A method and system for enhancing the positioning accuracy of downhole targets based on the Internet of Things

Abstract

This invention discloses a method and system for enhancing the accuracy of downhole target positioning based on the Internet of Things (IoT). The method includes: a terminal collecting target positioning-related data in real time to determine the target location and calculating the target displacement; recording the target displacement within a set time period as a continuous displacement sequence; calculating the error evaluation result of the current target displacement based on the continuous displacement sequence; when the error evaluation result exceeds a set error threshold, determining the surrounding positioning devices based on a set effective radius of the positioning device; each surrounding positioning device collecting target positioning-related data and transmitting it to the terminal via the IoT; the terminal using the target positioning-related data to determine multiple target locations; dividing all target locations into multiple clusters based on a K-means clustering algorithm; calculating the clustering rationality coefficient based on the center point of each cluster; and using the center point of the cluster with the largest clustering rationality coefficient as the current target location. This invention has significant advantages such as accurate positioning and high reliability.

Description

Technical Field

[0001] This invention belongs to the field of positioning technology, specifically relating to a method and system for enhancing the positioning accuracy of downhole targets based on the Internet of Things. Background Technology

[0002] Underground mining environments, characterized by high humidity, dust, and dynamic mine structures, lead to signal attenuation, multipath interference, and GPS signal unavailability, severely impacting positioning accuracy. Existing technologies, such as inertial measurement units (IMUs), suffer from limited accuracy and are prone to cumulative errors. Wireless technologies like Wi-Fi and ZigBee have insufficient range and accuracy, failing to meet high-precision requirements. While ultra-wideband (UWB) technology offers higher accuracy, its coverage is limited, and imperfect multi-sensor fusion and initial attitude errors can cause deviations. Regarding equipment, low power consumption limits computing power, harsh environments reduce equipment durability, and insufficient base station deployment density further impacts signal coverage. Furthermore, operational issues such as improper equipment use, inadequate maintenance, and personnel skill shortages exacerbate errors. These factors combined reduce positioning accuracy from centimeter-level to meter-level, failing to meet the high-precision requirements of underground operations.

[0003] CN114299409A discloses a method for locating personnel in underground mines based on image recognition and depth information, including: acquiring images of underground equipment; identifying the equipment number of the underground equipment based on the images; obtaining the absolute position information of the underground equipment underground based on the equipment number; measuring the depth information of the underground equipment relative to the personnel underground; calculating the relative position information of the personnel underground and the underground equipment based on the depth information; and obtaining the current absolute position information of the personnel underground based on the relative position information of the personnel underground and the underground equipment and the absolute position information of the underground equipment underground. However, this patent directly uses raw displacement data (such as IMU trajectory) without filtering abnormal displacement jumps caused by sudden environmental changes (rockfalls, collisions), which significantly amplifies the cumulative error, resulting in inaccurate positioning and low reliability.

[0004] CN118730117A discloses a multi-sensor fusion-based method for underground coal mine positioning and mapping, belonging to the field of coal mine equipment technology. The method includes the following steps: S1: Preprocessing the raw data collected by the sensors; S2: Constructing IMU pre-integration residuals, using the point cloud depth information of the lidar to construct a radar odometry, and completing a preliminary estimation of the camera pose; S3: Fusing the data collected by multiple sensors, constructing a hybrid residual equation of inertial, radar, and vision, and establishing a least-squares optimization equation; S4: Eliminating accumulated errors by establishing a closed-loop detection method combining visual bag-of-words and lidar descriptors, using the closed-loop detection module as a relative pose constraint edge. However, this patent uses a fixed-weight fusion algorithm (such as Kalman filtering), which cannot adapt to changes in reliability caused by sudden changes in underground humidity or temporary equipment failures, resulting in inaccurate positioning and low reliability. Summary of the Invention

[0005] To address the shortcomings of existing downhole positioning technologies, which suffer from decreased positioning accuracy in complex environments due to signal attenuation, multipath interference, sensor cumulative errors, and insufficient base station coverage, this invention provides a method and system for enhancing the positioning accuracy of downhole targets based on the Internet of Things.

[0006] The present invention adopts the following technical solution.

[0007] This invention discloses a method for enhancing the positioning accuracy of downhole targets based on the Internet of Things, comprising:

[0008] The target is equipped with a positioning device;

[0009] The terminal collects target positioning data in real time to determine the target positioning, and calculates the target displacement based on the current target positioning and the target positioning at the previous sampling time; the target displacement within a set time period is recorded as a continuous displacement sequence; and then the error evaluation result of the current target displacement is calculated based on the continuous displacement sequence.

[0010] When the error evaluation result exceeds the set error threshold, the terminal determines the positioning devices around the target based on the set effective radius of the positioning device.

[0011] Each target's surrounding positioning device collects target positioning-related data and transmits it to the terminal via the Internet of Things;

[0012] The terminal uses relevant data from each target location to determine the location of multiple targets; it divides all target locations into multiple clusters based on the K-means clustering algorithm; it calculates the cluster rationality coefficient based on the location of the center point of each cluster; and it takes the center point of the cluster with the largest cluster rationality coefficient as the current target location.

[0013] More preferably,

[0014] The positioning device includes a radio frequency identification tag and a wireless communication device.

[0015] More preferably,

[0016] The target location-related data includes the signal strength and signal arrival angle of the RFID tag.

[0017] More preferably,

[0018] After recording the target displacement within a set time period as a continuous displacement sequence, outliers in the continuous displacement sequence are removed to obtain a reasonable displacement sequence.

[0019] More preferably,

[0020] Error evaluation result r based on continuous displacement sequence for calculating the current target displacementi As shown in the following formula:

[0021]

[0022] Among them, h i h0 is the current signal strength of the RFID tag collected by the terminal; g is the set standard signal strength. i G represents the displacement of the target between the current sampling time and the previous sampling time; j represents the j-th target displacement in the reasonable displacement sequence; n is the total number of data points in the reasonable displacement sequence; j Let be the j-th target displacement in the reasonable displacement sequence.

[0023] More preferably,

[0024] The method of determining the positioning devices around the target based on the set effective radius of the positioning device is to draw a circle with the target positioning at the previous sampling time as the center and the set effective radius of the positioning device as the radius, and to regard the positioning devices inside the circle as the positioning devices around the target.

[0025] More preferably,

[0026] Each target-surrounding positioning device collects target positioning-related data and transmits it to the terminal via the Internet of Things, including:

[0027] Radio frequency identification (RFID) tags in positioning devices around the target emit radio frequency signals;

[0028] After receiving the radio frequency signal, the target's RFID tag responds by scattering the received signal using backscattering technology; the scattered signal includes target location-related data.

[0029] The wireless communication device in the surrounding positioning device receives the scattered signal and sends the target positioning data to the terminal via the Internet of Things.

[0030] More preferably,

[0031] The cluster rationality coefficient u, calculated based on the location of the center point of each cluster, is shown in the following formula:

[0032]

[0033] Where, x i-1 Let y be the x-coordinate of the target at time i-1; i-1 z is the ordinate of the target at time i-1; i-1 x is the vertical coordinate of the target at time i-1; Qi y is the x-coordinate of the center point of the cluster at time i; Qi z is the ordinate of the center point of the cluster at time i; QiG represents the vertical coordinate of the center point of the cluster at time i; j represents the j-th target displacement in the reasonable displacement sequence; n is the total number of data points in the reasonable displacement sequence; j Let be the j-th target displacement in the reasonable displacement sequence; a is the total number of target locations within the cluster; and A is the total number of target locations determined using the relevant data of each target location.

[0034] Another aspect of the present invention discloses a downhole target positioning accuracy enhancement system based on a downhole target positioning accuracy enhancement method, comprising a positioning acquisition module, an error evaluation module, a surrounding positioning device determination module, a surrounding positioning device data acquisition module, and a target positioning determination module:

[0035] The positioning displacement calculation module collects target positioning-related data in real time to determine the target positioning, and calculates the target displacement based on the current target positioning and the target positioning at the previous sampling time; the target displacement within a set time period is recorded as a continuous displacement sequence.

[0036] The error evaluation module calculates the error evaluation result of the current target displacement based on the continuous displacement sequence;

[0037] The surrounding positioning device determination module determines the surrounding positioning devices of the target based on the set effective radius of the positioning device when the error evaluation result exceeds the set error threshold.

[0038] The surrounding positioning device data acquisition module uses each target surrounding positioning device to collect target positioning-related data and transmits it to the terminal via the Internet of Things.

[0039] The target location determination module uses relevant data for each target location to determine multiple target locations; it divides all target locations into multiple clusters based on the K-means clustering algorithm; it calculates the clustering rationality coefficient based on the location of the center point of each cluster; and it takes the center point of the cluster with the largest clustering rationality coefficient as the current target location.

[0040] Another aspect of this application discloses an electronic device, including a processor and a storage medium;

[0041] The storage medium is used to store instructions;

[0042] The processor is configured to operate according to the instructions to execute the aforementioned method for enhancing the accuracy of downhole target positioning.

[0043] This application also discloses a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for enhancing the positioning accuracy of downhole targets.

[0044] The beneficial effects of this invention are compared with those of the prior art:

[0045] This invention improves positioning accuracy and solves the problems of environmental interference and sensor error. It directly addresses the pain points of the downhole environment (high humidity, dust, dynamic structures) and equipment limitations (low power consumption, insufficient sensor accuracy) and significantly improves positioning accuracy through algorithm optimization.

[0046] This invention reduces accumulated errors by eliminating outliers in a continuous displacement sequence, specifically by dynamically eliminating abnormal displacement data caused by environmental interference such as high humidity and dust, thereby restoring the positioning accuracy from the "meter level" of existing technologies to a better level.

[0047] By combining signal strength and displacement assessments, the limitations of relying solely on a single indicator (such as signal strength) are avoided, effectively identifying the true displacement deviation and reducing the negative impact of environmental interference on the positioning results.

[0048] This invention optimizes multi-sensor data fusion and enhances system reliability by adjusting the number of clusters in the K-means clustering algorithm based on contour coefficients. It compensates for the insufficient coverage or limited accuracy of single-point sensors (such as UWB and IMU) by collaboratively collecting target location information through multiple devices, particularly improving positioning reliability in areas with low base station deployment density.

[0049] This invention uses a rationality coefficient to screen clusters with "small movement distance" and "high dispersion," prioritizes compensation data that is closer to the actual location, effectively addresses signal abrupt changes caused by dynamic mine structures, and refines the positioning results.

[0050] This invention improves positioning accuracy to the centimeter level through a method and system that combines signal strength analysis, displacement error evaluation results, and compensation based on surrounding data. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the method flow of the present invention;

[0052] Figure 2 This is a schematic diagram of the signal transmission system architecture of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, other embodiments obtained by those skilled in the art without creative effort are all within the protection scope of this invention.

[0054] This application discloses a method for enhancing the positioning accuracy of downhole targets based on the Internet of Things (IoT). See attached document. Figure 1 ,include:

[0055] The positioning target is equipped with a positioning device; the positioning target can be a mobile target such as a staff member or a vehicle; there can be multiple positioning targets at the same time; the positioning device includes a radio frequency identification tag and a wireless communication device.

[0056] The terminal collects target positioning-related data in real time to determine the target location, and calculates the target displacement based on the current target location and the target location at the previous sampling time. The target positioning-related data includes the signal strength and angle of arrival of the RFID tag. The real-time collection of target positioning-related data to determine the target location involves determining the distance from the location where the RFID tag receives the signal strength to the target location, and then determining the target location based on the location where the signal strength is received, the distance from the location where the signal strength is received to the target location, and the angle of arrival.

[0057] The terminal records the target displacement within a set time period as a continuous displacement sequence; the set time period is preferably the period between the moment when the target begins to carry the positioning device and the current moment;

[0058] After recording the target displacement within a set time period as a continuous displacement sequence, outliers in the continuous displacement sequence are removed to obtain a reasonable displacement sequence. Then, based on the continuous displacement sequence (reasonable displacement sequence) after removing outliers, the error evaluation result r of the current target displacement is calculated. i As shown in the following formula:

[0059]

[0060] Among them, h i h0 is the current signal strength of the RFID tag collected by the terminal; h0 is the set standard signal strength, preferably -30dBm; g i This represents the displacement of the target between the current sampling time and the previous sampling time; when i = 1, g i Let g1 represent the displacement between the position at the first sampling time and the initial position of the target; j represent the j-th target displacement in the reasonable displacement sequence, where j is an integer and j∈[1,n]; n is the total number of data points in the reasonable displacement sequence; G j Let be the j-th target displacement in the reasonable displacement sequence.

[0061] When the error evaluation result of a certain positioning target exceeds the set error threshold, the terminal determines the positioning devices around the target based on the set effective radius of the positioning device; preferably, the preferred value range of the error threshold is 0.6-0.7.

[0062] The method of determining the positioning devices around the target based on the set effective radius of the positioning devices involves drawing a circle with the target's location at the previous sampling time as the center and the set effective radius of the positioning devices as the radius, and defining the positioning devices within the circle as the positioning devices around the target. The positioning devices around the target can be positioning devices equipped with other positioning targets whose error evaluation results do not exceed a set threshold.

[0063] Each target's surrounding positioning device collects target positioning-related data and transmits it to the terminal via the Internet of Things (IoT). Specifically, this includes:

[0064] Radio frequency identification (RFID) tags in positioning devices around the target emit radio frequency signals;

[0065] After receiving the radio frequency signal, the target's RFID tag responds by scattering the received signal using backscattering technology; the scattered signal includes target location-related data.

[0066] The wireless communication device in the surrounding positioning device receives the scattered signal and sends the target positioning data to the terminal via the Internet of Things.

[0067] The terminal uses relevant data from each target location to determine the locations of multiple targets; it then divides all target locations into multiple clusters based on the K-means clustering algorithm; this invention determines the number of clusters based on the silhouette coefficient adjustment of the K-means clustering algorithm, specifically including:

[0068] The initial number of clusters is set to a preset first cluster number, generating the first cluster number of clusters; preferably, the first cluster number is 2;

[0069] If the current contour coefficient is less than the set contour coefficient threshold, the first cluster number is increased by the set second cluster number to become the third cluster number; preferably, the second cluster number is 1.

[0070] Clustering is performed again using the third cluster number. If the current silhouette coefficient is less than the set silhouette coefficient threshold, the current third cluster number is increased by the set second cluster number as the new third cluster number, and clustering is performed again using the third cluster number until the current silhouette coefficient is greater than the set silhouette coefficient threshold.

[0071] Then, based on the location of the center point of each cluster, its cluster rationality coefficient u is calculated, as shown in the following formula:

[0072]

[0073] Where, x i-1 Let y be the x-coordinate of the target at time i-1; i-1 z is the ordinate of the target at time i-1; i-1 x is the vertical coordinate of the target at time i-1; Qiy is the x-coordinate of the center point of the cluster at time i; Qi z is the ordinate of the center point of the cluster at time i; Qi Let G be the vertical coordinate of the center point of the cluster at time i; j represents the j-th target displacement in the reasonable displacement sequence, j is an integer and j∈[1,n]; n is the total number of data points in the reasonable displacement sequence; G j Let be the j-th target displacement in the reasonable displacement sequence; a is the total number of target locations within the cluster; and A is the total number of target locations determined using the relevant data of each target location.

[0074] The center point of the cluster with the highest cluster rationality coefficient is used as the current target location.

[0075] Example 1

[0076] A method for improving the positioning accuracy of downhole targets based on the Internet of Things.

[0077] See appendix Figure 1 The method of this invention solves the problem of decreased positioning accuracy in downhole environments by analyzing signal strength and displacement, combined with surrounding data compensation and k-means clustering algorithm. Specifically, it includes:

[0078] Step 1: The terminal collects target positioning-related data in real time to determine the target location, calculates the target displacement in real time, and removes outliers;

[0079] The underground worker is equipped with a positioning device as the positioning target. This device can employ radio frequency identification (RFID) tags, ultra-wideband (UWB) tags, and inertial measurement unit (IMU) sensors to acquire the wearer's location information. Those skilled in the art can choose the appropriate device based on the specific circumstances, and further details are omitted here. In a preferred embodiment of the invention, the positioning device includes an RFID tag and a wireless communication device.

[0080] The terminal device receives target location-related data and connects to a central computer or server for processing. The target location-related data includes the RFID tag ID, signal strength, signal arrival time, and signal arrival angle.

[0081] The tag ID is used to identify the tag that emitted the signal.

[0082] Collecting target location-related data to determine the target location can be based on signal strength and signal angle of arrival; it can also be based on the signal angle of arrival (AOA) from two or more sets of target location-related data; it can also be based on the signal strength (RSSI) or signal time difference of arrival (TDOA) from three sets of target location-related data, using triangulation to determine the target location; or it can be based on the signal time difference of arrival (TDOA) and signal time of arrival (TDOA) from four sets of target location-related data to construct a spatial coordinate system and obtain the three-dimensional spatial location of the target. Those skilled in the art should know how to determine the target location using the above methods, and should be able to select an appropriate method based on each target location-related data to determine the target location according to the actual situation, which will not be elaborated here.

[0083] In a preferred embodiment of the present invention, the target location is determined by using the signal strength and signal arrival angle of the RFID tag. Specifically, the distance from the location where the signal strength is received to the location of the tag can be calculated based on the signal strength of the RFID tag. Those skilled in the art should know how to calculate the distance from the location where the signal strength is received to the location of the tag, which will not be elaborated here. The tag location can be known by knowing the location where the signal strength is received, the distance from the location where the signal strength is received to the location of the tag, and the signal arrival angle.

[0084] The computing infrastructure of this invention includes software tools for data processing, anomaly detection, and clustering algorithms (such as scikit-learn and ADTK).

[0085] At the current sampling time i, calculate the displacement g of the target worker. i That is, the target displacement is calculated based on the current target location and the target location at the previous sampling time. Specifically, it is calculated as follows: The Euclidean distance between the current target location and the target location at the previous sampling time is shown in the following formula:

[0086] Among them, g i Let x be the displacement of the target construction worker at time i. i ,y i ,z i Let be the three-dimensional position information of the target construction personnel at time i.

[0087] (x i-1 ,y i-1 ,z i-1 ) represents the three-dimensional location information of the target construction personnel at time i-1.

[0088] The terminal records the target displacement within a set time period as a continuous displacement sequence; the set time period is preferably the period between the moment when the target begins to carry the positioning device and the current moment;

[0089] For the displacement sequence {g} at consecutive time points i After removing outliers, a reasonable displacement sequence {G} is obtained. i The removal of outliers can be implemented using anomaly detection methods such as the Python machine learning library scikit-learn, the anomaly detection toolkit ADTK, and the IsolationForest algorithm. Those skilled in the art can choose the appropriate method based on the specific circumstances.

[0090] Step 2: Calculate the error evaluation result based on the signal strength and the continuous displacement sequence (reasonable displacement sequence) after removing outliers, and determine whether the error evaluation result exceeds the set threshold;

[0091] The error evaluation result r of calculating the current target displacement i As shown in the following formula:

[0092]

[0093] Among them, h i h0 is the current signal strength of the RFID tag collected by the terminal; h0 is the set standard signal strength, preferably -30dBm; g i This represents the displacement of the target between the current sampling time and the previous sampling time; when i = 1, g i Let g1 represent the displacement between the position at the first sampling time and the initial position of the target; j represent the j-th target displacement in the reasonable displacement sequence, where j is an integer and j∈[1,n]; n is the total number of data points in the reasonable displacement sequence; G j Let be the j-th target displacement in the reasonable displacement sequence.

[0094] If the error evaluation result of a certain target construction worker is r i If the error exceeds a preset error threshold, it indicates a large positioning deviation, and proceed to the next step; otherwise, return to step 1. Preferably, the preferred value range for the error threshold is 0.6-0.7.

[0095] Step 3: If the error evaluation result of a certain target construction worker exceeds the set threshold, the surrounding positioning devices are determined based on the set effective radius of the positioning device, that is, the range of surrounding workers is defined, and the positioning data of the surrounding positioning devices is used for compensation. The target construction worker is accurately located through clustering.

[0096] 3.1 such as Figure 2As shown, the range of surrounding personnel is determined by using the location at the sampling time before the error evaluation result exceeds the set threshold as the center, and the set effective radius of the positioning device as the radius. Those skilled in the art should know that the effective radius of the positioning device refers to the maximum effective distance at which the positioning device can stably perform its positioning function underground. Those skilled in the art can determine the effective radius of the equipment based on the actual situation.

[0097] Signal transmission between the terminal and the positioning device around the target can be achieved based on Internet of Things (IoT) technologies (such as Wi-Fi, ZigBee, UWB), or LoRa or other low-power technologies can be selected depending on the environment. Those skilled in the art can make their own choices.

[0098] Those skilled in the art can adjust the number and type of equipment for surrounding personnel according to the downhole environment, which will not be elaborated here.

[0099] Specifically, the terminal sends a signal to the positioning devices around the target (the positioning devices of the surrounding staff) to collect target positioning-related data. This signal is received by the wireless communication device in the positioning device of the staff around the target. The radio frequency identification tag in the positioning device of the staff around the target emits a radio frequency signal. After receiving the radio frequency signal, the radio frequency identification tag equipped on the target responds by scattering the received signal through backscattering technology. The scattered signal includes target positioning-related data.

[0100] The positioning devices of surrounding staff collect target positioning-related data via wireless communication and transmit this data to a terminal via the Internet of Things (IoT). The terminal uses the target positioning-related data to determine the locations of multiple targets, forming a "compensated location information group." The positioning devices of the surrounding staff are those equipped by other staff members; these devices act as relay points, providing auxiliary positioning data for the targets.

[0101] Apply the k-means clustering algorithm to the compensated location information group:

[0102] Initially set k=2, generating two clusters.

[0103] Calculate the silhouette coefficient. If it is less than the set silhouette coefficient threshold, increase k by 1 and re-cluster until the condition is met. The preferred value range of the silhouette coefficient threshold is 0.65-1.

[0104] Then, based on the location of the center point of each cluster, its cluster rationality coefficient u is calculated, as shown in the following formula:

[0105]

[0106] Where, x i-1 Let y be the x-coordinate of the target at time i-1;i-1 z is the ordinate of the target at time i-1; i-1 x is the vertical coordinate of the target at time i-1; Qi y is the x-coordinate of the center point of the cluster at time i; Qi z is the ordinate of the center point of the cluster at time i; Qi Let G be the vertical coordinate of the center point of the cluster at time i; j represents the j-th target displacement in the reasonable displacement sequence, j is an integer and j∈[1,n]; n is the total number of data points in the reasonable displacement sequence; G j Let be the j-th target displacement in the reasonable displacement sequence; a is the total number of target locations within the cluster; and A is the total number of target locations determined using the relevant data of each target location.

[0107] Choose the cluster with the largest u, and use its center point as the precise location of the target.

[0108] This application also discloses a downhole target positioning accuracy enhancement system based on a downhole target positioning accuracy enhancement method, including a positioning acquisition module, an error evaluation module, a surrounding positioning device determination module, a surrounding positioning device data acquisition module, and a target positioning determination module:

[0109] The positioning displacement calculation module collects target positioning-related data in real time to determine the target positioning, and calculates the target displacement based on the current target positioning and the target positioning at the previous sampling time; the target displacement within a set time period is recorded as a continuous displacement sequence.

[0110] The error evaluation module calculates the error evaluation result of the current target displacement based on the continuous displacement sequence;

[0111] The surrounding positioning device determination module determines the surrounding positioning devices of the target based on the set effective radius of the positioning device when the error evaluation result exceeds the set error threshold.

[0112] The surrounding positioning device data acquisition module uses each target surrounding positioning device to collect target positioning-related data and transmits it to the terminal via the Internet of Things.

[0113] The target location determination module uses relevant data for each target location to determine multiple target locations; it divides all target locations into multiple clusters based on the K-means clustering algorithm; it calculates the clustering rationality coefficient based on the location of the center point of each cluster; and it takes the center point of the cluster with the largest clustering rationality coefficient as the current target location.

[0114] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0115] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0116] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0117] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0118] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for enhancing the positioning accuracy of downhole targets based on the Internet of Things, characterized in that, include: The target is equipped with a positioning device; The terminal collects target positioning data in real time to determine the target location, and calculates the target displacement based on the current target location and the target location at the previous sampling time; the target displacement within a set time period is recorded as a continuous displacement sequence; and the error evaluation result of the current target displacement is calculated. : ;in, The signal strength of the current RFID tag collected by the terminal; The set standard signal strength; is the displacement of the target between the current sampling time and the previous sampling time; j represents the j-th target displacement in the reasonable displacement sequence; n is the total number of data points in the reasonable displacement sequence; This represents the j-th target displacement in the reasonable displacement sequence. When the error evaluation result exceeds the set error threshold, the terminal determines the positioning devices around the target based on the set effective radius of the positioning device. Each target's surrounding positioning device collects target positioning-related data and transmits it to the terminal via the Internet of Things; The terminal uses relevant data from each target location to determine the locations of multiple targets; it then divides all target locations into multiple clusters based on the K-means clustering algorithm; finally, it calculates the clustering rationality coefficient u for each cluster. ;in, Let x be the x-coordinate of the target at time i-1; Let be the ordinate of the target at time i-1; Let be the vertical coordinate of the target at time i-1; Let x be the x-coordinate of the center point of the cluster at time i; The ordinate of the center point of the cluster at time i; is the vertical coordinate of the center point of the cluster at time i; j represents the j-th target displacement in the reasonable displacement sequence; n is the total number of data points in the reasonable displacement sequence; This represents the j-th target displacement in the reasonable displacement sequence. Let A be the total number of target locations within each cluster, and let A be the total number of target locations determined using relevant data for each target location. The center point of the cluster with the highest clustering rationality coefficient is taken as the current target location.

2. The method for enhancing the positioning accuracy of downhole targets based on the Internet of Things according to claim 1, characterized in that: The positioning device includes a radio frequency identification tag and a wireless communication device.

3. The method for enhancing the positioning accuracy of downhole targets based on the Internet of Things according to claim 1, characterized in that: The target location-related data includes the signal strength and signal arrival angle of the RFID tag.

4. The method for enhancing the positioning accuracy of downhole targets based on the Internet of Things according to claim 1, characterized in that: After recording the target displacement within a set time period as a continuous displacement sequence, outliers in the continuous displacement sequence are removed to obtain a reasonable displacement sequence.

5. The method for enhancing the positioning accuracy of downhole targets based on the Internet of Things according to claim 1, characterized in that: The method of determining the positioning devices around the target based on the set effective radius of the positioning device is to draw a circle with the target positioning at the previous sampling time as the center and the set effective radius of the positioning device as the radius, and to regard the positioning devices inside the circle as the positioning devices around the target.

6. The method for enhancing the positioning accuracy of downhole targets based on the Internet of Things according to claim 1, 2, or 3, characterized in that: Each target-surrounding positioning device collects target positioning-related data and transmits it to the terminal via the Internet of Things, including: Radio frequency identification (RFID) tags in positioning devices around the target emit radio frequency signals; After receiving the radio frequency signal, the target's RFID tag responds by scattering the received signal using backscattering technology; the scattered signal includes target location-related data. The wireless communication device in the surrounding positioning device receives the scattered signal and sends the target positioning data to the terminal via the Internet of Things.

7. A system for enhancing the accuracy of downhole target positioning based on the Internet of Things (IoT) according to any one of claims 1-6, characterized in that... It includes a positioning acquisition module, an error evaluation module, a surrounding positioning device determination module, a surrounding positioning device data acquisition module, and a target positioning determination module: The positioning displacement calculation module collects target positioning-related data in real time to determine the target positioning, and calculates the target displacement based on the current target positioning and the target positioning at the previous sampling time; the target displacement within a set time period is recorded as a continuous displacement sequence. The error evaluation module calculates the error evaluation result of the current target displacement based on the continuous displacement sequence; The surrounding positioning device determination module determines the surrounding positioning devices of the target based on the set effective radius of the positioning device when the error evaluation result exceeds the set error threshold. The surrounding positioning device data acquisition module uses each target surrounding positioning device to collect target positioning-related data and transmits it to the terminal via the Internet of Things. The target location determination module uses relevant data for each target location to determine multiple target locations; it divides all target locations into multiple clusters based on the K-means clustering algorithm; and it calculates the clustering rationality coefficient based on the location of the center point of each cluster. The center point of the cluster with the highest cluster rationality coefficient is used as the current target location.

8. A terminal, comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the IoT-based method for enhancing the positioning accuracy of downhole targets according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the IoT-based method for enhancing the positioning accuracy of downhole targets as described in any one of claims 1-6.

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