CN113049026B - Multi-mode fusion identification method and device based on Internet of things - Google Patents
Multi-mode fusion identification method and device based on Internet of things Download PDFInfo
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Abstract
The embodiment of the application provides a multi-mode fusion recognition method and device based on the Internet of things, wherein the method comprises the following steps: collecting sensor data sent by a sensor at a set position in the process of cargo transportation; performing fusion judgment on various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result; the system can effectively solve the problems that goods in the warehousing system are difficult to identify in the processes of selecting, carrying and stacking the goods, and the system records the goods, and the accuracy of the starting and stopping positions of the goods is low.
Description
Technical Field
The application relates to the field of Internet of things, in particular to a multi-mode fusion recognition method and device based on the Internet of things.
Background
Nowadays, the technology of the internet of things is developed rapidly, and the ways of life, work and communication of people are changed rapidly. Different from traditional communication, the attention of the internet of things lies in communication between objects, and low cost, high coverage and low power consumption are pursued. In the warehouse logistics industry, particularly for valuables, the technology of the internet of things can enhance the processing precision in the storage and moving processes of goods, closely monitor the assets through tracking the goods, enhance the safety of the goods, prevent the assets from being lost and remarkably reduce the cost of suppliers and customers.
In the process of warehousing and logistics, the goods are various in types and the process is complicated. The goods are difficult to calibrate, the goods are selected, carried and stacked, the uncertainty is high, the conventional technical center mainly adopts the conventional manual storage mode, the starting position and the ending position of the goods can be only roughly recorded, and the precise and intelligent management cannot be realized. At present, a means (including low-frequency activation) capable of accurately identifying goods by 100% is not found, and some modes cannot meet the current requirements, such as a short rfid distance, failure after shielding and the like. There are several main aspects:
1) in the cargo handling process, the management system is difficult to identify the moved cargos from more cargos.
2) In the cargo handling process, the management system is difficult to acquire the quantity and the type of the cargos.
3) After the goods are stacked, the management system is difficult to record the positions and the sequence of the goods.
Disclosure of Invention
The multi-mode fusion recognition method and device based on the Internet of things can effectively solve the problems that goods in a warehousing system are difficult to recognize in the processes of selecting, carrying and stacking the goods, and the accuracy of starting and stopping positions of the system for recording the goods is low.
In order to solve at least one of the above problems, the present application provides the following technical solutions:
in a first aspect, the application provides a multi-mode fusion recognition method based on the internet of things, which includes:
collecting sensor data sent by a sensor at a set position in the process of cargo transportation;
and performing fusion judgment on the various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result.
Further, sensor data of the forklift during goods picking, walking and falling are collected through sensors arranged on the forklift and the goods label, and a first judgment result is obtained through acceleration correlation comparison of the sensor data;
acquiring initial input information, cargo weight and data in a database and judging through a sensor arranged on the platform scale to obtain a second judgment result;
collecting Bluetooth power data of the goods label for judgment through a sensor arranged in the elevator to obtain a third judgment result;
acquiring a cargo detection result when the cargo passes through the key position through an activator arranged at the key position to obtain a fourth judgment result;
and integrating the first judgment result, the second judgment result, the third judgment result and the fourth judgment result to obtain a fusion judgment result.
Further, processing input sensor data, performing weighted integral on each parameter index of the goods in the process of transporting the goods by the forklift, summing the integrals to obtain the integral of the goods, and determining the integral average value in the whole transporting process as the final integral of the goods;
and judging whether the goods are on the vehicle or not by comparing whether the integral of the goods is greater than a threshold or not, obtaining a goods candidate set of the vehicle after the vehicle drops the goods, comparing the candidate sets of all the vehicles after the goods drop, processing the condition that the same label is on different vehicles, and finally obtaining a result of the vehicle + goods set.
Further, adding the goods with high possibility into the candidate set, performing integral calculation on each goods in the candidate set, and when the integral is lower than a threshold value, judging that the goods are not on the vehicle and removing the goods;
and analyzing the same goods existing in the candidate set of the two vehicles, and deleting the goods with low possibility to obtain the final result of the forklift + goods candidate set.
In a second aspect, the present application provides a multimode fusion recognition device based on the internet of things, including:
the data acquisition module is used for acquiring sensor data sent by a sensor at a set position in the process of cargo transportation;
and the fusion judgment module is used for performing fusion judgment on the various sensor data and performing weighted calculation on various judgment results to obtain a cargo identification result.
Further, the first judgment unit is used for acquiring sensor data of the forklift during goods taking, walking and goods falling through sensors arranged on the forklift and the goods label, and obtaining a first judgment result through acceleration correlation comparison of the sensor data;
the second judgment unit is used for acquiring and judging initial input information, cargo weight and data in the database through a sensor arranged on the platform scale to obtain a second judgment result;
the third judgment unit is used for acquiring Bluetooth power data of the goods label for judgment through a sensor arranged in the elevator to obtain a third judgment result;
the fourth judgment unit is used for acquiring a cargo detection result when the cargo passes through the position through the activator arranged at the key position to obtain a fourth judgment result;
and the fusion judgment unit is used for integrating the first judgment result, the second judgment result, the third judgment result and the fourth judgment result to obtain a fusion judgment result.
Furthermore, the integral processing unit is used for processing the input sensor data, performing weighted integral on various parameter indexes of the goods in the process of transporting the goods by the forklift, summing the integral values to obtain the integral value of the goods, and determining the integral average value of the whole transporting process as the final integral value of the goods;
and the candidate set processing unit is used for judging whether the goods are on the vehicle or not by comparing whether the integral of the goods is larger than a threshold or not, obtaining a candidate set of the goods of the vehicle after the vehicle drops the goods, comparing the candidate sets of all the vehicles after the goods drop, processing the condition that the same label is on different vehicles, and finally obtaining the result of the vehicle + goods set.
Further, the goods identification is used for adding the goods with high possibility into the candidate set, performing integral calculation on each goods in the candidate set, and when the integral is lower than a threshold value, judging that the goods are not on the vehicle and rejecting the goods;
and the heavy goods eliminating unit is used for analyzing the same goods existing in the candidate set of the two vehicles, and deleting the goods with low possibility to obtain a final result of the forklift + goods candidate set.
In a third aspect, the present application provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method for multimodal fusion recognition based on internet of things when executing the program.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps of the method for identifying a multimodal fusion based on the internet of things.
According to the technical scheme, the multi-mode fusion recognition method and device based on the Internet of things are provided, and sensor data sent by a sensor at a set position in the process of goods transportation are collected; performing fusion judgment on various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result; the system can effectively solve the problems that goods in the warehousing system are difficult to identify in the processes of selecting, carrying and stacking the goods, and the system records the low accuracy of the starting and stopping positions of the goods.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a multi-mode fusion recognition method based on the internet of things in the embodiment of the application;
fig. 2 is a structural diagram of a multi-mode fusion recognition device based on the internet of things in the embodiment of the present application;
FIG. 3 is a system block diagram of an embodiment of the present application;
FIG. 4 is a flowchart of a fusion decision in an embodiment of the present application;
FIG. 5 is a second flowchart of a fusion decision in an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In the prior art, the storage logistics process has the defects of various goods and complicated process. The method and the device have the advantages that goods are difficult to calibrate, are selected, transported and stacked, are high in uncertainty, most of the existing technical centers mainly adopt a traditional manual storage mode, can only roughly record the starting position and the ending position of the goods, and cannot achieve accurate and intelligent management; performing fusion judgment on various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result; the system can effectively solve the problems that goods in the warehousing system are difficult to identify in the processes of selecting, carrying and stacking the goods, and the system records the goods, and the accuracy of the starting and stopping positions of the goods is low.
In order to effectively solve the problems that goods are difficult to identify in the processes of goods selection, carrying and stacking in the warehousing system and the accuracy of the starting and stopping positions of the goods recorded by the system is low, the application provides an embodiment of a multi-mode fusion identification method based on the internet of things, and referring to fig. 1, the multi-mode fusion identification method based on the internet of things specifically comprises the following contents:
step S101: collecting sensor data sent by a sensor at a set position in the process of cargo transportation;
step S102: and performing fusion judgment on the various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result.
As can be seen from the above description, the multimode fusion identification method based on the internet of things provided by the embodiment of the application can acquire sensor data sent by a sensor at a set position in the process of cargo transportation; fusion judgment is carried out on the various sensor data, and weighting calculation is carried out on various judgment results to obtain a cargo identification result; the system can effectively solve the problems that goods in the warehousing system are difficult to identify in the processes of selecting, carrying and stacking the goods, and the system records the low accuracy of the starting and stopping positions of the goods.
In an embodiment of the internet of things-based multi-mode fusion identification method, the following may be specifically included:
step S201: the method comprises the steps that sensor data of the forklift during goods taking, walking and falling are collected through sensors arranged on the forklift and a goods label, and a first judgment result is obtained through acceleration correlation comparison of the sensor data;
step S202: acquiring initial input information, cargo weight and data in a database and judging through a sensor arranged on the platform scale to obtain a second judgment result;
step S203: collecting Bluetooth power data of the cargo tag for judgment through a sensor arranged in the elevator to obtain a third judgment result;
step S204: acquiring a cargo detection result when the cargo passes through the key position through an activator arranged at the key position to obtain a fourth judgment result;
step S205: and synthesizing the first judgment result, the second judgment result, the third judgment result and the fourth judgment result to obtain a fusion judgment result.
In an embodiment of the internet of things-based multi-mode fusion identification method, the method may further include the following steps:
step S301: processing input sensor data, performing weighted integral on each parameter index of the goods in the process of transporting the goods by a forklift, summing the integrals to obtain the integral of the goods, and determining the integral average value in the whole transporting process as the final integral of the goods;
step S302: and judging whether the goods are on the vehicle or not by comparing whether the integral of the goods is greater than a threshold or not, obtaining a goods candidate set of the vehicle after the vehicle drops the goods, comparing the candidate sets of all the vehicles after the goods drop, processing the condition that the same label is on different vehicles, and finally obtaining a result of the vehicle + goods set.
In an embodiment of the internet of things-based multi-mode fusion identification method, the method may further include the following steps:
step S401: adding the goods with high possibility into a candidate set, performing integral calculation on each goods in the candidate set, and judging that the goods are not on the vehicle and removing the goods when the integral is lower than a threshold value;
step S402: and analyzing the same goods existing in the candidate sets of the two vehicles, and deleting the goods with low possibility to obtain a final result of the forklift + goods candidate set.
In order to effectively solve the problems that goods are difficult to identify in the processes of goods selection, transportation and stacking in a warehousing system and the accuracy of the starting and stopping positions of goods recorded by the system is low, the application provides an embodiment of a multi-mode fusion identification device based on the internet of things, which is used for realizing all or part of contents of the multi-mode fusion identification method based on the internet of things, and the multi-mode fusion identification device based on the internet of things specifically comprises the following contents:
the data acquisition module 10 is used for acquiring sensor data sent by a sensor at a set position in the process of cargo transportation;
and the fusion judgment module 20 is configured to perform fusion judgment on the multiple sensor data, and perform weighted calculation on multiple judgment results to obtain a cargo identification result.
As can be seen from the above description, the multimode fusion recognition device based on the internet of things provided by the embodiment of the application can acquire sensor data sent by a sensor at a set position in the process of cargo transportation; fusion judgment is carried out on the various sensor data, and weighting calculation is carried out on various judgment results to obtain a cargo identification result; the system can effectively solve the problems that goods in the warehousing system are difficult to identify in the processes of selecting, carrying and stacking the goods, and the system records the low accuracy of the starting and stopping positions of the goods.
In an embodiment of the multi-mode fusion recognition device based on the internet of things, the fusion decision module 20 includes:
the first judgment unit is used for acquiring sensor data of the forklift during goods taking, walking and goods falling through sensors arranged on the forklift and the goods label, and obtaining a first judgment result through acceleration correlation comparison of the sensor data;
the second judgment unit is used for acquiring and judging initial input information, cargo weight and data in the database through a sensor arranged on the platform scale to obtain a second judgment result;
the third judgment unit is used for acquiring the Bluetooth power data of the cargo tag for judgment through a sensor arranged in the elevator to obtain a third judgment result;
the fourth judgment unit is used for acquiring a cargo detection result when the cargo passes through the key position through the activator arranged at the key position to obtain a fourth judgment result;
and the fusion judgment unit is used for integrating the first judgment result, the second judgment result, the third judgment result and the fourth judgment result to obtain a fusion judgment result.
In an embodiment of the multi-mode fusion recognition device based on the internet of things, the fusion decision module 20 includes:
the integral processing unit is used for processing the input sensor data, performing weighted integral on each parameter index of the goods in the goods transportation process of the forklift, summing the integrals to obtain the integral of the goods, and determining the integral average value of the whole transportation process to be used as the final integral of the goods;
and the candidate set processing unit is used for judging whether the goods are on the vehicle or not by comparing whether the integral of the goods is larger than a threshold or not, obtaining a candidate set of the goods of the vehicle after the vehicle drops the goods, comparing the candidate sets of all the vehicles after the goods drop, processing the condition that the same label is on different vehicles, and finally obtaining the result of the vehicle + goods set.
In an embodiment of the multi-mode fusion recognition device based on the internet of things, the fusion decision module 20 includes:
the goods identification is used for adding the goods with high possibility into the candidate set, performing integral calculation on each goods in the candidate set, and when the integral is lower than a threshold value, judging that the goods are not on the vehicle and removing the goods;
and the heavy goods eliminating unit is used for analyzing the same goods existing in the candidate set of the two vehicles, and deleting the goods with low possibility to obtain the final result of the forklift + goods candidate set.
In order to further explain the scheme, the application further provides a specific application example for implementing the multi-mode fusion recognition method based on the internet of things by using the multi-mode fusion recognition device based on the internet of things, which specifically includes the following contents:
referring to fig. 3, the cargo identification process mainly depends on the comprehensive analysis of the data of various sensors, and the identification result is finally obtained through the weighted calculation of various discrimination results. The label and the forklift are provided with sensors, and when the forklift is used for picking up, walking and dropping goods, the higher the correlation is, the higher the possibility that the label is on the forklift is. The elevator relies on the bluetooth power of receiving the label to differentiate, and the platform scale is judged through initial input information and goods weight and data in the database, and fork truck, platform scale, elevator carry out the goods interchange when, carry out the analysis with corresponding goods record like data processing center. An activator is placed at some strategic location so that when a good passes through that location, the corresponding good is detected. And (4) integrating the multiple conditions, and calculating the result of each module by the data processing center to obtain a final result.
Referring to fig. 4, the main flow of the determination process is shown. The goods identification process is mainly to process the input sensor data, then carry out the weight integral to each item parameter index of goods in fork truck transportation goods process, then add up each integral, as the integral of goods. The points are calculated in real time during the whole shipping process, and the average value of the points during the whole shipping process is obtained. As the final tally of the good. By comparing whether the integral of the good is greater than a threshold. And judging whether the goods are on the vehicle or not, and obtaining a goods candidate set of the vehicle after the vehicle falls off the goods. After the drop, the candidate sets for all vehicles are compared. And the condition that the same label is on different vehicles is processed. And finally, obtaining a result of vehicle + cargo aggregation.
Specifically, cargo identification is mainly divided into 4 processes: fork truck loading, fork truck operation, fork truck unloading and heavy goods elimination.
The identification of the cargo is mainly centered on the forklift + candidate set. The goods with high possibility are added into the candidate set at the beginning, the integral calculation (the calculation method is described later) is carried out on each goods in the candidate set along with the progress of the goods transportation process, and when the integral is lower than the threshold value, the goods are considered not to be on the vehicle and are removed. When the entire shipment is complete, a very high probability of cargo remains in the candidate set on each cart. At this time, since the vehicles may be working at the same time, the same cargo exists in the candidate set of two vehicles. It needs to be analyzed and goods with low probability of being deleted. Thus, the final result of the forklift + goods candidate set is finally obtained. And reporting to the service layer.
And (5) a forklift goods taking process. Since it is not possible to determine which good was specifically picked up, it is necessary to find possible goods calculation points and add them to the candidate set. 1 retrieving from the database, depending on the truck position, a set of goods in the vicinity of the position that has been saved. 2 the goods near the position, which have violent motion and are close to the acceleration of the forklift truck, are added into a candidate set. And 3, adding the goods with the weight same as that of the goods lifted at this time into the candidate set. And 4, adding the goods activated by the activator into the candidate set. 5 if the goods are taken at the elevator or the weighing machine, setting a larger credit for the goods to be added into the candidate set. In this way, all possible goods have been added to the candidate set.
The forklift is operated. When the running speed of the vehicle changes or the vehicle bumps along with the running of the forklift, the acceleration of the goods at the moment is close to the acceleration of the forklift. At this point a weighted integral is calculated and the result of the integral is averaged with the historical value. If the integral is lower than the preset threshold M, the acceleration of the goods is relatively different from the acceleration of the vehicle, and the goods are not on the vehicle. It is removed from the candidate set. Similarly, when the vehicle is moved to a location where an activator is present, the cargo is activated. At the moment, integral calculation of the activation intensity is carried out, and goods lower than a threshold are removed.
And (5) dropping the goods by using a forklift. Processing is similar to the previous process, and acceleration integral is calculated and eliminated. The activator integrals are calculated and rejected. If the elevator or the scale is used, a larger integral is set, and goods below a threshold are rejected. After the goods are dropped, the whole goods transportation process is completed. However, since there may be a case where a plurality of vehicles are shipped at the same time, mutual interference occurs at this time. This requires the final result to be collated for restocking elimination.
Referring to fig. 5, the heavy goods are eliminated. After the shipment is dropped, a candidate set of shipments for each vehicle is obtained, but since vehicles operating simultaneously interfere with each other, there is a case where one shipment is determined to exist in a plurality of vehicles at the same time. However, the cargo points are different due to different cargo transportation processes of different vehicles, and at the moment, cargo points in the candidate sets of a plurality of vehicles are compared, and only one with a larger point is left. This results in the final car number + cargo collection.
Specifically, the input data in the discrimination process is mainly external sensor data. And performing weighted integral calculation in the discrimination process. The input data and the weighted integral calculation method are as follows:
1. acceleration of the forklift. Acceleration sensors are arranged on the vehicle and the label, and when the vehicle moves, the acceleration of the label on the vehicle is close to the acceleration of the vehicle, but not opposite to the acceleration of the label on the vehicle.
2. Tag (cargo) acceleration. The tag is a bluetooth device affixed to the cargo and represents the cargo. And calculating to obtain a correlation weighted integral V acceleration by comparing the acceleration of the forklift and the acceleration of the label. The acceleration integral is calculated by the formula:
the acceleration V is K acceleration X abs (a truck-acceleration-a truck-acceleration). Wherein: the Vacceleration is the acceleration weighted integral. The acceleration K is the acceleration weight, the goods acceleration A is the acceleration of the goods, and the vehicle acceleration A is the acceleration of the vehicle.
3. The cargo is activated by the activator. The vehicle and gate locations will be equipped with an activator and when the vehicle passes through the location, the tag will be activated accordingly. The closer the distance, the higher the activation intensity. The activation integral calculation formula is as follows:
v activation — K activation X A activation. Wherein: vactive is the activation integral, Kactive is the activation weight, and A active is the activated strength of the tag.
4. Information of goods lifted and dropped by the platform scale. And the platform scale lifts and lowers the goods information. Other interference conditions may be masked due to the cargo on the scale. A larger integrated value can be given to the cargo at this time. When the load is picked up from the scale: the scale integral formula is as follows: and the V platform scale is A platform scale. When goods are placed to the platform scale from the forklift, the elevator integral formula is as follows: the V scale is-A scale. Wherein: the V scale is the scale integral of the goods, and the A scale is the maximum scale weighted integral value of the goods. -a scales is the smallest scale weighted integral value of the cargo.
5. Elevator landing and landing cargo information. Elevator landing and landing cargo information. The weighted integral of the elevator is large and a fixed value, because the nearby disturbance conditions are small when landing a load in the elevator. When the goods are taken from the elevator, the goods are added to the candidate set with a larger credit. The elevator integral formula is as follows: v elevator is A elevator. When goods are placed into the elevator from the forklift, the elevator integral formula is as follows: v elevator is equal to-A elevator. Wherein: the V elevator is the elevator integral of the goods, and the A elevator is the maximum elevator weighted integral value of the goods. Elevator a is the smallest elevator weighted integral value of the cargo.
6. A forklift position. (since the cargo position cannot be obtained, the fork truck position needs to be used to deduce the cargo position). After each cargo drop, the system can determine the position information of the label and store the position information into the database. The next time the load is moved again, the location of the nearby tag can be retrieved from the database based on the truck position. But which one is not yet determinable, a weight may be given depending on the location. Although there is the greatest weight, it is still uncertain whether it is the good. More processes are required to be subsequently performed for identification. The position weighted integration is:
v position ═ K position X abs (Lmax-dis (L good-position, L cart-position)). where: the position V is a position integral, the position K is a position weighted value, Lmax is the maximum allowable distance of the cargos, the position L of the cargos is a retrieved cargo position, and the position L of the cargos is a vehicle position. dis is a calculated distance function.
7. The integral value of this time. With the continuous movement of the vehicle, the sensor data is continuously collected and the integral value is calculated. The integrated values of the plurality of sensors are added to the integrated value of the load at this time, and the calculation formula is as follows:
v this time is V acceleration + V activation + V platform scale + V elevator.
The current integral value. And in the integral calculation process, the historical integral values are always added, after a new integral is received, the integral value is accumulated to the historical integral sum, and then the average value is obtained to obtain the current integral value. The formula is as follows: and V history is V history + V this time. Count is Count + 1. V current ═ V history/Count. Wherein: vhistory is the sum of historical points, and Count is the number of historical points. And V is the current integration result.
As can be seen from the above, the present application can also achieve at least the following technical effects:
(1) the efficiency of the warehouse management system is significantly improved and optimized.
(2) The security of goods, in particular valuable items, is enhanced.
(3) The accuracy and the reliability of goods identification are obviously improved.
In order to effectively solve the problems that goods are difficult to identify in the processes of selecting, carrying and stacking the goods in the warehousing system and the accuracy of the starting and stopping positions of the goods recorded by the system is low in the hardware aspect, the application provides an embodiment of electronic equipment for realizing all or part of contents in the multi-mode fusion identification method based on the internet of things, and the electronic equipment specifically comprises the following contents:
a processor (processor), a memory (memory), a communication Interface (Communications Interface), and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the communication interface is used for realizing information transmission between the multi-mode fusion recognition device based on the Internet of things and relevant equipment such as a core service system, a user terminal, a relevant database and the like; the logic controller may be a desktop computer, a tablet computer, a mobile terminal, and the like, but the embodiment is not limited thereto. In this embodiment, the logic controller may be implemented with reference to the embodiment of the multi-mode fusion recognition method based on the internet of things and the embodiment of the multi-mode fusion recognition device based on the internet of things in the embodiment, and the contents thereof are incorporated herein, and repeated details are not repeated here.
It is understood that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a Personal Digital Assistant (PDA), an in-vehicle device, a smart wearable device, and the like. Wherein, intelligence wearing equipment can include intelligent glasses, intelligent wrist-watch, intelligent bracelet etc..
In practical applications, part of the multi-mode fusion recognition method based on the internet of things may be executed on the electronic device side as described above, or all operations may be completed in the client device. The selection may be specifically performed according to the processing capability of the client device, the limitation of the user usage scenario, and the like. This is not a limitation of the present application. The client device may further include a processor if all operations are performed in the client device.
The client device may have a communication module (i.e., a communication unit), and may be communicatively connected to a remote server to implement data transmission with the server. The server may include a server on the task scheduling center side, and in other implementation scenarios, the server may also include a server on an intermediate platform, for example, a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may include a single computer device, or may include a server cluster formed by a plurality of servers, or a server structure of a distributed apparatus.
Fig. 6 is a schematic block diagram of a system configuration of an electronic device 9600 according to an embodiment of the present application. As shown in fig. 6, the electronic device 9600 can include a central processor 9100 and a memory 9140; the memory 9140 is coupled to the central processor 9100. Notably, this FIG. 6 is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In an embodiment, the functions of the multi-mode fusion recognition method based on the internet of things can be integrated into the central processor 9100. The central processor 9100 can be configured to perform the following control:
step S101: collecting sensor data sent by a sensor at a set position in the process of cargo transportation;
step S102: and performing fusion judgment on the various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result.
As can be seen from the above description, the electronic device provided in the embodiment of the present application collects sensor data sent by a sensor at a set position during the transportation of goods; performing fusion judgment on various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result; the system can effectively solve the problems that goods in the warehousing system are difficult to identify in the processes of selecting, carrying and stacking the goods, and the system records the low accuracy of the starting and stopping positions of the goods.
In another embodiment, the multi-mode fusion recognition device based on the internet of things may be configured separately from the central processor 9100, for example, the multi-mode fusion recognition device based on the internet of things may be configured as a chip connected to the central processor 9100, and the function of the multi-mode fusion recognition method based on the internet of things is realized through the control of the central processor.
As shown in fig. 6, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is noted that the electronic device 9600 also does not necessarily include all of the components shown in fig. 6; further, the electronic device 9600 may further include components not shown in fig. 6, which may be referred to in the art.
As shown in fig. 6, the central processor 9100, which is sometimes referred to as a controller or operational control, can include a microprocessor or other processor device and/or logic device, the central processor 9100 receives input and controls the operation of various components of the electronic device 9600.
The memory 9140 can be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 9100 can execute the program stored in the memory 9140 to realize information storage or processing, or the like.
The input unit 9120 provides input to the central processor 9100. The input unit 9120 is, for example, a key or a touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used for displaying display objects such as images and characters. The display may be, for example, but is not limited to, an LCD display.
The memory 9140 can be a solid state memory, e.g., Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes referred to as an EPROM or the like. The memory 9140 could also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application/function storage portion 9142, the application/function storage portion 9142 being used for storing application programs and function programs or for executing a flow of operations of the electronic device 9600 by the central processor 9100.
The memory 9140 can also include a data store 9143, the data store 9143 being used to store data, such as contacts, digital data, pictures, sounds, and/or any other data used by an electronic device. The driver storage portion 9144 of the memory 9140 may include various drivers for the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging applications, contact book applications, etc.).
The communication module 9110 is a transmitter/receiver 9110 that transmits and receives signals via an antenna 9111. The communication module (transmitter/receiver) 9110 is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 9110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 9110 is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130 to provide audio output via the speaker 9131 and receive audio input from the microphone 9132, thereby implementing ordinary telecommunications functions. The audio processor 9130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 9130 is also coupled to the central processor 9100, thereby enabling recording locally through the microphone 9132 and enabling locally stored sounds to be played through the speaker 9131.
The embodiment of the present application further provides a computer-readable storage medium capable of implementing all the steps in the method for identifying a multi-mode fusion based on the internet of things, where an execution subject of the method is a server or a client in the above embodiments, and the computer-readable storage medium stores a computer program, where the computer program, when executed by a processor, implements all the steps in the method for identifying a multi-mode fusion based on the internet of things, where an execution subject of the computer program is a server or a client, for example, the processor implements the following steps when executing the computer program:
step S101: collecting sensor data sent by a sensor at a set position in the process of cargo transportation;
step S102: and performing fusion judgment on the various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result.
As can be seen from the above description, the computer-readable storage medium provided in the embodiments of the present application collects sensor data sent by a sensor at a set position during transportation of a cargo; performing fusion judgment on various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result; the system can effectively solve the problems that goods in the warehousing system are difficult to identify in the processes of selecting, carrying and stacking the goods, and the system records the goods, and the accuracy of the starting and stopping positions of the goods is low.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (6)
1. A multi-mode fusion recognition method based on the Internet of things is characterized by comprising the following steps:
collecting sensor data sent by a sensor at a set position in the process of cargo transportation;
performing fusion judgment on various sensor data, and performing weighted calculation on various judgment results to obtain a cargo identification result;
the fusion judgment of the sensor data comprises the following steps:
acquiring sensor data of the forklift during cargo lifting, walking and cargo falling through sensors arranged on the forklift and the cargo label, and obtaining a first judgment result through acceleration correlation comparison of the sensor data;
acquiring initial input information, cargo weight and data in a database through a sensor arranged on the platform scale and judging to obtain a second judgment result;
collecting Bluetooth power data of the goods label for judgment through a sensor arranged in the elevator to obtain a third judgment result;
acquiring a cargo detection result when the cargo passes through the key position through an activator arranged at the key position to obtain a fourth judgment result;
synthesizing the first judgment result, the second judgment result, the third judgment result and the fourth judgment result to obtain a fusion judgment result;
the weighting calculation of the multiple obtained judgment results to obtain the cargo identification result includes:
processing input sensor data, performing weighted integral on each parameter index of the goods in the process of transporting the goods by a forklift, summing the integrals to obtain the integral of the goods, and determining the integral average value in the whole transporting process as the final integral of the goods;
and judging whether the goods are on the vehicle or not by comparing whether the integral of the goods is larger than a threshold or not, obtaining a candidate set of the goods of the vehicle after the goods are dropped, comparing the candidate sets of all the vehicles after the goods are dropped, processing the condition that the same label is on different vehicles, and finally obtaining a result of the vehicle plus the goods set.
2. The method for multi-mode fusion recognition based on the internet of things of claim 1, wherein the fusion decision is performed on the multiple sensor data, and the weighted calculation is performed on the multiple obtained decision results to obtain a cargo recognition result, further comprising:
adding the goods with high possibility into a candidate set, performing integral calculation on each goods in the candidate set, and judging that the goods are not on the vehicle and removing the goods when the integral is lower than a threshold value;
and analyzing the same goods existing in the candidate sets of the two vehicles, and deleting the goods with low possibility to obtain a final result of the forklift + goods candidate set.
3. The utility model provides a multimode fuses recognition device based on thing networking which characterized in that includes:
the data acquisition module is used for acquiring sensor data sent by a sensor at a set position in the process of cargo transportation;
the fusion judgment module is used for performing fusion judgment on various sensor data and performing weighted calculation on various judgment results to obtain a cargo identification result;
the fusion judgment module comprises:
the first judgment unit is used for acquiring sensor data of the forklift during lifting, walking and dropping goods through the forklift and sensors arranged on the goods label, and obtaining a first judgment result through acceleration correlation comparison of the sensor data;
the second judgment unit is used for acquiring and judging initial input information, cargo weight and data in the database through a sensor arranged on the platform scale to obtain a second judgment result;
the third judgment unit is used for acquiring Bluetooth power data of the goods label for judgment through a sensor arranged in the elevator to obtain a third judgment result;
the fourth judgment unit is used for acquiring a cargo detection result when the cargo passes through the position through the activator arranged at the key position to obtain a fourth judgment result;
the fusion judgment unit is used for integrating the first judgment result, the second judgment result, the third judgment result and the fourth judgment result to obtain a fusion judgment result;
the fusion decision module further comprises:
the integral processing unit is used for processing the input sensor data, performing weighted integral on each parameter index of the goods in the goods transportation process of the forklift, summing the integrals to obtain the integral of the goods, and determining the integral average value of the whole transportation process as the final integral of the goods;
and the candidate set processing unit is used for judging whether the goods are on the vehicle or not by comparing whether the integral of the goods is larger than a threshold or not, obtaining a candidate set of the goods of the vehicle after the goods are dropped from the vehicle, comparing the candidate sets of all the vehicles after the goods are dropped, processing the condition that the same label is on different vehicles, and finally obtaining the result of the vehicle + goods set.
4. The device for multi-mode fusion recognition based on the internet of things of claim 3, wherein the fusion decision module comprises:
the goods identification unit is used for adding goods with high possibility into the candidate set, performing integral calculation on each goods in the candidate set, and judging that the goods are not on the vehicle and rejecting the goods when the integral is lower than a threshold value;
and the heavy goods eliminating unit is used for analyzing the same goods existing in the candidate set of the two vehicles, and deleting the goods with low possibility to obtain the final result of the forklift + goods candidate set.
5. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for identifying fusion of multiple ways based on internet of things according to any one of claims 1 to 2 when executing the program.
6. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for internet of things based multimodal fusion recognition according to any of the claims 1 to 2.
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