportation distance and empty load rate of at least one target logistics vehicle.

[0069] Optionally, in one embodiment of the present invention, the transportation distance of at least one target logistics vehicle is calculated based on the type of each stop point, and the empty load rate of at least one target logistics vehicle is calculated based on the transportation distance, including: extracting the stop points whose stop point types are loading points and unloading points in all main trips; marking the process from the loading point to the unloading point as the transportation process, and marking the mileage of the transportation process as the transportation distance; and calculating the empty load rate based on the transportation distance and the total mileage of the logistics vehicle.

[0070] As a possible implementation method, the embodiment of the present invention can extract the stop points of loading point and unloading point in all main trips, mark the process from loading point to unloading point as the transportation process, and the mileage of the transportation process is the transportation distance. The transportation distance of each transportation process is counted to obtain the transportation distance distribution of the logistics vehicle.

[0071] Furthermore, the calculation formula for the empty load rate of logistics vehicles can be as follows:

[0072] ,

[0073] in, w is the empty truck rate, S 运距 is the transportation distance of each transportation process of the target logistics vehicle, S 里程 is the total mileage of the target logistics vehicle.

[0074] Combination Figure 2 and Figure 3 As shown, the working principle of the method for calculating the transportation efficiency index of a logistics vehicle according to an embodiment of the present invention is described in detail by taking an embodiment as an example.

[0075] The embodiment of the present invention can use the transportation data of 100 vehicles for one year as sample data to calculate the transportation efficiency index.

[0076] Specifically, Figure 2 As shown, the embodiment of the present invention may include the following steps:

[0077] Step S201: Statistics of logistics vehicle transportation characteristics based on logistics fleet sample data.

[0078] In the actual implementation process, the logistics vehicle transportation characteristics statistics can include the following steps:

[0079] Step S1: Definition of stop point and classification of status: If the logistics vehicle moves less than 0.5 km for 10 minutes or more, it is identified as a stop point. In addition, the starting point and the end point of the transportation process are also considered as stop points. The logistics vehicle stop points are divided into the following five states: rest point, rest point during loading, loading point, rest point during transportation, and unloading point.

[0080] Step S2: Logistics vehicle transportation data collection: Establish a logistics vehicle fleet and record the driving status of the logistics vehicle fleet. If the logistics vehicles in the logistics fleet move less than 1 km for 24 consecutive hours or more, it is considered to have completed a main trip. The status and duration of the stop points in each main trip are counted to form a logistics vehicle data set. This embodiment of the present invention collects transportation data of 100 vehicles for one year to form a sample data set.

[0081] Step S3: Initial state probability matrix statistics: Based on the sample data set, the state of the first stop point of each main trip is counted to obtain the initial state probability matrix x ,in, x Represents the probability that the vehicle is in each state at the first stop in the main trip. x i Represents the first point in the main process as state S i probability.

[0082] .

[0083] The final initial state probability matrix is ​​shown in Table 1.

[0084] Table 1

[0085]

[0086] Step S4: Big data state transition probability matrix statistics: Based on the sample data set, the number of vehicle transitions between different stop point states is counted to obtain the big data state transition probability matrix A.

[0087] ,

[0088] Among them, the matrix elements a i,j Indicates the state S i Transfer to state S j The probability of a 12 It represents the probability of the stop point state changing from rest to rest point during loading. The big data state transition probability matrix is:

[0089] ,

[0090] Step S5: Acquisition of the stay duration-stay point state matrix: Based on the sample data set, statistics are collected on the distribution of stay point states corresponding to different stay time segments to obtain the stay duration-stay point state matrix. The stay duration-stay point state matrix may be as shown in Table 2.

[0091] Table 2

[0092]

[0093] Step S202: Preprocessing of target logistics vehicle data.

[0094] Data preprocessing can include the following steps:

[0095] Step S1: Main trip cutting: extract the actual driving data of the target logistics vehicle, the data parameters include time, longitude, latitude, and vehicle speed. Cut the actual driving data into multiple main trips. The embodiment of the present invention extracts any target logistics vehicle for subsequent calculation.

[0096] Step S2: Stay point extraction and feature statistics: extract the stay points in each main itinerary, and count the stay duration and longitude and latitude of each stay point.

[0097] Step S203: Calculate the target logistics vehicle stop point type.

[0098] The calculation process may include the following steps:

[0099] Step S1: Obtaining the observation matrix. Based on the stay duration of the stay point and the stay duration-stay point type distribution matrix, the embodiment of the present invention can generate an observation probability matrix B:

[0100] ,

[0101] ,

[0102] in, B t For stopover t The observation matrix of b j (O t ) Indicates a stop point t For stay type S j The observation probability of p i,j is an element in the stay duration-stay point type distribution matrix, representing the stay point duration in the first iWhen the stay time interval is S j probability.

[0103] Step S2: Construction and solution of the HMM model of the logistics vehicle stop type. The embodiment of the present invention can calculate the distribution probability of the construction stop type of each stop point based on the state transition probability matrix and the observation matrix, find the type with the largest distribution probability of each stop point, obtain the optimal type sequence, and thus obtain the type of each stop point. The recursive formula is as follows:

[0104] ,

[0105] in, is the stop point t is the stop type The probability of correction.

[0106] The status sequence of this stop point is rest point, loading point, rest point, rest point, unloading point, and rest point.

[0107] Step S204: Statistics of transportation indicators of target logistics vehicles.

[0108] The target logistics vehicle transportation indicator statistics can include the following steps:

[0109] Step S1: Calculate the transportation distance of the target logistics vehicle. In this embodiment of the present invention, the stop points of the loading point and the unloading point in all the main trips can be extracted, and the process from the loading point to the unloading point is marked as the transportation process, where the mileage of the transportation process is the transportation distance. The transportation distance of each transportation process is calculated to obtain Figure 3 The transportation distance distribution of logistics vehicles is shown.

[0110] Step S2: Calculate the target logistics vehicle empty load rate. The calculation formula of the logistics vehicle empty load rate can be as follows:

[0111] ,

[0112] in, w is the empty truck rate, S 运距 is the transportation distance of each transportation process of the target logistics vehicle, S 里程 is the total mileage of the target logistics vehicle. The empty rate of the target logistics vehicle is 35.26%.

[0113] According to the method for calculating the transportation efficiency index of a logistics vehicle proposed in an embodiment of the present invention, the transportation data of at least one target logistics vehicle can be cut according to the transportation data, and multiple main trips of each target logistics vehicle and the stop data in each main trip can be obtained. The stop data is input into a pre-built stop type model, and the type of each stop point is output. Based on the type of each stop point, the transportation efficiency index of at least one target logistics vehicle is calculated. By statistically analyzing the transportation characteristics of the logistics vehicle and combining the actual operation data of the vehicle, the transportation status of the logistics vehicle can be accurately identified, and then the transportation efficiency index of the logistics vehicle can be effectively calculated, saving labor costs and facilitating promotion and application. Thus, the technical problems in the related technology that the manual statistical tasks are heavy, the labor costs are high, the POI point statistical rules are easily interfered by GPS errors, the misjudgment rate is high, and it is difficult to ensure the accuracy of statistical data are solved.

[0114] Next, the transport efficiency index calculation device for a logistics vehicle according to an embodiment of the present invention will be described with reference to the accompanying drawings.

[0115] Figure 4 It is a block diagram of a transport efficiency index calculation device for a logistics vehicle according to an embodiment of the present invention.

[0116] like Figure 4 As shown, the transport efficiency index measuring device 10 of the logistics vehicle includes: a first acquisition module 100, a cutting module 200 and a first calculation module 300.

[0117] Specifically, the first acquisition module 100 is used to acquire transportation data of at least one target logistics vehicle.

[0118] The cutting module 200 is used to cut the transportation itinerary of at least one target logistics vehicle using the transportation data to obtain multiple main itineraries and stop data in each main itinerary.

[0119] The first calculation module 300 is used to input the stop data into a pre-constructed stop type model to obtain the type of each stop point of at least one target logistics vehicle, and calculate the transportation efficiency index of at least one target logistics vehicle based on the type of each stop point.

[0120] Optionally, in one embodiment of the present invention, the cutting module 200 includes: a definition unit, a cutting unit and a first extraction unit.

[0121] The definition unit is used to define the stay points and the type classification of the stay points based on preset standards.

[0122] The cutting unit is used to cut the transportation itinerary of at least one target logistics vehicle based on the definition, type classification and transportation data of the stop point to obtain multiple main itineraries.

[0123] The first extraction unit is used to extract the stay data from each main trip based on the definition and type classification of the stay point.

[0124] Optionally, in one embodiment of the present invention, the transport efficiency index calculation device 10 of a logistics vehicle further includes: a second acquisition module, a second calculation module, a generation module and a construction module.

[0125] The second acquisition module is used to acquire the fleet transportation data of the logistics fleet that meets the preset conditions, and obtain the corresponding sample data set based on the fleet transportation data.

[0126] The second calculation module is used to obtain an initial state probability matrix, a state transition probability matrix and a stay duration-stay point type distribution matrix based on the sample data set.

[0127] A generation module is used to generate an observation probability matrix based on the stay duration-stay point type distribution matrix.

[0128] A building module is used to build a stay type model by combining the observation probability matrix, the initial state probability matrix and the state transition probability matrix.

[0129] Optionally, in one embodiment of the present invention, the expression of the stay type model is:

[0130] ,

[0131] in, The first stop in the main itinerary is of stop type The probability of For stopover t For stay type The corrected probability of i,j The state transition probability matrix is ​​composed of state stay types Transfer to stay type The probability of For stopover t For stay type The observation probability of For stopover t -1 is the stay type The probability of correction.

[0132] Optionally, in one embodiment of the present invention, the transportation efficiency index includes the transportation distance and empty load rate of at least one target logistics vehicle.

[0133] Optionally, in one embodiment of the present invention, the first calculation module 300 includes: a second extraction unit, a marking unit and a calculation unit.

[0134] The second extraction unit is used to extract all the stop points in the main itinerary whose stop point types are loading points and unloading points.

[0135] The marking unit is used to mark the process from the loading point to the unloading point as the transportation process, and mark the mileage of the transportation process as the transportation distance.

[0136] The calculation unit is used to calculate the empty load rate based on the transportation distance and the total mileage of the logistics vehicle.

[0137] It should be noted that the above explanation of the embodiment of the method for calculating the transportation efficiency index of a logistics vehicle is also applicable to the device for calculating the transportation efficiency index of a logistics vehicle of this embodiment, and will not be repeated here.

[0138] According to the transportation efficiency index calculation device of the logistics vehicle proposed in the embodiment of the present invention, the transportation data of at least one target logistics vehicle can be cut according to the transportation data, and multiple main trips of each target logistics vehicle and the stop data in each main trip can be obtained. The stop data is input into the pre-built stop type model, and the type of each stop point is output. The transportation efficiency index of at least one target logistics vehicle is calculated based on the type of each stop point. By statistically analyzing the transportation characteristics of the logistics vehicle and combining the actual operation data of the vehicle, the transportation status of the logistics vehicle can be accurately identified, and then the transportation efficiency index of the logistics vehicle can be effectively calculated, saving labor costs and facilitating promotion and application. Therefore, the technical problems in the related technology that the manual statistical tasks are heavy, the labor costs are high, the POI point statistical rules are easily interfered by GPS errors, the misjudgment rate is high, and it is difficult to ensure the accuracy of statistical data are solved.

[0139] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. The electronic device may include:

[0140] A memory 501 , a processor 502 , and a computer program stored in the memory 501 and executable on the processor 502 .

[0141] When the processor 502 executes the program, the method for calculating the transportation efficiency index of the logistics vehicle provided in the above embodiment is implemented.

[0142] Furthermore, the electronic device further comprises:

[0143] The communication interface 503 is used for communication between the memory 501 and the processor 502 .

[0144] The memory 501 is used to store computer programs that can be executed on the processor 502 .

[0145] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0146] If the memory 501, the processor 502 and the communication interface 503 are implemented independently, the communication interface 503, the memory 501 and the processor 502 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0147] Optionally, in a specific implementation, if the memory 501, the processor 502 and the communication interface 503 are integrated on a chip, the memory 501, the processor 502 and the communication interface 503 can communicate with each other through an internal interface.

[0148] The processor 502 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention.

[0149] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-mentioned method for calculating the transportation efficiency index of a logistics vehicle is implemented.

[0150] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the method for calculating the transportation efficiency index of a logistics vehicle provided in an embodiment of the present invention.

[0151] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0152] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "N" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0153] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present invention belong.

[0154] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wirings (electronic device), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0155] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0156] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0157] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0158] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for calculating the transport efficiency index of a logistics vehicle, characterized in that: The following steps are involved: Obtaining transportation data of at least one target logistics vehicle; Using the transportation data, the transportation itinerary of the at least one target logistics vehicle is segmented to obtain a plurality of main itineraries and stop data in each main itinerary; Acquire fleet transportation data of a logistics fleet that meets preset conditions, and obtain a corresponding sample data set based on the fleet transportation data; Based on the sample data set, an initial state probability matrix, a state transition probability matrix and a stay duration-stay point type distribution matrix are obtained; based on the stay duration-stay point type distribution matrix, an observation probability matrix is ​​generated; and a stay type model is constructed by combining the observation probability matrix, the initial state probability matrix and the state transition probability matrix; Wherein, the expression of the stay type model is: ， in, The first stop in the main itinerary is of stop type The probability of For stopover t For stay type The corrected probability of i,j The state transition probability matrix is ​​composed of state dwell types Transfer to stay type The probability of For stopover t For stay type The observation probability of For stopover t -1 is the stay type The probability of correction; The stop data is input into a pre-constructed stop type model to obtain the type of each stop point of the at least one target logistics vehicle, and the transportation efficiency index of the at least one target logistics vehicle is calculated based on the type of each stop point.

2. The method for calculating the transport efficiency index of a logistics vehicle according to claim 1, characterized in that: The method of using the transportation data to cut the transportation itinerary of the at least one target logistics vehicle to obtain a plurality of main itineraries and the stop data in each main itinerary includes: defining stay points and classification of types of the stay points based on preset criteria; Based on the definition of the stop point, the type classification and the transportation data, the transportation itinerary of the at least one target logistics vehicle is cut to obtain the plurality of main itineraries; The stop data is extracted from each of the main trips based on the definition of the stop point and the type classification.

3. The method for calculating the transport efficiency index of a logistics vehicle according to claim 2, characterized in that: The transportation efficiency index includes the transportation distance and empty load rate of the at least one target logistics vehicle.

4. The method for calculating the transport efficiency index of a logistics vehicle according to claim 3 is characterized in that: The calculating the transportation efficiency index of the at least one target logistics vehicle based on the type of each stop point includes: Extract all the stop points in the main itinerary whose stop point types are loading points and unloading points; Marking the process from the loading point to the unloading point as the transportation process, and marking the mileage of the transportation process as the transportation distance; The empty load rate is calculated based on the transportation distance and the total mileage of the logistics vehicle.

5. A device for calculating the transport efficiency index of a logistics vehicle, characterized in that: include: A first acquisition module, used to acquire transportation data of at least one target logistics vehicle; A cutting module, used to cut the transportation itinerary of the at least one target logistics vehicle using the transportation data to obtain a plurality of main itineraries and stop data in each main itinerary; A second acquisition module is used to acquire fleet transportation data of a logistics fleet that meets preset conditions, and obtain a corresponding sample data set based on the fleet transportation data; A second calculation module is used to obtain an initial state probability matrix, a state transition probability matrix and a stay duration-stay point type distribution matrix based on the sample data set; a generation module is used to generate an observation probability matrix based on the stay duration-stay point type distribution matrix; a construction module is used to construct a stay type model by combining the observation probability matrix, the initial state probability matrix and the state transition probability matrix; Wherein, the expression of the stay type model is: ， in, The first stop in the main itinerary is of stop type The probability of For stopover t For stay type The corrected probability of i,j The state transition probability matrix is ​​composed of state dwell types Transfer to stay type The probability of For stopover t For stay type The observation probability of For stopover t -1 is the stay type The probability of correction; The first calculation module is used to input the stop data into a pre-constructed stop type model to obtain the type of each stop point of the at least one target logistics vehicle, and calculate the transportation efficiency index of the at least one target logistics vehicle based on the type of each stop point.

6. The device for calculating the transport efficiency index of a logistics vehicle according to claim 5, characterized in that: The cutting module comprises: A definition unit, used for defining a stay point and a type classification of the stay point based on a preset standard; A cutting unit, configured to cut the transportation itinerary of the at least one target logistics vehicle based on the definition of the stop point, the type classification and the transportation data to obtain the plurality of main itineraries; An extraction unit is used to extract the stay data from each main trip based on the definition of the stay point and the type classification.

7. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for calculating the transportation efficiency index of a logistics vehicle as described in any one of claims 1 to 4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for calculating the transportation efficiency index of a logistics vehicle as described in any one of claims 1 to 4.

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