A recognition method for the loading and unloading of trucks

Abstract

The present application provides a method for identifying the loading and unloading of a freight truck. Among them, the method includes: obtaining the load weight value of the first target freight truck at each sampling moment at the target operation point; whenever the amount of data of the obtained load weight value reaches the data segmentation threshold, forming a data group; performing the following processing on the current data group: determining the effective loading and unloading data from the current data group; obtaining the first fitting straight line by fitting the multiple sampling moments and the corresponding multiple load weight values included in the effective loading and unloading data; determining the interval start time and the interval end time corresponding to the current data group according to the maximum and minimum values of the first fitting straight line; determining the actual loading and unloading start time and end time of the first target freight truck according to the interval start time and the interval end time. Through the present application, the effect of accurately identifying the loading and unloading process of the freight truck is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of data processing, and in particular, to a method for identifying the loading and unloading of freight trucks. Background Art

[0002] Currently, for loading and unloading scenarios with a long unloading time, external detection data such as GPS positioning data and the volume of goods in the vehicle are often used to identify loading and unloading events. However, when judging loading and unloading events through external detection data such as GPS positioning data and the volume of goods in the vehicle, the amount of calculation is large and the calculation time consumed is long; for loading and unloading scenarios with a short unloading time, if the same method as that for loading and unloading scenarios with a long unloading time is used to identify loading and unloading events, the recognition accuracy of loading and unloading events will be very low or the events cannot be recognized.

[0003] In addition, in the actual loading and unloading work of freight trucks, due to the poor working environment, situations such as weak signals and data transmission delays are very likely to occur, which in turn leads to large errors in the judgment of the loading and unloading of freight trucks. Summary of the Invention

[0004] In view of this, the purpose of the present application is to provide a method for identifying the loading and unloading of freight trucks, which can determine the actual loading and unloading time of the freight truck by fitting and analyzing the load weight values of the collected freight truck, so as to solve the problems in the prior art that the recognition accuracy of loading and unloading events with a short time is low and the judgment error of loading and unloading in areas with a poor working environment and weak signals is large, and achieve the effect of accurately identifying the loading and unloading process of the freight truck.

[0005] The embodiment of the present application provides a method for identifying the loading and unloading of freight trucks. The method includes: obtaining the load weight value of a first target freight truck at each sampling moment at a target operation point; whenever the amount of data of the obtained load weight value reaches a data segmentation threshold, forming a data group; performing the following processing on the current data group: determining the effective loading and unloading data from the current data group; obtaining a first fitting line by fitting a plurality of sampling moments and corresponding plurality of load weight values included in the effective loading and unloading data; determining the start time and end time of the interval corresponding to the current data group according to the maximum and minimum values of the first fitting line; determining the start time and end time of the actual loading and unloading of the first target freight truck according to the start time and end time of the interval.

[0006] Optionally, the step of determining the loading and unloading valid data from the current data group includes: for each sampling moment after the first sampling moment in the current data group, calculate the difference between the load value corresponding to this sampling moment and the load value corresponding to the previous sampling moment in sequence, so as to find the first sampling moment when the difference is greater than the first set threshold, and determine it as the first moment; for each sampling moment after the first moment in the current data group, calculate the difference between the load value corresponding to this sampling moment and the load value corresponding to the previous sampling moment in sequence, so as to find the first sampling moment when the difference is less than the second set threshold, and determine it as the second moment; determine the load values corresponding to the sampling moments between the first moment and the second moment as the loading and unloading valid data.

[0007] Optionally, the step of determining the interval start time and interval end time corresponding to the current data group according to the maximum and minimum values of the first fitting line includes: determining the first maximum load value and the first minimum load value in the loading and unloading valid data; substituting the first maximum load value into the first fitting line to obtain the first fitting time point; determining the interval start time corresponding to the current data group according to the first fitting time point; substituting the first minimum load value into the first fitting line to obtain the second fitting time point; determining the interval end time corresponding to the current data group according to the second fitting time point.

[0008] Optionally, the step of determining the interval start time corresponding to the current data group according to the first fitting time point includes: obtaining the load values corresponding to the first threshold number of sampling moments before the first fitting time point; determining the first target sampling moment corresponding to the second maximum load value from the load values corresponding to the first threshold number of sampling moments; determining the time point corresponding to the first target sampling moment as the interval start time.

[0009] Optionally, the step of determining the interval end time corresponding to the current data group according to the second fitting time point includes: obtaining the load values corresponding to the second threshold number of sampling moments after the second fitting time point; determining the second target sampling moment corresponding to the second minimum load value from the load values corresponding to the second threshold number of sampling moments; determining the time point corresponding to the second target sampling moment as the interval end time.

[0010] Optionally, the step of determining the actual loading / unloading start time and end time of the first target truck according to the interval start time and the interval end time includes: determining whether the current data group meets the merging condition; if it is determined that the current data group does not meet the merging condition, then determining the interval start time as the actual loading / unloading start time of the first target truck, and determining the interval end time as the actual loading / unloading end time of the first target truck; if it is determined that the current data group meets the merging condition, then updating the stored record end time with the interval end time, determining the stored record start time as the actual loading / unloading start time of the first target truck, and determining the updated record end time as the actual loading / unloading end time of the first target truck.

[0011] Optionally, the step of determining whether the current data group meets the merging condition includes: determining the first slope value of the first fitting line corresponding to the current data group; determining the second slope value of the second fitting line corresponding to the previous data group; according to the first slope value and the second slope value, determining whether the data change trends of the current data group and the previous data group are consistent, and determining whether the first time interval between the interval end time of the previous data group and the interval start time of the current data group is less than a preset interval threshold; if the data change trends are inconsistent, and / or, the first time interval is greater than the preset interval threshold, then determining that the current data group does not meet the merging condition; if the data change trends are consistent, and the first time interval is not greater than the preset interval threshold, then determining that the current data group meets the merging condition.

[0012] Optionally, it further includes: determining the actual loading / unloading duration of the first target truck according to the actual loading / unloading start time and end time of the first target truck; judging whether the actual loading / unloading duration of the first target truck exceeds a preset loading / unloading duration threshold; if the actual loading / unloading duration exceeds the preset loading / unloading duration threshold, then generating a loading / unloading abnormal alarm message, where the loading / unloading abnormal alarm message includes at least one of the following items: a vehicle identifier for indicating the first target truck, a location identifier for indicating the location of the first target truck, a first time identifier for indicating the loading / unloading duration of the first target truck; and / or, it further includes: detecting whether the first target truck has left its target operation area, where the target area includes at least one target operation point; if the first target truck has left the target area, then generating a driving abnormal alarm message, where the driving abnormal alarm message includes at least one of the following items: a vehicle identifier for indicating the first target truck, a location identifier for indicating the location of the first target truck, a second time identifier for indicating the moment when the first target truck leaves.

[0013] Optionally, it further includes: determining a first arrival time when a second target truck among at least one truck waiting for an operation at a target operation point arrives at the target operation point; determining a first estimated departure time when the second target truck departs from the target operation point; calculating a second time interval between the first arrival time and the first estimated departure time; if the second time interval is not less than the estimated loading and unloading duration, sending a first instruction message to the second target truck, where the estimated loading and unloading duration is a first time difference between a time point corresponding to the last sampling moment and a time point corresponding to the first sampling moment in a data group, and the first instruction message is used to instruct that the second target truck is allowed to perform loading and unloading operations at the target operation point; if the second time interval is less than the estimated loading and unloading duration, sending a second instruction message to the second target truck, and the second instruction message is used to instruct that the second target truck is prohibited from performing loading and unloading operations at the target operation point.

[0014] Optionally, it further includes: for a target operation point, determining whether there are other trucks performing loading and unloading operations after the second target truck arrives at the target operation point; if there are no other trucks performing loading and unloading operations, when the second time interval is not less than the estimated loading and unloading duration, sending a first instruction message to the second target truck; if there are other trucks performing loading and unloading operations, calculating a second time difference between the second time interval and the actual loading and unloading duration of the other trucks; if the second time difference is not less than the estimated loading and unloading duration, sending a first instruction message to the second target truck; if the second time difference is less than the estimated loading and unloading duration, sending a second instruction message to the second target truck.

[0015] The method for identifying the loading and unloading of trucks provided by the embodiments of the present application can determine the actual loading and unloading duration of trucks through data fitting and analysis of the load values of trucks, and can solve the problems of low recognition accuracy for loading and unloading events with short time and large judgment errors for loading and unloading in areas with poor working environments and weak signals in the prior art, achieving the effect of accurately identifying the loading and unloading process of trucks.

[0016] To make the above objects, features, and advantages of the present application more obvious and understandable, the following specifically enumerates preferred embodiments and, in conjunction with the accompanying drawings, makes a detailed description as follows. Description of the Drawings

[0017] To more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required to be used in the embodiments. It should be understood that the following drawings only show some embodiments of the present application and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0018] Figure 1A flowchart of a method for identifying the loading and unloading of a freight truck provided by an embodiment of the present application;

[0019] Figure 2 A flowchart of another method for identifying the loading and unloading of a freight truck provided by an embodiment of the present application. Detailed implementation manners

[0020] To make the objectives, 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 accompanying drawings in the embodiments of the present application. Apparently, the described embodiments are only some, but not all, of the embodiments of the present application. The components of the embodiments of the present application described and illustrated herein generally may be arranged and designed in a variety of different configurations. Therefore, the detailed description of the embodiments of the present application provided herein is not intended to limit the scope of the claimed present application, but is merely representative of selected embodiments of the present application. Based on the embodiments of the present application, every other embodiment obtained by those skilled in the art without creative efforts shall fall within the protection scope of the present application.

[0021] First, an applicable application scenario of the present application is introduced. The present application can be applied to determining the loading and unloading time of a freight truck performing loading and unloading work in a loading and unloading area.

[0022] It has been found through research that in the current methods for identifying freight truck loading and unloading data, parameters such as the loading rate and loading acceleration of the vehicle are calculated relying on the driving data of the moving vehicle, the loading volume of the vehicle, the loading data, etc., which greatly increases the computing amount of the server and reduces the computing efficiency. As a result, the efficiency of judging the loading and unloading time of the freight truck is extremely low and the judgment of the actual loading and unloading time of the freight truck is not accurate enough.

[0023] Based on this, the embodiments of the present application provide a method for identifying the loading and unloading of a freight truck to accurately identify loading and unloading events with short time, and can also accurately identify the loading and unloading process in areas with poor working environments and weak signals.

[0024] Please refer to Figure 1 , Figure 1 A flowchart of a method for identifying the loading and unloading of a freight truck provided by an embodiment of the present application. As Figure 1 shown in

[0025] S101. Obtain the load value of the first target freight truck at each sampling moment at the target operation point.

[0026] In this step, the server obtains the load weight value of the first target truck at each sampling moment at the target operation point according to the load weight value collected by the load weight data collection device on the truck and the collection time interval of the load weight value.

[0027] Exemplarily, it may include, but is not limited to, a weight sensor and a vehicle-mounted controller provided on the truck. The weight sensor is used to detect the load weight value of the goods carried by each truck, and the vehicle-mounted controller uploads the load weight value detected by the weight sensor to the server.

[0028] It should be noted that the load weight data collection device on the truck and the server usually transmit data wirelessly. In the case of weak communication signals, there may be a situation where the load weight value is sent with a delay. In the case of delayed data transmission, this application can still analyze and process the load weight value data received with a delay at each sampling moment to determine the actual start time and actual end time of loading and unloading of the truck.

[0029] In a preferred embodiment, after obtaining the load weight value of the truck, the server also needs to clean the obtained load weight value of the truck. For example, the range of the load weight value of the truck is from 0 ton to 30 tons. Due to the detection error easily generated by the truck during loading and unloading or running, the load weight detection device on the truck may detect a load weight value less than 0 ton or greater than 30 tons. When the server obtains data greater than 30 tons or less than 0 ton, the load weight value is cleaned. The load weight value less than 0 ton is recorded as 0 ton, and the load weight value greater than 30 tons is recorded as 30 tons. Subsequently, analysis and processing can be performed based on the load weight value after data cleaning.

[0030] S102. Whenever the data volume of the obtained load weight value reaches the data segmentation threshold, a data group is formed.

[0031] In this step, the server segments the obtained load weight value according to the data segmentation threshold to form a data group.

[0032] Exemplarily, the above data segmentation threshold can be determined according to the historical loading and unloading duration of the truck at the target operation point. Due to the differences between the loading and unloading equipment at each operation point and the differences between different types of trucks, the loading and unloading durations of different types of trucks at different operation points are different.

[0033] Based on this, in step S102, the target type to which the first target truck belongs can be determined first, and then multiple historical loading and unloading durations of trucks of this target type at the target operation point can be obtained, and the target value determined based on the multiple historical loading and unloading durations is used to determine the data segmentation threshold. Exemplarily, the target value may include, but is not limited to: the average value, maximum value, minimum value, and median value of the multiple historical loading and unloading durations.

[0034] Taking the maximum value of the multiple historical loading and unloading durations as the target value as an example, the data volume of the load weight values collected within this maximum historical loading and unloading duration can be determined as the data segmentation threshold. For example, if there are 65 sampling moments within this maximum historical loading and unloading duration, the data segmentation threshold can be determined as 65 pieces of data. Here, if the number of sampling moments included within this maximum historical loading and unloading duration is not an integer, the method of rounding up can be used to determine the number of sampling moments. This application is not limited to this, and the number of sampling moments can also be determined by other means.

[0035] In a preferred embodiment, a data segmentation threshold can be determined based on the multiple historical loading and unloading durations of trucks of this target type at the target operation point, so that a data group can include the complete loading and unloading stage of the truck.

[0036] For each data group formed after the current segmentation by the server, the following steps S103 to S106 can be executed.

[0037] S103. Determine the valid loading and unloading data from the current data group.

[0038] In this step, the server confirms the load weight values of each sampling moment in the current data group and identifies the valid sampling data in the current data group.

[0039] For example, the step of determining the valid loading and unloading data from the current data group may include: for each sampling moment after the first sampling moment in the current data group, calculate the difference between the load weight value corresponding to this sampling moment and the load weight value corresponding to the previous sampling moment in turn to find the first sampling moment whose difference is greater than the first set threshold, and determine it as the first moment; for each sampling moment after the first moment in the current data group, calculate the difference between the load weight value corresponding to this sampling moment and the load weight value corresponding to the previous sampling moment in turn to find the first sampling moment whose difference is less than the second set threshold, and determine it as the second moment; determine the load weight values corresponding to the sampling moments between the first moment and the second moment as the valid loading and unloading data.

[0040] Through the above processing process, when the difference between the load values corresponding to two adjacent sampling times is not greater than the first set threshold or less than the second set threshold, it can be considered that the load values collected at adjacent sampling times are relatively close and there is no obvious change. For such data, this application does not process it. In this application, variable data is screened out from the current data group, and the variable data is analyzed and processed.

[0041] Here, the first set threshold and the second set threshold may be the same or different. Those skilled in the art can set the magnitudes of the first set threshold and the second set threshold according to their own needs.

[0042] S104. By fitting multiple sampling times and corresponding multiple load values included in the effective loading and unloading data, a first fitting straight line is obtained.

[0043] In this step, various existing straight line fitting methods can be used to fit multiple sampling times and corresponding multiple load values in the current data group to obtain the first fitting straight line.

[0044] As an example, the first fitting straight line can be expressed by the following formula:

[0045] y = kx + b (1)

[0046] In formula (1), y represents the load value, x represents the sampling time, k represents the slope of the first fitting straight line, and b represents the intercept of the first fitting straight line.

[0047] In the embodiment of this application, the values of the slope k and the intercept b in the first fitting straight line can be obtained by fitting multiple sampling times and corresponding multiple load values, so as to obtain the expression of the first fitting straight line.

[0048] S105. According to the extreme values of the first fitting straight line, the interval start time and the interval end time corresponding to the current data group are determined.

[0049] In this step, the extreme values of the first fitting straight line may include the first maximum load value and the first minimum load value in the effective loading and unloading data. Substitute the above extreme values into the expression of the first fitting straight line respectively to obtain the corresponding fitting time points, and then determine the interval start time and the interval end time corresponding to the current data group based on the obtained fitting time points.

[0050] Specifically, the steps of determining the start time and end time of the interval corresponding to the current data group according to the maximum and minimum values of the first fitting line may include: determining the maximum value of the first load weight and the minimum value of the first load weight in the effective loading and unloading data; substituting the maximum value of the first load weight into the first fitting line to obtain the first fitting time point; determining the start time of the interval corresponding to the current data group according to the first fitting time point; substituting the minimum value of the first load weight into the first fitting line to obtain the second fitting time point; determining the end time of the interval corresponding to the current data group according to the second fitting time point.

[0051] For example, substituting the maximum value of the first load weight and the minimum value of the first load weight into the expression of the first fitting line of the above formula (1) respectively, we get:

[0052]

[0053] In formula (2), y1 represents the maximum value of the first load weight, y2 represents the minimum value of the first load weight, x1 represents the first fitting time point, and x2 represents the second fitting time point.

[0054] Here, after determining the first fitting time point and the second fitting time point, the first fitting time point can be directly determined as the start time of the interval corresponding to the current data group, and the second fitting time point can be determined as the end time of the interval corresponding to the current data group. However, the above determination method is not accurate enough. For this reason, the present application proposes a preferred embodiment for determining the start time and end time of the interval corresponding to the current data group.

[0055] For example, the steps of determining the start time of the interval corresponding to the current data group according to the first fitting time point may include: obtaining the load weight values corresponding to the first threshold number of sampling moments before the first fitting time point; determining the first target sampling moment corresponding to the second maximum load weight value from the load weight values corresponding to the first threshold number of sampling moments; determining the time point corresponding to the first target sampling moment as the start time of the interval.

[0056] The steps of determining the end time of the interval corresponding to the current data group according to the second fitting time point may include: obtaining the load weight values corresponding to the second threshold number of sampling moments after the second fitting time point; determining the second target sampling moment corresponding to the second minimum load weight value from the load weight values corresponding to the second threshold number of sampling moments; determining the time point corresponding to the second target sampling moment as the end time of the interval.

[0057] Exemplarily, the first quantity threshold and the second quantity threshold may be the same or different. Preferably, the first quantity threshold and the second quantity threshold may be one-fourth of the data segmentation threshold. Alternatively, the first quantity threshold may also be greater than the second quantity threshold. For example, the first quantity threshold may be selected as 12, and the second quantity threshold may be selected as 8.

[0058] In this way, the accuracy of determining the start time and end time of the interval can be ensured.

[0059] S106. Determine the actual loading and unloading start time and end time of the first target truck according to the interval start time and the interval end time.

[0060] In this step, the server determines whether the interval start time and the interval end time corresponding to adjacent data groups need to be merged based on the comparison between adjacent data groups, and determines the actual loading and unloading start time and end time of the first target truck based on the merge judgment result.

[0061] Specifically, the step of determining the actual loading and unloading start time and end time of the first target truck according to the interval start time and the interval end time may include: determining whether the current data group meets the merge condition; if it is determined that the current data group does not meet the merge condition, determining the interval start time as the actual loading and unloading start time of the first target truck, and determining the interval end time as the actual loading and unloading end time of the first target truck; if it is determined that the current data group meets the merge condition, updating the stored record end time with the interval end time, determining the stored record start time as the actual loading and unloading start time of the first target truck, and determining the updated record end time as the actual loading and unloading end time of the first target truck.

[0062] In a preferred embodiment, the step of determining whether the current data group meets the merge condition may include: determining the first slope value of the first fitting line corresponding to the current data group; determining the second slope value of the second fitting line corresponding to the previous data group; according to the first slope value and the second slope value, determining whether the data change trends of the current data group and the previous data group are consistent, and determining whether the first time interval between the interval end time of the previous data group and the interval start time of the current data group is less than a preset interval threshold; if the data change trends are inconsistent, and / or the first time interval is greater than the preset interval threshold, determining that the current data group does not meet the merge condition; if the data change trends are consistent and the first time interval is not greater than the preset interval threshold, determining that the current data group meets the merge condition.

[0063] Here, the second slope value, the interval start time, and the interval end time of the previous data group are recorded locally in the server, and can be retrieved by the server when the current data group requires the data of the previous data group.

[0064] In the embodiment of the present application, based on the first slope value of the first fitting line corresponding to the current data group and the second slope value of the second fitting line corresponding to the previous data group, it can be determined whether the data change trends of the two data groups are consistent. If the first slope value and the second slope value are both positive or both negative, it is determined that the data change trends of the two data groups are consistent. If the first slope value and the second slope value are one positive and one negative, it is determined that the data change trends of the two data groups are inconsistent.

[0065] Optionally, if the current data group does not meet the merging condition, it indicates that the current data group and the previous data group belong to different loading and unloading events. The server then determines that the current data does not need to be merged, and the interval start time and the interval end time are the actual start time and the actual end time of this loading and unloading time. If the current data group meets the merging condition, it indicates that the current data group and the previous data group belong to the same loading and unloading event. At this time, merging is required to determine the start time and the end time of this loading and unloading event.

[0066] In this way, it can be ensured that the start time and the end time of the complete loading and unloading event are determined.

[0067] Optionally, the method for identifying the loading and unloading of a freight truck in the embodiment of the present application can also perform alarm monitoring on the loading and unloading process of the freight truck.

[0068] For example, according to the actual start time and the actual end time of the loading and unloading of the first target freight truck, the actual loading and unloading duration of the first target freight truck is determined; it is judged whether the actual loading and unloading duration of the first target freight truck exceeds a preset loading and unloading duration threshold; if the actual loading and unloading duration exceeds the preset loading and unloading duration threshold, a loading and unloading abnormal alarm message is generated. The loading and unloading abnormal alarm message includes at least one of the following items: a vehicle identifier for indicating the first target freight truck, a location identifier for indicating the location where the first target freight truck is located, and a first time identifier for indicating the loading and unloading duration of the first target freight truck. In this way, when the loading and unloading duration of the freight truck exceeds the maximum duration limit, the server can alarm this event to prompt the staff to check whether there is an abnormality in the freight truck or the loading and unloading point.

[0069] Optionally, it is detected whether the first target freight truck has left its target area for operation. The target area includes at least one target operation point; if the first target freight truck has left the target area, a driving abnormal alarm message is generated. The driving abnormal alarm message includes at least one of the following items: a vehicle identifier for indicating the first target freight truck, a location identifier for indicating the location where the first target freight truck is located, and a second time identifier for indicating the moment when the first target freight truck leaves. Among them, the location identifier transmits positioning data to the server by the positioning device of the freight truck.

[0070] In this way, the server can monitor the driving range of the truck and manage the truck in a refined manner.

[0071] The method for identifying the loading and unloading of a truck provided by the embodiment of the present application can determine the actual loading and unloading duration of the truck by performing data fitting and analysis on the load data collected in real time by the truck, and can solve the problems existing in the prior art, such as low recognition accuracy for loading and unloading events with short time and large judgment errors for loading and unloading in areas with poor working environments and weak signals, and achieve the effect of accurately identifying the loading and unloading process of the truck.

[0072] Please refer to Figure 2 , Figure 2 which is a flowchart of the method for identifying the loading and unloading of a truck provided by another embodiment of the present application. As Figure 2 shown in

[0073] S201. Calculate the time time_need required for loading and unloading.

[0074] Here, time_need is determined according to the historical loading and unloading duration of the truck at this loading and unloading target operation point. In a preferred embodiment, the time_need can be the duration of a data group corresponding to the data segmentation threshold, that is, the duration from the first sampling moment to the last sampling moment of a data group.

[0075] In this way, the server can estimate the time required for the truck to reach the target operation point for loading and unloading.

[0076] S202. Calculate the idle time delta between A_start and A_end of the second target truck.

[0077] In this step, the server calculates the idle time delta of the second target truck between the first arrival time A_start and the first estimated departure time A_end.

[0078] Here, the server determines the possible stay time of the second target truck at the target operation point according to the historical data of the second target truck, and the server determines the first estimated departure time of the second target truck according to the first arrival time and the estimated stay time of the second target truck reaching the target operation point.

[0079] In this way, the server can calculate the idle time delta of the second target truck between the first arrival time and the first estimated departure time.

[0080] After determining the second time interval, execute step S203. Determine whether the idle time delta of the second target truck is greater than the time time_need required for loading and unloading.

[0081] If the idle time delta of the second target truck is greater than the time time_need required for loading and unloading, then step S205 is executed: issue the first instruction message to the second target truck. At the same time, calculate the idle time gap between B_start and B_end of the next truck.

[0082] In this way, after receiving the first instruction message, the second target truck waits for the loading and unloading operation to be performed.

[0083] Among them, the first instruction message is used to indicate that the second target truck is allowed to perform loading and unloading operations at the target operation point.

[0084] If the second time interval is not greater than the estimated unloading duration, then step S205 is executed: the task cannot be completed.

[0085] In this way, when the server determines that the idle time of the second target truck that enters the target operation point earliest cannot complete the loading and unloading task, it is considered that this task cannot be completed.

[0086] After calculating the idle time gap between B_start and B_end of the next truck, execute step S206: determine whether gap is greater than time_need.

[0087] If gap is greater than time_need, then execute step S210: assign the gap idle time slice between B_start and B_end to the current vehicle. In this way, the next truck can continue to perform the loading and unloading task.

[0088] If gap is not greater than time_need, then execute step S207: calculate whether the time between N_start and N_end is greater than time_need - gap. Among them, N_start and N_end refer to the entry time and the estimated departure time of multiple trucks at the target operation point.

[0089] If the time between N_start and N_end is not greater than time_need - gap, then execute step S208: this task cannot be transported. In this way, when the server determines that the idle time of the trucks entering the target operation point cannot all complete the loading and unloading task, it is considered that this task cannot be completed.

[0090] If the time between N_start and N_end is greater than time_need - gap, then execute step S209: issue the first instruction message to the truck that can complete this task, instructing it to perform the loading and unloading task at the target operation point.

[0091] The method for identifying the loading and unloading of trucks provided by the embodiments of the present application can determine the actual loading and unloading duration of the truck by fitting and analyzing the load data collected in real time for the truck. At the same time, the loading and unloading operation point can also allocate the loading and unloading tasks of the truck according to data such as the arrival time and historical stay time of the truck, reducing the waiting time of the truck and improving the efficiency of loading and unloading.

[0092] In several embodiments provided in the present application, it should be understood that the disclosed method can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For another example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some communication interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical or other form.

[0093] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0094] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0095] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a non-volatile computer-readable storage medium executable by a processor. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or this part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0096] Finally, it should be noted that the above-described embodiments are only specific embodiments of the present application, used to illustrate the technical solutions of the present application, rather than limiting it. The protection scope of the present application is not limited thereto. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that any person skilled in the art within the technical scope disclosed by the present application can still modify the technical solutions recorded in the foregoing embodiments or can easily think of changes, or perform equivalent replacements on some of the technical features; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method for identifying the loading and unloading of a freight vehicle, characterized in that, Including: Obtain the load weight value of the first target truck at each sampling moment at the target operation point; Whenever the data volume of the obtained load weight value reaches the data segmentation threshold, form a data group; Perform the following processing on the current data group: Determine the effective loading and unloading data from the current data group; Obtain the first fitting straight line by fitting the multiple sampling moments and the corresponding multiple load weight values included in the effective loading and unloading data; Determine the interval start time and interval end time corresponding to the current data group according to the maximum and minimum values of the first fitting straight line; Determine the start time and end time of the actual loading and unloading of the first target truck according to the interval start time and the interval end time; The step of determining the interval start time and interval end time corresponding to the current data group according to the maximum and minimum values of the first fitting straight line includes: Determine the first maximum load weight and the first minimum load weight in the effective loading and unloading data; Substitute the first maximum load weight into the first fitting straight line to obtain the first fitting time point; Determine the interval start time corresponding to the current data group according to the first fitting time point; Substitute the first minimum load weight into the first fitting straight line to obtain the second fitting time point; Determine the interval end time corresponding to the current data group according to the second fitting time point; Among them, the step of determining the interval start time corresponding to the current data group according to the first fitting time point includes: Obtain the load weight values corresponding to the first threshold number of sampling moments before the first fitting time point; Determine the first target sampling moment corresponding to the second maximum load weight from the load weight values corresponding to the first threshold number of sampling moments; Determine the time point corresponding to the first target sampling moment as the interval start time.

2. The method according to claim 1, characterized in that, The step of determining the effective loading and unloading data from the current data group includes: For each sampling moment after the first sampling moment in the current data group, calculate the difference between the load weight value corresponding to this sampling moment and the load weight value corresponding to the previous sampling moment in turn, so as to find the sampling moment whose first difference is greater than the first set threshold, and determine it as the first moment; For each sampling moment after the first moment in the current data group, calculate the difference between the load weight value corresponding to this sampling moment and the load weight value corresponding to the previous sampling moment in turn, so as to find the sampling moment whose first difference is less than the second set threshold, and determine it as the second moment; Determine the load weight values corresponding to the sampling moments between the first moment and the second moment as the effective loading and unloading data.

3. The method according to claim 1, characterized in that, The step of determining the interval end time corresponding to the current data group according to the second fitting time point includes: Obtain the load weight values corresponding to the second threshold number of sampling moments after the second fitting time point; Determine the second target sampling moment corresponding to the second minimum load weight from the load weight values corresponding to the second threshold number of sampling moments; Determine the time point corresponding to the second target sampling moment as the interval end time.

4. The method according to claim 1, characterized in that, The step of determining the start time and end time of the actual loading and unloading of the first target truck according to the interval start time and the interval end time includes: Determine whether the current data group meets the merging condition; If it is determined that the current data group does not meet the merging condition, the start time of the interval is determined as the start time of the actual loading and unloading of the first target truck, and the end time of the interval is determined as the end time of the actual loading and unloading of the first target truck; If it is determined that the current data group meets the merging condition, the stored record end time is updated with the end time of the interval, and the stored record start time is determined as the start time of the actual loading and unloading of the first target truck, and the updated record end time is determined as the end time of the actual loading and unloading of the first target truck.

5. The method according to claim 4, characterized in that, The steps of determining whether the current data group meets the merging condition include: Determining a first slope value of a first fitting line corresponding to the current data group; Determining a second slope value of a second fitting line corresponding to the previous data group; According to the first slope value and the second slope value, determining whether the data change trends of the current data group and the previous data group are consistent, and determining whether a first time interval between the end time of the interval of the previous data group and the start time of the interval of the current data group is less than a preset interval threshold; If the data change trends are inconsistent, and / or the first time interval is greater than the preset interval threshold, it is determined that the current data group does not meet the merging condition; If the data change trends are consistent and the first time interval is not greater than the preset interval threshold, it is determined that the current data group meets the merging condition.

6. The method according to claim 1, wherein The method further includes: Determining the actual loading and unloading duration of the first target truck according to the start time and end time of the actual loading and unloading of the first target truck; Judging whether the actual loading and unloading duration of the first target truck exceeds a preset loading and unloading duration threshold; If the actual loading and unloading duration exceeds the preset loading and unloading duration threshold, a loading and unloading abnormal alarm message is generated, and the loading and unloading abnormal alarm message includes at least one of the following items: a vehicle identifier for indicating the first target truck, a position identifier for indicating the location of the first target truck, and a first time identifier for indicating the loading and unloading duration of the first target truck; and / or, the method further includes: Detecting whether the first target truck has left its target area for operation, where the target area includes at least one target operation point; If the first target truck leaves the target area, a driving abnormal alarm message is generated, and the driving abnormal alarm message includes at least one of the following items: a vehicle identifier for indicating the first target truck, a position identifier for indicating the location of the first target truck, and a second time identifier for indicating the moment when the first target truck leaves; 7. The method according to claim 1, wherein The method further includes: Determining a first arrival time when a second target truck among at least one truck waiting for operation at the target operation point arrives at the target operation point; Determining a first estimated departure time when the second target truck leaves the target operation point; Calculating a second time interval between the first arrival time and the first estimated departure time; If the second time interval is not less than the estimated loading and unloading duration, a first instruction message is sent to the second target truck, where the estimated loading and unloading duration is the first time difference between the time point corresponding to the last sampling moment and the time point corresponding to the first sampling moment in a data group, and the first instruction message is used to indicate that the second target truck is allowed to perform loading and unloading operations at the target operation point; If the second time interval is less than the estimated loading and unloading duration, a second instruction message is sent to the second target truck, and the second instruction message is used to indicate that the second target truck is prohibited from performing loading and unloading operations at the target operation point.

8. The method according to claim 7, wherein The method further includes: For the target operation point, determining whether there are other trucks that are performing loading and unloading operations after the second target truck arrives at the target operation point; If there are no other trucks performing loading and unloading operations, when the second time interval is not less than the estimated loading and unloading duration, a first instruction message is sent to the second target truck; If there are other trucks performing loading and unloading operations, calculate the second time difference between the second time interval and the actual loading and unloading duration of the other trucks; If the second time difference is not less than the estimated loading and unloading duration, a first instruction message is sent to the second target truck; If the second time difference is less than the estimated loading and unloading duration, a second instruction message is sent to the second target truck.

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