Entity tracking and localization method

Abstract

The application relates to the transportation positioning technical field, and particularly discloses an entity tracking and positioning method, which comprises the following steps: querying a transportation task in a production line, determining a to-be-positioned subject and a motion path of the to-be-positioned subject based on the transportation task; determining an information acquisition device based on the motion path, determining a collection feature corresponding to the information acquisition device based on the to-be-positioned subject; receiving data acquired by the information acquisition device in real time, traversing and querying the collection feature in the data, determining a target subject and a motion trajectory; regularly counting all target subjects and motion trajectories, generating a subject distribution diagram at a current time, verifying the subject distribution diagram by a preset radar device, and updating the information acquisition device according to a verification result. The information acquisition device is limited to an image acquisition device, a positioning mapping relationship between the information acquisition device and a color value label is established by setting the color value label on the product, the real-time identification requirement of the identification process is not high, and the robustness is extremely high.

Description

Technical Field

[0001] This invention relates to the field of transportation positioning technology, specifically a physical tracking and positioning method. Background Technology

[0002] In intelligent workshops, physical tracking and positioning technology is indispensable in the transportation control systems of various production equipment or products. However, most existing physical tracking and positioning technologies rely on real-time positioning methods using Bluetooth or other wireless information. This method has extremely high requirements for recognition speed. If any problems occur in the transmission or recognition process, errors are very likely to occur. Therefore, how to provide a more robust physical tracking and positioning method is the technical problem that this invention aims to solve. Summary of the Invention

[0003] The purpose of this invention is to provide an entity tracking and positioning method to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] An entity tracking and localization method, the method comprising:

[0006] Query the transportation tasks in the production line, and determine the subject to be located and its movement path based on the transportation tasks;

[0007] The information acquisition device is determined based on the motion path, and the acquisition features corresponding to the information acquisition device are determined based on the subject to be located.

[0008] The system receives data from information acquisition devices in real time, iterates through and queries the acquisition features in the data, and determines the target subject and its movement trajectory; the movement trajectory is a movement path containing time information.

[0009] The system periodically collects statistics on all target entities and their movement trajectories, generates a current-time entity distribution map, verifies the entity distribution map using pre-set radar equipment, and updates the information acquisition equipment based on the verification results.

[0010] As a further aspect of the present invention: the step of querying transportation tasks in the production line and determining the subject to be located and its movement path based on the transportation tasks includes:

[0011] Receive product labels input by users and query the production line in the registered production process based on the product labels;

[0012] Get the task list in the production line and query the transportation tasks in the task list that contain the subject to be located;

[0013] The transport vehicle and its control components for the transport task are queried, and the movement path is determined based on the control components.

[0014] As a further aspect of the present invention: the step of determining the information acquisition device based on the motion path and determining the acquisition features corresponding to the information acquisition device based on the subject to be located includes:

[0015] Obtain the control components and transport vehicle corresponding to the motion path;

[0016] The system iterates through the locators in the transport vehicle. If a locator exists, a communication channel is established with the locator. If no locator exists, a locator of a preset model is installed according to the physical parameters of the transport vehicle.

[0017] Query the preset importance level of the transport vehicle in the transport task, and select the sampling frequency according to the importance level;

[0018] Based on the sampling frequency, determine and install the information acquisition equipment that is compatible with the locator;

[0019] Based on the subject to be located, the collection features corresponding to the information collection device are determined.

[0020] As a further aspect of the present invention: the step of determining the acquisition features corresponding to the information acquisition device based on the subject to be located includes:

[0021] When the information acquisition device is an image acquisition device, the locator is a programmable LED light;

[0022] Query all transportation tasks of the transportation vehicle and the subject to be located, obtain the task volume of the subject to be located, and determine the display gradient based on the maximum and minimum values ​​of the task volume and the number of transportation tasks; the task volume contains a field that distinguishes the subject to be located.

[0023] The display color values ​​of the locator under different workloads are determined based on the display gradient, and used as the acquisition features.

[0024] As a further aspect of the present invention: the step of determining the target subject and its motion trajectory by traversing and querying the collected features in the data obtained by the real-time receiving information acquisition device includes:

[0025] The system receives data from an information acquisition device in real time. When the information acquisition device is an image acquisition device, the data is an image.

[0026] Color value recognition is performed on the image to locate and collect feature regions;

[0027] The feature area is subjected to feature recognition to obtain the feature, the task volume is determined based on the feature, and the corresponding subject to be located is queried as the target subject.

[0028] Extract the position of each target subject in the image, and statistically analyze the position based on the time information to obtain the motion trajectory.

[0029] As a further aspect of the present invention: the step of periodically statistically analyzing all target subjects and their motion trajectories to generate a subject distribution map at the current moment, verifying the subject distribution map with a preset radar device, and updating the information acquisition device based on the verification results includes:

[0030] The system periodically collects statistics on all target subjects and their movement trajectories within a preset time interval, and determines the current position and velocity of all subjects to be located at the current moment.

[0031] Based on the preset mapping relationship, all subjects to be located and their positions are mapped onto the preset empty map to obtain the subject distribution map;

[0032] The actual motion parameters of each subject to be inspected are obtained by a preset radar device, and the abnormal subject is located in the subject distribution map based on the actual motion parameters;

[0033] When an abnormal entity is detected, the location result is sent to the manual detection terminal, and the information collection equipment is updated according to the investigation instructions fed back by the manual detection terminal.

[0034] As a further aspect of the present invention: the step of acquiring the actual motion parameters of each subject under inspection by a preset radar device includes:

[0035] The detection wavelength is randomly determined within a preset band range, and at least two detection wavelengths are used.

[0036] At least two detection waves are sent at preset intervals, and echo signals are received in real time to obtain an echo table corresponding to the two detection waves.

[0037] The distance table is determined based on the echo table and the detection wave, and the actual motion parameters of each subject under inspection are determined based on each distance table; the actual motion parameters include position and velocity.

[0038] As a further aspect of the present invention: when the information acquisition device is an image acquisition device, the information acquisition device includes an infrared module; when the image acquisition mode is a fog-penetrating mode, the image acquisition device establishes a connection channel with the infrared module, and uses the infrared module as a signal emission source to acquire the image.

[0039] As a further aspect of the present invention: the step of acquiring an image using the infrared module as a signal emission source includes:

[0040] The converged visible and infrared light is filtered to remove the near-infrared-visible transition band from the visible and infrared light.

[0041] During each exposure cycle, at least once, a first RGBIR image is generated by sensing filtered visible light and infrared light based on the saved first exposure duration; at least once, a second RGBIR image is generated by sensing filtered visible light and infrared light based on the saved second exposure duration; wherein the first exposure duration and the second exposure duration may be the same or different.

[0042] For each first RGBIR image, the infrared (IR) component is removed from each first RGBIR image, and the average brightness of the corresponding RGB image is calculated. Similarly, for each second RGBIR image, the RGB component is removed, and the average brightness of the corresponding IR image is calculated. Based on the average brightness of each RGB image and a preset first target brightness value, the first exposure time is adjusted. Based on the average brightness of each IR image and a preset second target brightness value, the second exposure time is adjusted. The adjusted exposure time is then communicated to the RGBIR sensor, and a fog-penetrating image is generated based on each first RGBIR image and each second RGBIR image.

[0043] As a further aspect of the present invention: the image acquisition device further includes a control module, the execution process of which includes:

[0044] The collected road condition images are converted into photoelectric data, and the converted data are statistically analyzed to obtain a histogram.

[0045] The histogram distribution changes are judged to determine whether there is smog and the smog concentration; in non-smog scenes, the image histogram distribution is relatively uniform, while in smog scenes, the image histogram is basically concentrated in the middle area, and the heavier the smog, the more concentrated the histogram.

[0046] When the histogram indicates the presence of haze, the image acquisition mode is set to fog-penetrating mode.

[0047] Compared with the prior art, the beneficial effects of the present invention are: the present invention limits the information acquisition device to an image acquisition device, and establishes a positioning mapping relationship between the information acquisition device and the color value label by setting color value labels on the product. This method does not have high requirements for the real-time performance of the recognition process. That is, if the data transmission channel is interrupted at a certain moment, the information acquisition device will still work normally. When the data transmission channel is restored, recognition can continue. It has extremely high robustness. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0049] Figure 1 A flowchart of the entity tracking and localization method.

[0050] Figure 2 This is the first sub-flowchart of the entity tracking and localization method.

[0051] Figure 3 This is the second sub-flowchart of the entity tracking and localization method.

[0052] Figure 4 This is the third sub-flowchart of the entity tracking and positioning method.

[0053] Figure 5 This is the fourth sub-flowchart of the entity tracking and positioning method. Detailed Implementation

[0054] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0055] Example 1

[0056] Figure 1 The flowchart below illustrates an entity tracking and localization method. In this embodiment of the invention, an entity tracking and localization method includes:

[0057] Step S100: Query the transportation tasks in the production line, and determine the subject to be located and its movement path based on the transportation tasks;

[0058] For a company, each product has a pre-stored production line. The production line contains production tasks and transportation tasks. The transportation tasks can determine the subject to be positioned and its movement path. The subject to be positioned generally includes pallets, collapsible boxes, turnover boxes, pallet boxes, material racks, and insulated boxes. The movement path is the displacement adjustment device that drives these subjects to be positioned. The movement path is fixed and is existing data that can be read.

[0059] Step S200: Determine the information acquisition device based on the motion path, and determine the acquisition features corresponding to the information acquisition device based on the subject to be located;

[0060] The motion path determines the information acquisition device, which is used to obtain the position of the subject to be located. To obtain the position of the subject to be located, a recognition feature (acquisition feature) needs to be preset. The recognition feature is a feature that can be mutually recognized between the subject to be located and the information acquisition device.

[0061] Step S300: Receive data acquired by the information acquisition device in real time, traverse and query the acquisition features in the data, and determine the target subject and its movement trajectory; the movement trajectory is a movement path containing time information;

[0062] The system receives data from the information acquisition device in real time, identifies the data, and identifies the target subject based on the acquisition features set in the preprocessing process (step S200). Based on the identification results at different times, the system determines the movement trajectory of each target subject. The difference between the movement trajectory and the movement path is that the movement trajectory contains time information.

[0063] Step S400: Periodically collect statistics on all target subjects and their movement trajectories, generate a subject distribution map at the current moment, verify the subject distribution map with preset radar equipment, and update the information acquisition equipment based on the verification results;

[0064] The actual motion parameters of each subject to be located in the scene are obtained by a preset radar device. The actual motion parameters include position and speed. The subject distribution map can be verified by the actual motion parameters, and then the generation process of the subject distribution map can be adjusted.

[0065] It should be noted that the technical solution of this invention aims to generate a main body distribution map. Radar equipment can only obtain motion parameters based on field waves, which is less intuitive than a main body distribution map.

[0066] Figure 2 This is a flowchart of the first sub-process of the entity tracking and positioning method. The step of querying transportation tasks in the production line and determining the entity to be located and its movement path based on the transportation tasks includes:

[0067] Step S101: Receive the product label input by the user, and query the production line in the registered production process based on the product label;

[0068] Step S102: Obtain the task list in the production line and query the transportation tasks in the task list that contain the subject to be located;

[0069] Step S103: Query the transport vehicle and its control components for the transport task, and determine the movement path based on the control components.

[0070] The above describes the process of generating the target object and its movement path. First, the product label input by the user is received. Different products correspond to different production processes, and the production process determines the production line.

[0071] A production line consists of multiple tasks; we will focus on analyzing the transportation task.

[0072] Finally, the transport vehicle and its control components for the transport task are queried, and the possible movement paths of the transport vehicle are determined by the structure of the control components themselves.

[0073] Figure 3 This is a second sub-flowchart of the entity tracking and localization method. The steps of determining the information acquisition device based on the motion path and determining the acquisition features corresponding to the information acquisition device based on the subject to be located include:

[0074] Step S201: Obtain the control components and transport vehicle corresponding to the motion path;

[0075] Step S202: Iterate through and query the locators in the transport vehicle. When a locator exists, establish a communication channel with the locator. When no locator exists, install a locator of a preset model according to the physical parameters of the transport vehicle.

[0076] Step S203: Query the preset importance level of the transport vehicle in the transport task, and select the sampling frequency according to the importance level;

[0077] Step S204: Determine and install the information acquisition equipment that is compatible with the locator based on the sampling frequency;

[0078] Step S205: Determine the acquisition features corresponding to the information acquisition device based on the subject to be located.

[0079] Steps S201 to S205 define the installation process of the information acquisition device and the parameter setting process of the subject to be located. First, the control components and transport vehicle of the motion path are queried, which can be read directly. Then, a locator is set in the transport vehicle (if it exists, an existing locator is used). Finally, the importance of the transport vehicle is queried, and different information processing devices are selected according to the importance, thereby determining the locator acquisition features corresponding to the information processing device.

[0080] As a preferred embodiment of the technical solution of the present invention, the step of determining the acquisition features corresponding to the information acquisition device based on the subject to be located includes:

[0081] When the information acquisition device is an image acquisition device, the locator is a programmable LED light;

[0082] Query all transportation tasks of the transportation vehicle and the subject to be located, obtain the task volume of the subject to be located, and determine the display gradient based on the maximum and minimum values ​​of the task volume and the number of transportation tasks; the task volume contains a field that distinguishes the subject to be located.

[0083] The display color values ​​of the locator under different workloads are determined based on the display gradient, and used as the acquisition features.

[0084] In one example of the technical solution of this invention, the information acquisition device and the locator are defined. The simplest way is to set the locator as a signal source such as sound waves, and the information acquisition device acquires and identifies the signal emitted by the signal source to determine the position of each subject to be located. Although this method is relatively easy to design, it requires real-time data processing. That is, once the data is acquired, it needs to be identified. When the network is interrupted, the identification process will fail. Therefore, this invention uses an image acquisition device (information acquisition device) and color value tags (LED lights) to set the acquisition and identification process.

[0085] Specifically, the function of color value tags is to reflect the identity information of the subject to be located. There are many types of color value tags, as long as they can distinguish different subjects to be located. The number of subjects to be located is generally limited, and the number of color value tag types is sufficient.

[0086] Figure 4 The third sub-flowchart of the entity tracking and positioning method includes the following steps: receiving data from the real-time information acquisition device, traversing and querying the acquisition features in the data, and determining the target entity and its motion trajectory.

[0087] Step S301: Receive data acquired by the information acquisition device in real time. When the information acquisition device is an image acquisition device, the data is an image.

[0088] Step S302: Perform color value recognition on the image to locate and collect feature regions;

[0089] Step S303: Perform feature recognition on the collected feature area to obtain the collected features, determine the task volume based on the collected features, and query the corresponding subject to be located as the target subject;

[0090] Step S304: Extract the position of each target subject in the image, and calculate the position based on the time information to obtain the motion trajectory.

[0091] The specific application process of steps S301 to S304 can be regarded as the reverse process of steps S201 to S205: the acquired image is identified, the acquired features (color value labels) in the image are located, and the corresponding subject to be located is queried according to the acquired features, which is the target subject.

[0092] By statistically analyzing the target subject at different times, the movement trajectory can be obtained.

[0093] Figure 5 The fourth sub-flowchart of the entity tracking and positioning method includes the following steps: periodically statistically analyzing all target entities and their trajectories to generate a current entity distribution map; verifying the entity distribution map with a preset radar device; and updating the information acquisition device based on the verification results.

[0094] Step S401: Periodically count all target subjects and their trajectories within a preset time interval, and determine the current position and current speed of all subjects to be located at the current moment;

[0095] Step S402: Map all subjects to be located and their positions onto a preset empty map according to the preset mapping relationship to obtain a subject distribution map;

[0096] Step S403: The actual motion parameters of each subject to be inspected are obtained by the preset radar equipment, and the abnormal subject is located in the subject distribution map according to the actual motion parameters;

[0097] Step S404: When an abnormal entity is found, the location result is sent to the manual detection terminal, and the information collection device is updated according to the investigation instructions fed back by the manual detection terminal.

[0098] The process from steps S401 to S404 is not difficult. The radar acquires the actual motion parameters and checks the generated subject distribution map for any abnormal target subjects. If any are found, it indicates a problem in the radar's detection process or the application of the information acquisition equipment, requiring investigation. It is worth noting that radar equipment has high energy consumption; therefore, periodic checks are sufficient.

[0099] In one embodiment of the technical solution of the present invention, the step of acquiring the actual motion parameters of each subject to be detected by a preset radar device includes:

[0100] The detection wavelength is randomly determined within a preset band range, and at least two detection wavelengths are used.

[0101] At least two detection waves are sent at preset intervals, and echo signals are received in real time to obtain an echo table corresponding to the two detection waves.

[0102] The distance table is determined based on the echo table and the detection wave, and the actual motion parameters of each subject under inspection are determined based on each distance table; the actual motion parameters include position and velocity.

[0103] Traditional radar operates in a simplistic manner, relying solely on the Doppler principle to transmit sound waves and determine the speed of abnormal moving objects. However, in reality, dust concentrations vary under different weather conditions, and a single measurement method often leads to inaccuracies. Therefore, the radar equipment described above first randomly selects several detection wavelengths within a preset band range. For each detection wavelength, multiple detection waves are transmitted. Based on the echo signals and wave propagation speed, motion parameters can be determined. It's worth noting that different moving objects may exist on a road segment, and the echoes may be trapezoidal. Therefore, the above describes the generation of an echo table, where each echo represents one object.

[0104] As a preferred embodiment of the technical solution of the present invention, when the information acquisition device is an image acquisition device, the information acquisition device includes an infrared module. When the image acquisition mode is a fog-penetrating mode, the image acquisition device establishes a connection channel with the infrared module and uses the infrared module as a signal emission source to acquire the image.

[0105] Furthermore, the step of acquiring an image using the infrared module as a signal emission source includes:

[0106] The converged visible and infrared light is filtered to remove the near-infrared-visible transition band from the visible and infrared light.

[0107] During each exposure cycle, at least once, a first RGBIR image is generated by sensing filtered visible light and infrared light based on the saved first exposure duration; at least once, a second RGBIR image is generated by sensing filtered visible light and infrared light based on the saved second exposure duration; wherein the first exposure duration and the second exposure duration may be the same or different.

[0108] For each first RGBIR image, the infrared (IR) component is removed from each first RGBIR image, and the average brightness of the corresponding RGB image is calculated. Similarly, for each second RGBIR image, the RGB component is removed, and the average brightness of the corresponding IR image is calculated. Based on the average brightness of each RGB image and a preset first target brightness value, the first exposure time is adjusted. Based on the average brightness of each IR image and a preset second target brightness value, the second exposure time is adjusted. The adjusted exposure time is then communicated to the RGBIR sensor, and a fog-penetrating image is generated based on each first RGBIR image and each second RGBIR image.

[0109] Specifically, the image acquisition device further includes a control module, and the execution process of the control module includes:

[0110] The collected road condition images are converted into photoelectric data, and the converted data are statistically analyzed to obtain a histogram.

[0111] The histogram distribution changes are judged to determine whether there is smog and the smog concentration; in non-smog scenes, the image histogram distribution is relatively uniform, while in smog scenes, the image histogram is basically concentrated in the middle area, and the heavier the smog, the more concentrated the histogram.

[0112] When the histogram indicates the presence of haze, the image acquisition mode is set to fog-penetrating mode.

[0113] The core of the control module is that the image histogram distribution is relatively uniform in non-smog scenes, while in smog scenes the image histogram is basically concentrated in the middle area. The heavier the smog, the more concentrated the histogram. Statistical analysis of the acquired histograms can be performed to analyze the smog status and then perform a simple review of the monitoring data of the environmental monitoring terminal 10.

[0114] The functions of the entity tracking and localization method are all performed by a computer device, which includes one or more processors and one or more memories. The one or more memories store at least one piece of program code, which is loaded and executed by the one or more processors to implement the functions of the entity tracking and localization method.

[0115] The processor fetches instructions from memory one by one, analyzes the instructions, and then performs the corresponding operations according to the instructions, generating a series of control commands to enable the various parts of the computer to act automatically, continuously, and in a coordinated manner, forming an organic whole. This enables the input of programs and data, as well as the calculation and output of results. The arithmetic or logical operations generated in this process are all performed by the arithmetic unit. The memory includes a read-only memory (ROM), which is used to store computer programs. The memory is equipped with external protection devices.

[0116] For example, a computer program can be divided into one or more modules, one or more of which are stored in memory and executed by a processor to perform the present invention. The one or more modules can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0117] Those skilled in the art will understand that the above description of the service equipment is merely an example and does not constitute a limitation on the terminal equipment. It may include more or fewer components than described above, or a combination of certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0118] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. This processor is the control center of the terminal device, connecting various parts of the user terminal via various interfaces and lines.

[0119] The aforementioned memory can be used to store computer programs and / or modules. The aforementioned processor implements various functions of the aforementioned terminal device by running or executing the computer programs and / or modules stored in the memory, and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as information collection template display function, product information publishing function, etc.); the data storage area may store data created based on the use of the berth status display system (such as product information collection templates corresponding to different product types, product information that different product providers need to publish, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0120] If the modules / units integrated into the terminal device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the modules / units in the systems of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the functions of the various system embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0121] It should be noted that, in this document, 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. Unless otherwise specified, 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 that element.

[0122] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A method of entity tracking localization, the method comprising: The method comprises: querying a transportation task in a production line, determining a subject to be positioned and a motion path thereof based on the transportation task; determining an information collection device based on the motion path, and determining a collection feature corresponding to the information collection device based on the subject to be positioned; receiving data acquired by the information collection device in real time, querying the collection feature in the data, and determining a target subject and a motion trajectory; the motion trajectory is a motion path containing time information; timely counting all target subjects and motion trajectories, generating a subject distribution map at a current time, verifying the subject distribution map by a preset radar device, and updating the information collection device according to a verification result; the step of determining the information collection device based on the motion path and determining the collection feature corresponding to the information collection device based on the subject to be positioned comprises: acquiring a control component and a transportation carrier corresponding to the motion path; iteratively querying a positioner in the transportation carrier, establishing a communication channel with the positioner when the positioner exists, and installing a positioner of a preset model according to entity parameters of the transportation carrier when the positioner does not exist; querying a preset importance level of the transportation carrier in the transportation task, and selecting a sampling frequency according to the importance level; determining and installing an information collection device matched with the positioner based on the sampling frequency; determining the collection feature corresponding to the information collection device based on the subject to be positioned; the step of determining the collection feature corresponding to the information collection device based on the subject to be positioned comprises: the information collection device is an image collection device, and the positioner is a programmable LED lamp; querying all transportation tasks of the transportation carrier and subjects to be positioned thereof, acquiring a task amount of the subject to be positioned, determining a display gradient according to a maximum value, a minimum value and a number of the transportation tasks of the task amount; the task amount contains a field for distinguishing the subject to be positioned; determining a display color value of the positioner under different task amounts as the collection feature according to the display gradient; the step of receiving data acquired by the information collection device in real time, querying the collection feature in the data, and determining a target subject and a motion trajectory comprises: receiving data acquired by the information collection device in real time, the data being an image; performing color value recognition on the image, and positioning a collection feature region; performing feature recognition on the collection feature region to obtain a collection feature, determining a task amount according to the collection feature, querying a corresponding subject to be positioned as a target subject; extracting a position of each target subject in the image, and statistically obtaining a motion trajectory according to time information.

2. The entity tracking and localization method of claim 1, wherein, the step of querying a transportation task in a production line, and determining a subject to be positioned and a motion path thereof based on the transportation task comprises: receiving a product label input by a user, and querying a production line in a recorded production process according to the product label; acquiring a task list in the production line, and querying a transportation task containing a subject to be positioned in the task list; querying a transportation carrier and a control component thereof of the transportation task, and determining a motion path according to the control component.

3. The entity tracking and localization method of claim 1, wherein, The timing statistics all target subjects and motion trajectories, generates the subject distribution map of the current moment, verifies the subject distribution map by the preset radar device, and updates the information collection device according to the verification result, and the steps include: Timing statistics all target subjects and motion trajectories in a preset time period step, determining the current position and current speed of all to-be-positioned subjects at the current moment; According to the preset mapping relationship, all to-be-positioned subjects and their positions are mapped in the preset empty graph to obtain a subject distribution map; Obtain the actual motion parameters of each to-be-positioned subject by the preset radar device, and position the abnormal subject in the subject distribution map according to the actual motion parameters. When there is an abnormal subject, the positioning result is sent to the artificial detection end, and the information collection device is updated according to the investigation instruction fed back by the artificial detection end.

4. The entity tracking and localization method of claim 3, wherein, The step of obtaining the actual motion parameters of each to-be-positioned subject by the preset radar device includes: Randomly determine the detection wavelength in the preset wavelength range, and the detection wavelength is at least two; Send at least two detection waves according to the preset interval time, receive the echo signal in real time, and obtain the echo table corresponding to the two detection waves; Determine the distance table according to the echo table and the detection wave, and determine the actual motion parameters of each to-be-positioned subject according to each distance table; The actual motion parameters include position and speed.

5. The entity tracking and localization method of claim 1, wherein, The information collection device includes an infrared module, the image acquisition mode is a fog-penetrating mode, and the image collection device establishes a connection channel with the infrared module, and uses the infrared module as a signal emission source to acquire images.

6. The entity tracking and localization method of claim 5, wherein, The step of using the infrared module as a signal emission source to acquire images includes: Filtering the converged visible light and infrared light, filtering out the near-infrared visible light transition band in the visible light and infrared light; In each exposure process, at least once, according to the saved first exposure time, the filtered visible light and infrared light are perceived to generate a first RGBIR image; At least once, according to the saved second exposure time, the filtered visible light and infrared light are perceived to generate a second RGBIR image; Wherein, the first exposure time and the second exposure time are the same or different; For each first RGBIR image, remove the infrared IR component in each first RGBIR image, count the average brightness of the RGB image corresponding to each first RGBIR image, and remove the RGB component in each second RGBIR image. Count the average brightness of the IR image corresponding to each second RGBIR image, adjust the first exposure time according to the average brightness of each RGB image and the preset first target brightness value, adjust the second exposure time according to the average brightness of each IR image and the preset second target brightness value, notify the RGBIR sensor of the adjusted exposure time, and generate a fog-penetrating image according to each first RGBIR image and each second RGBIR image.

7. The entity tracking and localization method of claim 1, wherein, The image collection device further includes a control module, and the execution process of the control module includes: Photoelectric conversion is performed on the collected road condition image, and the photoelectric conversion data is counted to obtain a histogram; Judge the histogram distribution change, judge whether there is haze and haze concentration; wherein, in the non-haze scene, the image histogram distribution is relatively uniform, in the haze scene, the image histogram is basically concentrated in the middle area, the heavier the haze, the more concentrated the histogram; When it is judged from the histogram that there is haze, the image acquisition mode is determined as a haze-penetrating mode.

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