CN110334986B - Vehicle loading and unloading point identification method and device based on cargo measure - Google Patents

Vehicle loading and unloading point identification method and device based on cargo measure Download PDF

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CN110334986B
CN110334986B CN201910537900.2A CN201910537900A CN110334986B CN 110334986 B CN110334986 B CN 110334986B CN 201910537900 A CN201910537900 A CN 201910537900A CN 110334986 B CN110334986 B CN 110334986B
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李乐
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Jiqi Iot Technology Shanghai Co ltd
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Abstract

The application provides a vehicle loading and unloading point identification method and device based on a cargo measure side, and the method comprises the following steps: finding out basic information of the vehicle stop according to the query time; according to the vehicle parking information, the corresponding vehicle amount of the corresponding time is correlated to calculate the amount information of the current moment; according to the change information of the goods quantity and direction, a loading acceleration parameter is provided; establishing a cargo loading model according to the loading acceleration parameters, and describing basic information of loading and unloading cargos according to a cargo volume change curve; the method and the device can process the loading acceleration of the sensor through a time window, judge the second derivative of the cargo loading capacity, analyze the cargo loading change catastrophe point, and realize the identification of the starting point and the ending point of the loading and unloading of the vehicle by utilizing the cargo loading change catastrophe point.

Description

Vehicle loading and unloading point identification method and device based on cargo measure
Technical Field
The application relates to the field of data processing, in particular to a vehicle loading and unloading point identification method and device based on a cargo measuring party.
Background
At present, more goods loading and unloading points are identified by manually judging the goods loading and unloading process in the modes of loading and unloading videos, fixed-point card punching, manual statistics and the like; a logistics company needs to invest a large amount of manpower to work at variable time; meanwhile, the method has low analysis acceptability degree for modes such as fixed-point card punching of long-distance drivers, video monitoring, manual statistics and the like. Through the communication with the corresponding staff in the same line, the corresponding conclusion is drawn: the disadvantages of time consumption, high cost, easy error and the like are not very painful for logistics companies. The identification of the goods loading and unloading points by the logistics company is beneficial to the real-time monitoring of the whole process of the goods loading and unloading points, the goods are prevented from being misplaced and lost, and the accurate positioning of goods demands is facilitated. At present, the market adopts the goods locater to carry out the goods tracking to the location of goods mostly, but to the smallclothes goods, the price is very high goods, and the cost of positioning instrument is higher for whole freight cost is higher, and the customer is unable to accept, and the rate of accuracy is difficult to guarantee, and is inefficient.
Disclosure of Invention
Aiming at the problems in the prior art, the application provides a vehicle loading and unloading point identification method and device based on a cargo measure side, which can be used for carrying out time window processing on loading acceleration of a sensor, judging a second derivative of cargo loading, analyzing a cargo loading change catastrophe point, and realizing identification of a starting point and an ending point of vehicle loading and unloading by utilizing the cargo change catastrophe point.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the present application provides a vehicle loading and unloading point identification method based on a cargo measure, including:
finding out basic information of the vehicle stop according to the query time;
according to the vehicle parking information, the corresponding vehicle amount of the corresponding time is correlated to calculate the amount information of the current moment;
according to the change information of the goods quantity and direction, a loading acceleration parameter is provided;
and establishing a cargo loading model according to the loading acceleration parameters, and describing basic information of loading and unloading cargos according to the cargo volume change curve.
Firstly, finding out basic information of vehicle stop according to the query time comprises the following steps:
and collecting vehicle stopping point data and vehicle cargo load volume data, marking the data, filtering, compensating and performing data transformation on the data, and enhancing the data quantity and the robustness of model training.
Secondly, finding out basic information of the vehicle stop according to the query time comprises the following steps:
and judging the retention time, wherein the average value of the data analysis time in an interval of 85% is selected as the judgment of the retention time.
Then, the basic information of the vehicle stop is found according to the query time, and the basic information comprises the following steps:
and judging that the mutation of the event exists when the adjacent time difference exceeds 7 hours according to the time difference.
Finally, finding out the basic information of the vehicle stop according to the query time comprises the following steps:
the loading and unloading processes and the non-loading and unloading processes are identified,
wherein the load acceleration is calculated: load _ a ═ (volume)t-volumet-1)/t-(t-1)，
Merging acceleration: preprocessing acceleration data, and considering that no loading and unloading event occurs when the acceleration of the sum of 100 seconds of acceleration of a moving window is less than or equal to 0.035 and more than or equal to-0.035; pre-processing the loading acceleration to 0[1 ]]. According to the loading acceleration
Figure GDA0003424243460000021
Tagging data according to loading acceleration: and (3) loading stage: 1; no loading and unloading: 0; and (3) unloading: -1;
and (3) label combination: traversing the whole label sequence, setting the initial label as the first label, traversing the label sequence by taking 30min as a time window, counting the label sequence in the time window, marking the label sequence as the label of the time window by the number of the labels, if the label is not changed, not changing the label, and if the label is changed, changing the label.
Further, the finding out the basic information of the vehicle stop according to the query time further comprises:
and (3) error analysis: wherein | load _ a1The | is less than or equal to 0.035 can be adjusted;
identification of loading and unloading start and end:
calculating the second derivative of the load: load _ a _2 ═ (a)t-at-1) And/t- (t-1), wherein the point equal to zero is the loading trend changing point.
In a second aspect, the present application provides a cargo-volume-based vehicle loading/unloading point identification apparatus comprising:
the data preparation module is used for finding out basic information of the vehicle stop according to the query time;
the data association module is used for associating the vehicle volume corresponding to the corresponding time according to the vehicle stop information to calculate the volume information of the current moment;
the acceleration determining module is used for proposing loading acceleration parameters according to the change information of the cargo quantity;
and the model identification module is used for establishing a cargo loading model according to the loading acceleration parameters and describing basic information of loading and unloading cargos according to the cargo volume change curve.
Further, the data preparation module comprises:
and the data preprocessing unit is used for collecting vehicle stopping point data and vehicle cargo load amount data, marking the data, filtering, compensating and performing data transformation on the data, and enhancing the data amount and the robustness of model training.
In a third aspect, the present application provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the cargo volume based vehicle loading and unloading point identification method when executing the program.
In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for load side based vehicle handling point identification.
According to the technical scheme, the method and the device for identifying the vehicle loading and unloading points based on the cargo quantity side are used for judging the second derivative of the cargo loading quantity by processing the loading acceleration of the sensor through the time window, analyzing the cargo loading change catastrophe points and identifying the starting points and the ending points of the vehicle loading and unloading cargos by utilizing the cargo loading change catastrophe points.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a cargo-volume-based vehicle loading/unloading point identification method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a vehicle loading/unloading point identification device based on a cargo measure according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Considering that in the related technology, the identification of the goods loading and unloading points is more by means of loading and unloading video, fixed-point card punching, manual statistics and the like, and the goods loading and unloading process is judged manually; a logistics company needs to invest a large amount of manpower to work at variable time; meanwhile, the method has low analysis acceptability degree for modes such as fixed-point card punching of long-distance drivers, video monitoring, manual statistics and the like. Through the communication with the corresponding staff in the same line, the corresponding conclusion is drawn: the disadvantages of time consumption, high cost, easy error and the like are not very painful for logistics companies. The identification of the goods loading and unloading points by the logistics company is beneficial to the real-time monitoring of the whole process of the goods loading and unloading points, the goods are prevented from being misplaced and lost, and the accurate positioning of goods demands is facilitated. At present, market is to the location of goods, mostly adopt the goods locater to carry out the goods and track, but to the smallclothes goods, the price is not very high goods, positioning instrument's cost is higher, make whole freight cost higher, the customer is unable to accept, the rate of accuracy is difficult to guarantee, the problem of inefficiency, the application provides a vehicle loading and unloading point identification method and device based on goods volume side, through carrying out the time window to sensor loading acceleration and handling, judge goods loading second derivative, analysis goods loading change catastrophe point, utilize goods change catastrophe point, realize the beginning of vehicle loading and unloading goods, the identification of endpoint.
In order to process the loading acceleration of the sensor by a time window, judge the second derivative of the loading capacity of the goods, analyze a change mutation point of the loading capacity of the goods and recognize the starting point and the ending point of loading and unloading of the vehicle by utilizing the change mutation point of the goods, the application provides an embodiment of a vehicle loading and unloading point recognition method based on a goods quantity party, and referring to fig. 1, the vehicle loading and unloading point recognition method based on the goods quantity party specifically comprises the following contents:
step S101: and finding out basic information of the vehicle stop according to the query time.
Step S102: and according to the vehicle parking information, the corresponding vehicle amount of the corresponding time is correlated to calculate the amount information of the current moment.
Step S103: and providing a loading acceleration parameter according to the change information of the cargo quantity.
Step S104: and establishing a cargo loading model according to the loading acceleration parameters, and describing basic information of loading and unloading cargos according to the cargo volume change curve.
As can be seen from the above description, the method for identifying a loading and unloading point of a vehicle based on a cargo volume side, provided by the embodiment of the application, can perform time window processing on the loading acceleration of a sensor, determine the second derivative of the cargo loading volume, analyze the cargo loading change catastrophe point, and identify the starting point and the ending point of loading and unloading of the vehicle by using the cargo loading change catastrophe point.
The detailed steps are as follows:
(1) preparing data: in the data preprocessing stage, vehicle stopping point data and vehicle cargo load volume data are collected, as shown in the following figure, data are labeled, data are filtered, compensated and transformed, and the data quantity and the robustness of model training are enhanced.
a) And (3) determining the stay time:
according to the data distribution characteristics, only 75% of the area of the distributed data is needed, and in order to research more data characteristics, the mean value of the data analysis time in the 85% interval of the selected area is used as the judgment of the stay time:
820692*0.85＝697588.2；
finally, the residence time was confirmed to exceed 1059.66s, and therefore, the application is considered a precondition for residence in terms of a time greater than 1059.66 seconds;
b) and (3) judging the fluctuation abnormity:
and (4) carrying segment analysis that the carrying rates of a plurality of vehicles do not change. The normal range fluctuation is considered by 0.3 cubes above and below the fluctuation; therefore, the data can be preprocessed according to corresponding data changes in the data preprocessing stage;
judging the time window:
respectively analyzing 5 time windows, 6 time windows, 7 time windows, 8 time windows and 9 time windows;
analyzing the mutation points, wherein data loss exists, loading or unloading can not be known due to data loss of loading and unloading, and subdivision can be known; through the time difference, if the adjacent time difference exceeds 7 hours, the event mutation is considered, and the ' data transmission interruption is directly given, and the ' label cannot be analyzed ';
c) and identifying the loading and unloading processes and the non-loading and unloading processes.
Calculating the loading acceleration: load _ a ═ (volume)t-volumet-1)/t-(t-1)，
Merging acceleration: preprocessing acceleration data, and considering that no loading and unloading event occurs when the acceleration of the sum of 100 seconds of acceleration of a moving window is less than or equal to 0.035 and more than or equal to-0.035; its loading acceleration is preconditioned to 0.
According to the loading acceleration
Figure GDA0003424243460000051
(load_a1One hundred second acceleration); tagging data according to loading acceleration: and (3) loading stage: 1; no loading and unloading: 0; and (3) unloading: -1;
and (3) label combination: traversing the whole label sequence, setting an initial label as a first label, traversing the label sequence by taking 30min as a time window, counting the label sequence in the time window, recording the number of the labels as the labels of the time window, if the labels are not changed, not changing the labels, and if the labels are changed, changing the labels;
1) and (3) error analysis: wherein | load _ a1The | is less than or equal to 0.035 can be adjusted; according to the current threshold, the identification accuracy is 100%, but if other trucks need to be tested for many times; and (3) improving the identification of the start and the end of loading and unloading at the later stage:
2) calculating the second derivative of the load: load _ a _2 ═ (a)t-at-1) T- (t-1), the point of which is equal to zero being the loading trend change point;
a) loading is started: load _ a1> 0, load _ a _2 ═ 0, time of first occurrence;
b) and (4) finishing loading: load _ a1> 0, load _ a _2 equals 0 and load _ a within 60s1< 0, load _ a _2 ═ 0, time of first occurrence;
c) unloading is started: load _ a10, load _ a _2 is 0 and load _ a appears first within 15min1Time < 0, load _ a _2 ═ 0
d) And (4) finishing unloading: load _ a1< 0, load _ a _2 equals 0 and load _ a occurs first within 15min1> 0, load _ a _2 ═ 0 or load _ a1Time 0, and load _ a _2 0.
Loading is started: defined as the load continues to increase and for a period of time until no significant change in load occurs (load no more than 3 cubes in 600 seconds, the load is considered relatively stable) and the start of loading is considered to be at an acceleration greater than 0.29545 over a 5 time window.
In order to perform time window processing on the loading acceleration of the sensor, judge the second derivative of the load capacity, analyze the load change mutation point, and recognize the start and end points of loading and unloading of the vehicle by using the load change mutation point, the present application provides an embodiment of a load capacity party-based vehicle loading and unloading point recognition device for implementing all or part of the load capacity party-based vehicle loading and unloading point recognition method, and with reference to fig. 2, the load capacity party-based vehicle loading and unloading point recognition device specifically includes the following contents:
the data preparation module 10 is used for finding out basic information of vehicle stop according to the query time;
the data association module 20 is used for associating the vehicle volume corresponding to the corresponding time according to the vehicle staying information to calculate the volume information of the current moment;
the acceleration determining module 30 is used for providing loading acceleration parameters according to the change information of the cargo quantity;
and the model identification module 40 is used for establishing a cargo loading model according to the loading acceleration parameters and describing basic information of loading and unloading cargos according to the cargo volume change curve.
As can be seen from the above description, the cargo volume side-based vehicle loading and unloading point identification device provided in the embodiment of the present application can perform time window processing on the loading acceleration of the sensor, determine the second derivative of the cargo loading volume, analyze the cargo loading change catastrophe point, and implement identification of the starting point and the ending point of loading and unloading of the vehicle by using the cargo loading change catastrophe point.
In an embodiment of the application, the data preparation module includes:
and the data preprocessing unit is used for collecting vehicle stopping point data and vehicle cargo load amount data, marking the data, filtering, compensating and performing data transformation on the data, and enhancing the data amount and the robustness of model training.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the hardware + program class embodiment, since it is substantially similar to the method embodiment, the description is simple, and the relevant points can be referred to the partial description of the method embodiment.
The foregoing description has been directed to specific embodiments of this disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
Although the present application provides method steps as described in an embodiment or flowchart, additional or fewer steps may be included based on conventional or non-inventive efforts. The order of steps recited in the embodiments is merely one manner of performing the steps in a multitude of orders and does not represent the only order of execution. When an actual apparatus or client product executes, it may execute sequentially or in parallel (e.g., in the context of parallel processors or multi-threaded processing) according to the embodiments or methods shown in the figures.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a vehicle-mounted human-computer interaction device, a cellular telephone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The described embodiments may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and variations to the embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present specification should be included in the scope of the claims of the embodiments of the present specification.

Claims (6)

1. A vehicle loading and unloading point identification method based on a cargo measure, the method comprising:
collecting vehicle stopping point data and vehicle cargo load volume data, marking the data, filtering, compensating and performing data transformation on the data, and enhancing the data quantity and the robustness of model training;
judging the retention time, wherein the average value of the data analysis time in an interval of 85% is selected as the judgment of the retention time;
judging that the event mutation exists when the adjacent time difference exceeds 7 hours according to the time difference;
according to the vehicle parking information, the corresponding vehicle amount of the corresponding time is correlated to calculate the amount information of the current moment;
according to the change information of the goods quantity and direction, a loading acceleration parameter is provided;
and establishing a cargo loading model according to the loading acceleration parameters, and describing basic information of loading and unloading cargos according to the cargo volume change curve.
2. The method for identifying a loading/unloading point of a vehicle based on cargo volume according to claim 1, wherein the finding out basic information about the stop of the vehicle according to the query time comprises:
the loading and unloading processes and the non-loading and unloading processes are identified,
wherein the load acceleration is calculated: load _ a ═ (volume)t-volumet-1)/t-(t-1)，
Merging acceleration: preprocessing acceleration data, and considering that no loading and unloading event occurs when the acceleration of the sum of 100 seconds of acceleration of a moving window is less than or equal to 0.035 and more than or equal to-0.035; preprocessing the loading acceleration to 0, and then judging according to the loading acceleration
Figure FDA0003424243450000011
Tagging data according to loading acceleration: and (3) loading stage: 1; no loading and unloading: 0; and (3) unloading: -1;
and (3) label combination: traversing the whole label sequence, setting the initial label as the first label, traversing the label sequence by taking 30min as a time window, counting the label sequence in the time window, marking the label sequence as the label of the time window by the number of the labels, if the label is not changed, not changing the label, and if the label is changed, changing the label.
3. The method of claim 1, wherein the finding of the basic information about the stop of the vehicle according to the polling time further comprises:
and (3) error analysis: wherein | load _ a1The | is less than or equal to 0.035 can be adjusted;
identification of loading and unloading start and end:
calculating the second derivative of the load: load _ a _2 ═ (a)t-at-1) And t (t-1), wherein the point equal to zero is the loading trend changing point.
4. A vehicle loading and unloading point identification device based on a cargo measure, comprising:
the data preparation module is used for collecting vehicle stopping point data and vehicle cargo load amount data, marking the data, filtering, compensating and performing data transformation on the data, and enhancing the data amount and the robustness of model training; judging the retention time, wherein the average value of the data analysis time in an interval of 85% is selected as the judgment of the retention time; judging that the event mutation exists when the adjacent time difference exceeds 7 hours according to the time difference;
the data association module is used for associating the vehicle volume corresponding to the corresponding time according to the vehicle stop information to calculate the volume information of the current moment;
the acceleration determining module is used for proposing loading acceleration parameters according to the change information of the cargo quantity;
and the model identification module is used for establishing a cargo loading model according to the loading acceleration parameters and describing basic information of loading and unloading cargos according to the cargo volume change curve.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method for identifying a loading point of a vehicle based on a quantity of goods according to any one of claims 1 to 3 are carried out when the program is executed by the processor.
6. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method for load side based vehicle handling point identification according to any one of claims 1 to 3.
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