Cargo state estimation method and device for logistics conveying system and electronic equipment

Abstract

The application relates to the technical field of logistics automation sorting, in particular to a cargo state estimation method and device for a logistics conveying system and electronic equipment. The method comprises the following steps: acquiring a first speed estimation value of the cargo and observation data, wherein the first speed estimation value corresponds to a conveying unit speed, and the observation data is generated by a detection unit during detection of the cargo; performing physical error correction on the observation data to obtain corrected observation data; calculating a second speed observation value of the cargo based on the corrected observation data; and determining a real-time speed estimation value of the cargo through fusion processing based on the first speed estimation value and the second speed observation value. The method can fuse odometer estimation and sensor asynchronous calibration, actively compensate network delay and sensor beam width, and realize millimeter-level cargo positioning and speed sensing.

Description

Technical Field

[0001] This application relates to the field of automated sorting technology in logistics, and in particular to a method, apparatus and electronic device for estimating the state of goods in a logistics conveying system. Background Technology

[0002] In modern automated sorting systems, the real-time speed of goods on the conveyor belt is a key parameter for achieving accurate sorting (such as swing arm placement and slider movement). Current mainstream methods for obtaining speed have the following shortcomings:

[0003] 1. Mechanical error: The speed feedback from the motor encoder alone (i.e., the odometer method) has significant physical deviations during conveyor belt load changes, conveyor belt slippage, or start-stop phases.

[0004] 2. Sensing lag: There is an uncertain network delay when sensor signals are transmitted to the control system via industrial buses (such as Modbus), which causes the system's sensing trigger time to be later than the physical occurrence time.

[0005] 3. Physical interference: The beam of the photoelectric sensor has a physical width. The "beam width effect" generated when high-speed cargo passes by will cause systematic deviations in the estimation of cargo length and center of mass. Summary of the Invention

[0006] In view of this, this application provides a method, apparatus and electronic device for cargo status estimation in a logistics transportation system, which can integrate odometer prediction and asynchronous sensor calibration, and achieve millimeter-level cargo positioning and speed perception by actively compensating for network delay and sensor beamwidth.

[0007] This application provides a method, apparatus, and electronic device for estimating the state of goods in a logistics transportation system. The following description covers various aspects of this application, and the embodiments and beneficial effects described below can be referenced interchangeably.

[0008] In a first aspect, this application provides a method for estimating the state of goods in a logistics transportation system, including:

[0009] The first speed estimate and observation data of the cargo are obtained, wherein the first speed estimate corresponds to the speed of the conveying unit, and the observation data are generated by the detection unit during cargo detection.

[0010] Physical error correction is applied to the observation data to obtain the corrected observation data;

[0011] Based on the corrected observation data, the second velocity observation value of the cargo is calculated;

[0012] Based on the first velocity estimate and the second velocity observation, the real-time velocity estimate of the cargo is determined through fusion processing.

[0013] According to the embodiments of this application, the above-described technical solution of this application has at least the following beneficial effects:

[0014] The real-time speed of cargo is estimated by fusing multi-source data. Observational data from photoelectric sensors are used to asynchronously calibrate the speed estimate under the odometer method, while actively compensating for transmission delay and beamwidth effects in the observation data.

[0015] In one possible implementation of the first aspect described above, physical error correction of the observation data includes at least one of network transmission delay compensation and spatial geometry compensation.

[0016] In one possible implementation of the first aspect above, network transmission delay compensation includes:

[0017] Obtain the preset global delay parameter, where the global delay parameter represents the time delay required for the observation data to be transmitted from the detection unit to the processing unit;

[0018] Based on the global delay parameter, the reception time of the observation data received by the processing unit from the detection unit is corrected to the physical occurrence time.

[0019] In one possible implementation of the first aspect above, spatial geometry compensation includes:

[0020] The inherent size parameters of the detection area of ​​the detection unit and the current speed estimate are obtained. The inherent size parameters of the detection area represent the effective sensing width of the detection unit in the direction of movement of the goods, and the current speed estimate is the first speed estimate or the real-time speed estimate determined at the previous moment.

[0021] Based on the inherent size parameters of the detection area and the current speed estimate, calculate the passage time corresponding to the inherent size of the detection area of ​​the detection unit;

[0022] The motion duration between the physical occurrence times of the trigger signal in the observation data from adjacent detection units is obtained, wherein the trigger signal is the rising edge signal generated when the detection unit starts detecting the cargo;

[0023] The corrected exercise duration is obtained by removing the passing time from the exercise duration.

[0024] In one possible implementation of the first aspect above, calculating the second velocity observation of the cargo includes:

[0025] Obtain the distance between adjacent detection units;

[0026] The second velocity observation is calculated based on the distance and the corrected motion duration.

[0027] One possible implementation of the first aspect mentioned above also includes:

[0028] Obtain multiple motion samples of goods within a unit period;

[0029] The maximum likelihood estimation method is used to statistically model multiple motion samples and determine the first sample variance associated with the delivery unit and the second sample variance associated with the detection unit.

[0030] The variances of the first and second samples are used as prediction uncertainty parameters to adjust their weights in the fusion process.

[0031] One possible implementation of the first aspect mentioned above also includes:

[0032] Obtain the occlusion duration between the physical occurrence times of the trigger signal and the disconnection signal in the observation data from the same detection unit, where the disconnection signal is a falling edge signal generated when the detection unit no longer detects the cargo;

[0033] Calculate the estimated length of the cargo based on the duration of occlusion and the current speed estimate;

[0034] Determine whether the difference between the estimated length and the nominal length exceeds the preset tolerance range;

[0035] If the difference exceeds the preset tolerance range, the second velocity observation is determined to be invalid; otherwise, the second velocity observation is determined to be valid.

[0036] In one possible implementation of the first aspect above, determining the real-time speed estimate of the cargo includes:

[0037] Establish a velocity sampling pool based on a first-in-first-out queue, wherein the velocity sampling pool has a preset queue length and is used to store the first velocity estimate and the valid second velocity observation determined at multiple historical moments;

[0038] When a new velocity value is added to the velocity sampling pool, it is determined whether the velocity sampling pool is full.

[0039] If the velocity sampling pool is full, the earliest added velocity value is removed and the new velocity value is added to the tail of the velocity sampling pool; otherwise, the new velocity value is added directly to the tail of the velocity sampling pool.

[0040] The weighted average of all velocity values ​​in the velocity sampling pool is calculated as the real-time velocity estimate.

[0041] One possible implementation of the first aspect mentioned above also includes:

[0042] Based on real-time speed estimates, the estimated time for goods to reach the execution unit is determined;

[0043] Start an asynchronous timer task, and trigger the execution unit to perform an action when the expected time has elapsed.

[0044] Secondly, this application provides a cargo status estimation device for a logistics conveying system, comprising:

[0045] The acquisition module is used to acquire the first speed estimate and observation data of the cargo, wherein the first speed estimate corresponds to the speed of the conveying unit, and the observation data is generated by the detection unit during cargo detection.

[0046] The correction module is used to correct physical errors in the observation data to obtain corrected observation data.

[0047] The calculation module is used to calculate the second velocity observation value of the cargo based on the corrected observation data;

[0048] The determination module is used to determine the real-time speed estimate of the cargo based on the first speed estimate and the second speed observation through fusion processing.

[0049] Thirdly, this application provides an electronic device including a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the cargo status estimation method for a logistics transportation system disclosed in the first aspect and any possible implementation thereof.

[0050] Fourthly, this application provides a computer-readable storage medium storing at least one instruction or at least one program, wherein the at least one instruction or at least one program is loaded and executed by a processor to implement the cargo state estimation method for a logistics transportation system disclosed in the first aspect and any possible implementation thereof.

[0051] Fifthly, this application provides a computer program product including computer instructions that, when executed on an electronic device, cause the electronic device to perform the cargo state estimation method for a logistics transportation system disclosed in the first aspect and any possible implementation thereof.

[0052] The beneficial effects of the second to fifth aspects can be found in the first aspect and the beneficial effects of any possible implementation of the first aspect, and will not be repeated here. Attached Figure Description

[0053] Figure 1 This is a schematic diagram of the cargo status estimation process in the embodiments of this application;

[0054] Figure 2A flowchart of network transmission delay compensation in an embodiment of this application is shown;

[0055] Figure 3 A flowchart illustrating the spatial geometry compensation process in an embodiment of this application is shown.

[0056] Figure 4 A flowchart illustrating the multi-source data fusion process in an embodiment of this application is shown;

[0057] Figure 5 A flowchart illustrating the real-time velocity estimation process in an embodiment of this application is shown;

[0058] Figure 6 A flowchart of the dynamic tolerance filtering process in an embodiment of this application is shown;

[0059] Figure 7 This is a structural block diagram of the cargo status estimation device in the embodiments of this application;

[0060] Figure 8 This is a block diagram of the electronic device in the embodiments of this application;

[0061] Figure 9 This is a block diagram of a system-on-chip (SoC) in the embodiments of this application. Detailed Implementation

[0062] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0063] The technical problems to be solved by the embodiments of this application will be described below.

[0064] As described in the background section above, existing technologies for obtaining the real-time speed of goods on a conveyor belt have the following shortcomings:

[0065] 1. Mechanical error: The speed feedback from the motor encoder alone (i.e., the odometer method) has significant physical deviations during conveyor belt load changes, conveyor belt slippage, or start-stop phases.

[0066] 2. Sensing lag: There is an uncertain network delay when sensor signals are transmitted to the control system via industrial buses (such as Modbus), which causes the system's sensing trigger time to be later than the physical occurrence time.

[0067] 3. Physical interference: The beam of the photoelectric sensor has a physical width. The "beam width effect" generated when high-speed cargo passes by will cause systematic deviations in the estimation of cargo length and center of mass.

[0068] Therefore, to address the aforementioned problems, this application provides a method for estimating the state of goods in a logistics transportation system. Specifically, based on a first speed estimate and second speed observations, a real-time speed estimate of the goods is determined through fusion processing.

[0069] This method integrates odometer prediction and asynchronous sensor calibration, enabling millimeter-level cargo positioning and speed sensing. It reduces the cargo edge positioning error from ±20mm to ±3mm under high-speed operation, effectively solving the speed fluctuation problem caused by frequent start-stop and empty / heavy load switching of the conveyor belt.

[0070] To better understand the cargo state estimation method for logistics transportation systems in the embodiments of this application, the following is combined with... Figures 1 to 6 The solutions corresponding to the methods in the embodiments of this application will be described in detail.

[0071] Reference Figure 1 , Figure 1 This is a schematic diagram of the cargo status estimation process in an embodiment of this application.

[0072] In this application embodiment, a cargo status estimation method for a logistics transportation system includes steps S10-S40.

[0073] S10, acquire the first speed estimate and observation data of the cargo, wherein the first speed estimate corresponds to the speed of the conveying unit, and the observation data is generated by the detection unit during cargo detection.

[0074] In practical applications, the conveyor belt (i.e., the conveyor unit) of a logistics conveying system is driven by a motor-driven roller shaft. An encoder is installed on the motor shaft, and the encoder outputs a fixed number of pulses for each revolution of the motor shaft. The control system can count the pulses to calculate the rotational speed of the motor shaft. Then, based on the transmission ratio and the circumference of the conveyor belt, the theoretical linear speed of the conveyor belt, i.e., the speed of the conveyor unit, is also an estimate of the initial speed of the goods. This method of speed feedback from the encoder at the motor is also known as the odometer method.

[0075] The control system can update the first speed estimate at high frequencies (such as 10ms / time) and can only count pulses when the motor is in a steady-state range (such as above 80% of the planned speed), thereby ensuring the real-time performance and accuracy of the first speed estimate.

[0076] Multiple photoelectric sensors (i.e. detection units) are also installed on the conveyor belt. Each photoelectric sensor includes a transmitter and a receiver respectively located on both sides of the conveyor belt. The transmitter emits a light beam. When no goods pass by, the light beam is received by the receiver. When goods pass by, the goods will block the light beam, so the receiver will no longer receive the light beam, and thus the photoelectric sensor detects the goods.

[0077] The observation data includes trigger signals (rising edge signals generated when the photoelectric sensor begins to detect goods), disconnect signals (falling edge signals generated when the photoelectric sensor stops detecting goods), and timestamps for each signal generation. However, since these timestamps are actually generated by the control system (i.e., the processing unit) when it receives the corresponding signal from the photoelectric sensor, they cannot represent the physical time of the detected event if there is a network delay in the transmission of the signal from the sensor to the control system.

[0078] S20, perform physical error correction on the observation data to obtain the corrected observation data.

[0079] As discussed above, considering network transmission latency, physical error correction is required for the observation data. In addition to network transmission latency, it is also necessary to consider that the beam of the photoelectric sensor has a certain physical width. The cargo will take a certain amount of time to pass through this beam width (i.e., passage time). This passage time should not be included in the time the cargo moves between two adjacent photoelectric sensors (i.e., movement time), which is spatial geometric compensation.

[0080] S30, based on the corrected observation data, calculate the second velocity observation value of the cargo.

[0081] The distance between adjacent detection units can be obtained. Based on this distance and the corrected motion duration, a second velocity observation value is calculated, where the second velocity observation value is the distance between adjacent detection units divided by the corrected motion duration.

[0082] In addition, we can first check whether the motion duration is within the valid range (such as 50ms to 3000ms). If it is not within the valid range, we can directly discard the observation data; otherwise, we can continue to calculate the second velocity observation value of the cargo.

[0083] S40, based on the first speed estimate and the second speed observation, determines the real-time speed estimate of the cargo through fusion processing.

[0084] This application estimates the real-time speed of goods through multi-source data fusion. Observational data from photoelectric sensors is used to asynchronously calibrate the speed estimate under the odometry method. At the same time, it actively compensates for transmission delay and beamwidth effects in the observation data, which can transform unstable industrial underlying signals into a high-confidence digital mirror of the physical world (Digital Twin). This multi-level error elimination mechanism is a technological watershed for achieving sorting efficiency from 3,000 pieces / hour to over 8,000 pieces / hour.

[0085] Furthermore, this application is also applicable to transport paths with bifurcations, achieving smooth speed switching under complex topologies by maintaining independent motion state pools in different sections.

[0086] To facilitate understanding of the technical solution of this application, steps S20 and S40 above will be described in detail below.

[0087] refer to Figure 2 , Figure 2 A flowchart illustrating the network transmission delay compensation process in an embodiment of this application is shown.

[0088] S21, Obtain a preset global delay parameter, whereby the global delay parameter represents the time delay required for the observation data to be transmitted from the detection unit to the processing unit. This global delay parameter can be a fixed value or updated in real time to adapt to different network communication environments.

[0089] S22, based on the global delay parameter, corrects the reception time of the observation data received by the processing unit from the detection unit to the physical occurrence time, where the physical occurrence time is the reception time minus the global delay parameter.

[0090] refer to Figure 3 , Figure 3 A flowchart illustrating spatial geometry compensation in an embodiment of this application is shown.

[0091] S23, acquire the inherent size parameters of the detection area of ​​the detection unit and the current speed estimate, wherein the inherent size parameters of the detection area characterize the effective sensing width (i.e. beam width) of the detection unit in the direction of movement of the goods, and the current speed estimate is the first speed estimate or the real-time speed estimate determined at the previous moment.

[0092] S24. Based on the inherent size parameter of the detection area and the current speed estimate, calculate the passage time corresponding to the inherent size of the detection area of ​​the detection unit, where the passage time is the inherent size parameter of the detection area divided by the current speed estimate.

[0093] S25, acquire the motion duration between the physical occurrence time of the trigger signal in the observation data from the adjacent detection unit, wherein the trigger signal is the rising edge signal generated when the detection unit starts detecting the cargo.

[0094] S26, remove the passing time from the exercise duration to obtain the corrected exercise duration.

[0095] refer to Figure 4 , Figure 4 A flowchart illustrating the multi-source data fusion process in an embodiment of this application is shown.

[0096] S41, acquire multiple motion samples of the goods within a unit period.

[0097] S42, use the maximum likelihood estimation method to statistically model multiple motion samples and determine the first sample variance associated with the delivery unit and the second sample variance associated with the detection unit.

[0098] The first sample variance characterizes the uncertainty in position prediction using the odometer method, which can originate from errors caused by encoders, conveyor belts, and motion inertia. Generally, the farther the goods travel, the greater the prediction error, and the first sample variance automatically increases. For example, the mean of the goods' movement distances within a unit cycle is calculated, representing the true average speed / displacement of the conveyor belt. Then, the variance is calculated based on these multiple movement distances and their mean, representing the intensity of mechanical vibration, slippage, and electrical noise from the conveyor belt.

[0099] The second sample variance characterizes the measurement accuracy level of the detection unit, which can originate from errors caused by factors such as the physical resolution and installation stability of the detection unit. The second sample variance indicates that the observation data from the detection unit cannot be trusted with absolute certainty.

[0100] S43, the variance of the first sample and the variance of the second sample are used as prediction uncertainty parameters to adjust their weights in the fusion process.

[0101] A dynamic gain weight can be set for the second velocity observation, where the dynamic gain weight is the first sample variance divided by the sum of the first and second sample variances. When the second sample variance increases, the dynamic gain weight decreases, and the system becomes more biased towards the first velocity estimate under the odometry method. If the second sample variance remains excessively large and the trigger sequence of the detection unit does not conform to the topological logic, the system can automatically switch to a pure odometry safety mode and report an error. When the second sample variance decreases, the dynamic gain weight increases, and the system quickly aligns with the second velocity observation. This significantly improves the system's fault tolerance in extreme environments.

[0102] refer to Figure 5 , Figure 5A flowchart illustrating the real-time velocity estimation process in an embodiment of this application is shown.

[0103] S44, establish a velocity sampling pool based on a first-in-first-out queue, wherein the velocity sampling pool has a preset queue length and is used to store the first velocity estimate and valid second velocity observation determined at multiple historical moments.

[0104] S45, when a new speed value is added to the speed sampling pool, determine whether the speed sampling pool is full.

[0105] S46. If the velocity sampling pool is full, remove the earliest added velocity value and add the new velocity value to the tail of the velocity sampling pool; otherwise, add the new velocity value directly to the tail of the velocity sampling pool.

[0106] S47, calculate the weighted average of all velocity values ​​in the velocity sampling pool as a real-time velocity estimate, where the weighted average is based on the dynamic gain weights discussed above.

[0107] The first speed estimate is updated frequently, and the data is continuous but easily distorted. The second speed observation is more realistic but the sampling is scattered. Therefore, putting these two sets of speed data into a sliding window for filtering can offset their respective defects, remove abrupt outliers, smooth fluctuating data, and filter out noise caused by installation deviations or signal jitter, and finally calculate a real-time estimated speed of the cargo that is closer to reality.

[0108] refer to Figure 6 , Figure 6 A flowchart of the dynamic tolerance filtering process in an embodiment of this application is shown.

[0109] S441, acquire the occlusion duration between the physical occurrence times of the trigger signal and the disconnection signal in the observation data from the same detection unit, wherein the disconnection signal is a falling edge signal generated when the detection unit no longer detects the cargo.

[0110] S442, Calculate the length estimate of the cargo based on the occlusion duration and the current speed estimate, where the length estimate is the occlusion duration multiplied by the current speed estimate.

[0111] S443, determine whether the difference between the length estimate and the nominal length exceeds the preset tolerance range.

[0112] S444 If the difference exceeds the preset tolerance range, the second velocity observation is determined to be invalid; otherwise, the second velocity observation is determined to be valid.

[0113] Constraints can be established on the physical length of the cargo and / or the duration of obstruction. If the length estimate or the duration of obstruction is abnormal, the second velocity observation calculated at this moment can be considered abnormal and should be discarded.

[0114] Furthermore, the length estimate can be used to update the centroid coordinates of the cargo, and based on the estimated real-time velocity and centroid variance, a "virtual repulsion field" can be established between the cargoes. When the distance between two cargoes decreases, the system can automatically adjust the placement priority of the cargoes in front and behind.

[0115] After determining the real-time speed estimate of the goods, this application can determine the estimated time for the goods to reach the execution unit (such as a sorting arm) based on the real-time speed estimate, wherein the estimated time is the distance between the current position of the goods (such as at a certain detection unit) and the execution unit divided by the real-time speed estimate.

[0116] Then, an asynchronous timing task is started, which triggers the execution unit to perform actions when the expected time is up. Through asynchronous coroutine scheduling, the execution unit is delayed based on real-time estimated speed, ensuring physical synchronization between instruction issuance and cargo movement trajectory.

[0117] refer to Figure 7 , Figure 7 A structural block diagram of the cargo status estimation device in an embodiment of this application is shown.

[0118] like Figure 7 As shown, this application provides a cargo status estimation device for a logistics transportation system, including an acquisition module, a correction module, a calculation module, and a determination module.

[0119] The acquisition module is used to acquire the first speed estimate and observation data of the goods. The first speed estimate corresponds to the speed of the conveying unit, and the observation data is generated by the detection unit during the detection of the goods.

[0120] The correction module is used to correct physical errors in the observation data to obtain corrected observation data.

[0121] The calculation module is used to calculate the second velocity observation value of the cargo based on the corrected observation data;

[0122] The determination module is used to determine the real-time speed estimate of the cargo based on the first speed estimate and the second speed observation through fusion processing.

[0123] In the apparatus of this application embodiment, each module executes the method of the above embodiment, and its specific functions and corresponding technical effects can be referred to the above embodiment. Figures 1-6 The methods explained will not be elaborated here.

[0124] Now for reference Figure 8 The diagram shown is a block diagram of an electronic device 1200 according to an embodiment of this application. The electronic device 1200 may include one or more processors (corresponding to...) coupled to a controller hub 1203. Figure 8 The first processor 1201 is described above. In at least one embodiment, the controller hub 1203 communicates with the first processor 1201 via a multi-branch bus such as a front-side bus (FSB), a point-to-point interface such as a quick path interconnect (QPI), or a similar connection. The first processor 1201 executes instructions that control general types of data processing operations. In one embodiment, the controller hub 1203 includes, but is not limited to, a graphics memory controller hub (GMCH) (not shown) and an input / output hub (IOH) (which may be on a separate chip) (not shown), wherein the GMCH includes memory and a graphics controller and is coupled to the IOH.

[0125] Electronic device 1200 may also include a coprocessor coupled to controller hub 1203 (corresponding to...) Figure 8 The first coprocessor 1202 and memory 1204 are integrated within the processor (as described in this application). Alternatively, one or both of the memory and GMCH can be integrated within the processor (as described in this application), with memory 1204 and the first coprocessor 1202 directly coupled to the first processor 1201 and the controller hub 1203, which is located on a single chip with the IOH. Memory 1204 can be, for example, dynamic random access memory (DRAM), phase change memory (PCM), or a combination of both. In one embodiment, the first coprocessor 1202 is a dedicated processor, such as a high-throughput MIC processor (many integerized core, MIC), a network or communication processor, a compression engine, a graphics processor, a general-purpose computing on GPU (GPGPU), or an embedded processor, etc. Optional properties of the first coprocessor 1202 are indicated by dashed lines. Figure 8 middle.

[0126] As a computer-readable storage medium, memory 1204 may include one or more tangible, non-transitory computer-readable media for storing data and / or instructions. For example, memory 1204 may include any suitable non-volatile memory such as flash memory and / or any suitable non-volatile storage device such as one or more hard-disk drives (HDDs), one or more compact disc (CD) drives, and / or one or more digital versatile disc (DVD) drives.

[0127] In one embodiment, electronic device 1200 may further include a network interface controller (NIC) 1206. Network interface 1206 may include a transceiver for providing a radio interface for electronic device 1200 to communicate with any other suitable device, such as a front-end module, antenna, etc. In various embodiments, network interface 1206 may be integrated with other components of electronic device 1200. Network interface 1206 can implement the functions of the communication unit in the above embodiments.

[0128] Electronic device 1200 may further include input / output (I / O) device 1205. I / O device 1205 may include: a user interface designed to enable a user to interact with electronic device 1200; a peripheral component interface designed to enable peripheral components to also interact with electronic device 1200; and / or sensors designed to determine environmental conditions and / or location information related to electronic device 1200.

[0129] It is worth noting that, Figure 8 This is merely an example. That is, although... Figure 8 The electronic device 1200 shown includes multiple devices such as a first processor 1201, a first coprocessor 1202, a controller hub 1203, and a memory 1204. However, in actual applications, devices using the methods of this application may include only a portion of the devices in the electronic device 1200. For example, it may include only the first processor 1201 and the network interface 1206. Figure 8 The properties of the optional devices are shown in dashed lines. According to some embodiments of this application, the memory 1204, which is a computer-readable storage medium, stores instructions that, when executed on a computer, cause the electronic device 1200 to perform the cargo state estimation method for a logistics transportation system according to the above embodiments. Specific details can be found in the methods described in the above embodiments, and will not be repeated here.

[0130] Now for reference Figure 9The diagram shown is a block diagram of a SoC (system on chip) 1300 according to an embodiment of this application. Figure 9 In the diagram, similar components share the same reference numerals. Additionally, dashed boxes are an optional feature for more advanced SoCs. Figure 9 In the SoC1300, interconnect unit 1350 is coupled to the processor (corresponding to...). Figure 9 The system includes a second processor 1310, a system agent unit 1380, a bus controller unit 1390, an integrated memory controller unit 1340, and one or more coprocessors (corresponding to...). Figure 9 The second coprocessor 1320 may include integrated graphics logic, an image processor, an audio processor, and a video processor; a static random access memory (SRAM) unit 1330; and a direct memory access (DMA) unit 1360. In one embodiment, the second coprocessor 1320 includes a dedicated processor, such as a network or communication processor, a compression engine, a GPGPU, a high-throughput MIC processor, or an embedded processor.

[0131] The static random access memory (SRAM) cell 1330 may include one or more computer-readable media for storing data and / or instructions. The computer-readable storage medium may store instructions, specifically, temporary and permanent copies of those instructions. These instructions may include, when executed by at least one unit in the processor, causing the SoC 1300 to perform the cargo state estimation method for a logistics delivery system according to the above embodiments, specifically referring to the methods in the above embodiments, which will not be repeated here.

[0132] Various embodiments of the mechanisms disclosed in this application can be implemented in hardware, software, firmware, or combinations of these implementation methods. Embodiments of this application can be implemented as computer programs or program code executable on a programmable system, the programmable system including at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0133] Program code can be applied to input instructions to execute the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, the processing system includes any system having a processor such as a digital signal processor (DSP), microcontroller, application-specific integrated circuit (ASIC), or microprocessor.

[0134] The program code can be implemented using a high-level procedural language or an object-oriented programming language to communicate with the processing system. Assembly language or machine language can also be used when needed. In fact, the mechanisms described in this application are not limited to any particular programming language. In either case, the language can be a compiled language or an interpreted language.

[0135] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored thereon on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or through other computer-readable media. Therefore, machine-readable media may include any mechanism for storing or transmitting information in a machine-readable (e.g., computer-readable) form, including but not limited to floppy disks, optical disks, CD-ROMs, compact disc read-only memory (CD-ROMs), magneto-optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic cards or optical cards, flash memory, or tangible machine-readable storage for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in the form of electrical, optical, acoustic, or other forms of propagated signals. Therefore, machine-readable media include any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a machine-readable (e.g., computer-readable) form.

[0136] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the accompanying drawings. Furthermore, including structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0137] This application also provides a computer-readable storage medium, the specific functions and corresponding technical effects of which can be referred to the above embodiments. Figures 1-4 The methods explained will not be elaborated here.

[0138] This application also provides a computer program product, which includes a computer program or instructions. When the computer program or instructions are loaded and executed by a processor, they implement the cargo status estimation method for a logistics transportation system described in the above embodiments. The specific functions and corresponding technical effects can be found in the above embodiments. Figures 1-4 The methods explained will not be elaborated here.

[0139] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims can be performed in a different order than that shown in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0140] It should be noted that all units / modules mentioned in the device embodiments of this application are logical units / modules. Physically, a logical unit / module can be a physical unit / module, a part of a physical unit / module, or a combination of multiple physical units / modules. The physical implementation of these logical units / modules themselves is not the most important factor; the combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed in this application. Furthermore, to highlight the innovative aspects of this application, the above-described device embodiments of this application have not introduced units / modules that are not closely related to solving the technical problems proposed in this application. This does not mean that the above-described device embodiments do not contain other units / modules.

[0141] It should be noted that in the examples and description of this application, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one" does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0142] Although this application has been illustrated and described with reference to certain preferred embodiments thereof, those skilled in the art should understand that various changes in form and detail may be made thereto without departing from the spirit and scope of this application.

Claims

1. A method for estimating the state of goods in a logistics transportation system, characterized in that, include: A first speed estimate and observation data of the cargo are obtained, wherein the first speed estimate corresponds to the speed of the conveying unit, and the observation data is generated by the detection unit during the detection of the cargo; The observation data is corrected for physical errors to obtain the corrected observation data; Based on the corrected observation data, the second velocity observation value of the cargo is calculated; Based on the first speed estimate and the second speed observation, the real-time speed estimate of the cargo is determined through fusion processing; The physical error correction of the observation data includes spatial geometric dimension compensation, and the spatial geometric dimension compensation includes: The inherent size parameters of the detection area of ​​the detection unit and the current speed estimate are obtained, wherein the inherent size parameters of the detection area characterize the effective sensing width of the detection unit in the direction of movement of the goods, and the current speed estimate is the first speed estimate determined at the previous moment or the real-time speed estimate. Based on the inherent size parameters of the detection area and the current speed estimate, calculate the passage time corresponding to the inherent size of the detection area of ​​the detection unit; The motion duration between the physical occurrence times of the trigger signals in the observation data from adjacent detection units is obtained, wherein the trigger signal is a rising edge signal generated when the detection unit begins to detect the cargo; The passage time is removed from the total movement time to obtain the corrected movement time.

2. The method according to claim 1, characterized in that, Physical error correction of the observation data also includes network transmission delay compensation.

3. The method according to claim 2, characterized in that, The network transmission delay compensation includes: Obtain a preset global delay parameter, wherein the global delay parameter characterizes the time delay required for the observation data to be transmitted from the detection unit to the processing unit; Based on the global delay parameter, the reception time of the observation data received by the processing unit from the detection unit is corrected to the physical occurrence time.

4. The method according to claim 1, characterized in that, Calculating the second velocity observation of the cargo includes: Obtain the distance between the adjacent detection units; The second velocity observation is calculated based on the distance and the corrected motion duration.

5. The method according to claim 4, characterized in that, Also includes: Obtain multiple motion samples of the cargo within a unit period; The maximum likelihood estimation method is used to statistically model the plurality of motion samples, and a first sample variance associated with the delivery unit and a second sample variance associated with the detection unit are determined. The first sample variance and the second sample variance are used as prediction uncertainty parameters to adjust their weights in the fusion process.

6. The method according to claim 5, characterized in that, Also includes: The occlusion duration between the physical occurrence times of the trigger signal and the disconnection signal in the observation data from the same detection unit is obtained, wherein the disconnection signal is a falling edge signal generated when the detection unit no longer detects the cargo; Based on the duration of the obstruction and the current speed estimate, calculate the estimated length of the cargo; Determine whether the difference between the estimated length and the nominal length exceeds a preset tolerance range; If the difference exceeds the preset tolerance range, the second velocity observation value is determined to be invalid; otherwise, the second velocity observation value is determined to be valid.

7. The method according to claim 6, characterized in that, Determining the estimated real-time speed of the cargo includes: A velocity sampling pool based on a first-in-first-out queue is established, wherein the velocity sampling pool has a preset queue length and is used to store the first velocity estimate determined at multiple historical moments and the valid second velocity observation. When a new velocity value is added to the velocity sampling pool, it is determined whether the velocity sampling pool is full. If the velocity sampling pool is full, the earliest added velocity value is removed, and the new velocity value is added to the tail of the velocity sampling pool; otherwise, the new velocity value is added directly to the tail of the velocity sampling pool. The weighted average of all velocity values ​​in the velocity sampling pool is calculated as the real-time velocity estimate.

8. The method according to claim 1, characterized in that, Also includes: Based on the real-time speed estimate, the estimated time for the goods to reach the execution unit is determined; An asynchronous timing task is started, and the execution unit is triggered to perform an action when the expected time expires.

9. A cargo status estimation device for a logistics conveying system, characterized in that, include: The acquisition module is used to acquire a first speed estimate and observation data of the cargo, wherein the first speed estimate corresponds to the speed of the conveying unit, and the observation data is generated by the detection unit during the detection of the cargo; The correction module is used to correct physical errors in the observation data to obtain corrected observation data. The calculation module is used to calculate the second velocity observation value of the cargo based on the corrected observation data; The determination module is used to determine the real-time speed estimate of the cargo based on the first speed estimate and the second speed observation value through fusion processing; The physical error correction of the observation data includes spatial geometric dimension compensation, and the spatial geometric dimension compensation includes: The inherent size parameters of the detection area of ​​the detection unit and the current speed estimate are obtained, wherein the inherent size parameters of the detection area characterize the effective sensing width of the detection unit in the direction of movement of the goods, and the current speed estimate is the first speed estimate determined at the previous moment or the real-time speed estimate. Based on the inherent size parameters of the detection area and the current speed estimate, calculate the passage time corresponding to the inherent size of the detection area of ​​the detection unit; The motion duration between the physical occurrence times of the trigger signals in the observation data from adjacent detection units is obtained, wherein the trigger signal is a rising edge signal generated when the detection unit begins to detect the cargo; The passage time is removed from the total movement time to obtain the corrected movement time.

10. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by the processor to implement the cargo status estimation method for a logistics transportation system as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction or at least one program, which is loaded and executed by a processor to implement the cargo status estimation method for a logistics transportation system as described in any one of claims 1 to 8.

12. A computer program product, characterized in that, The computer program product includes computer instructions that, when executed on an electronic device, cause the electronic device to perform a cargo status estimation method for a logistics transport system as described in any one of claims 1 to 8.

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