An internet of things device point position distance calculation method and system

Abstract

The application provides a kind of Internet of Things equipment point distance calculation method and system, wherein, method includes: step 1: obtaining the calculation requirement information of target personnel for Internet of Things equipment point distance calculation;Step 2: training ranging scheme determination model, inputting the calculation requirement information into the ranging scheme determination model, obtaining the first ranging scheme;Step 3: based on the first ranging scheme, calculating the first point distance of target personnel to Internet of Things equipment.The Internet of Things equipment point distance calculation method and system of the application introduce calculation requirement information and ranging scheme determination model, determine the first ranging scheme according to the adaptability of calculation requirement information, avoid using the same ranging scheme for point distance calculation to meet all ranging requirements of users and all use high-precision ranging scheme for point distance calculation to cause system calculation delay, high energy consumption situation, improve the suitability of the first ranging scheme determination, and also more intelligent.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of computer algorithms, in particular to a method and system for calculating the distance between a point and an Internet of Things device. BACKGROUND

[0002] Nowadays, Internet of Things devices are widely used in the industrial Internet industry. Generally, when a staff member managing an Internet of Things device (for example, an industrial robot) wants to know the distance between a point and the Internet of Things device for management, the staff member usually calculates the distance between a point and the Internet of Things device according to a manually pre-set distance measurement scheme. For example, the invention patent CN108445500A Distance calculation method and system of TOF sensor uses a sensor to emit modulated near-infrared light, which is reflected after encountering an object, and the sensor calculates the distance of the photographed object by calculating the time difference or phase difference between light emission and reflection. The measurement range of the infrared distance measurement technology is generally 1-5 kilometers, which is not suitable for measuring the distance of an Internet of Things device at a longer distance. For example, the invention patent CN114443655A System and method for calculating the distance between a point and an Internet of Things device discloses an Internet of Things device point distance calculation method based on a geohash algorithm. When the user positioning calculation accuracy requirement is not high, the system has a large computational burden and high energy consumption. In addition, the above-mentioned prior art needs to manually determine the distance measurement scheme, which is not intelligent.

[0003] Therefore, there is an urgent need for a solution. SUMMARY

[0004] One of the objectives of the present application is to provide a method for calculating the distance between a point and an Internet of Things device, which introduces a calculation requirement information and a distance measurement scheme determination model, determines a first distance measurement scheme according to the adaptability of the calculation requirement information, avoids the situation that using the same distance measurement scheme for point distance calculation cannot meet all user requirements and using high-precision distance measurement scheme for point distance calculation causes system calculation delay and high energy consumption, improves the suitability of the first distance measurement scheme determination, and is more intelligent.

[0005] The method for calculating the distance between a point and an Internet of Things device provided by the present application comprises the following steps:

[0006] Step 1: Obtain the calculation requirement information of a target staff member for calculating the distance between a point and an Internet of Things device;

[0007] Step 2: Train a distance measurement scheme determination model, input the calculation requirement information into the distance measurement scheme determination model, and obtain a first distance measurement scheme;

[0008] Step 3: Calculate the first point distance between the target staff member and the Internet of Things device based on the first distance measurement scheme.

[0009] Preferably, the computing requirement information of the Internet of Things device requiring point-to-point distance calculation of the target personnel includes:

[0010] Obtaining the computing purpose information of the target personnel;

[0011] Determining the computing requirement information based on the computing purpose information;

[0012] And / or,

[0013] Obtaining the computing requirement information of the target personnel inputting the ranging calculation precision requirement input interface.

[0014] Preferably, the training ranging scheme determination model comprises:

[0015] Obtaining a pre-selected determination process record of artificial ranging scheme determination from a pre-set big data platform;

[0016] Reasonably analyzing the ranging scheme determination process corresponding to the pre-selected determination process record to obtain a rationality degree;

[0017] If the rationality degree is greater than or equal to a pre-set rationality threshold, the pre-selected determination process record corresponding to the pre-selected determination process record is taken as a target determination process record;

[0018] Inputting the target determination process record into a pre-set neural network model for model training to obtain the ranging scheme determination model trained to a convergence state.

[0019] Preferably, the reasonably analyzing the ranging scheme determination process corresponding to the pre-selected determination process record to obtain a rationality degree comprises:

[0020] Process splitting the ranging scheme determination process to obtain a plurality of ranging scheme determination sub-processes;

[0021] Determining the rationality value of the ranging scheme determination sub-process based on a pre-set rationality value determination template;

[0022] Extracting a first characteristic value of the ranging scheme determination sub-process based on a pre-set first feature extraction template;

[0023] Constructing a first process description factor based on the first characteristic value;

[0024] Obtaining a pre-set process weight library, the process weight library comprising: a plurality of one-to-one second process description factors and process weight values;

[0025] Matching the first process description factor with each of the second process description factors to obtain a matching value, and taking the process weight value corresponding to the second process description factor with the largest matching value as a target process weight value;

[0026] multiplying the target process weight value with the corresponding reasonable value to obtain a target summation value and associating the target summation value with the ranging scheme determination process;

[0027] accumulatively calculating the target summation value associated with each ranging scheme determination process to obtain the reasonability.

[0028] Preferably, the Internet of Things device point position distance calculation method further comprises:

[0029] determining whether the Internet of Things device performing the Internet of Things device point position distance calculation needs to be overhauled, and if so, determining a worker closest to the Internet of Things device to overhaul the Internet of Things device;

[0030] Preferably, determining whether the Internet of Things device performing the Internet of Things device point position distance calculation needs to be overhauled comprises:

[0031] obtaining a device state of the Internet of Things device, the device state comprising: device online and device offline;

[0032] if the device state is device offline, attempting to remotely wake up the Internet of Things device;

[0033] obtaining the device state of the Internet of Things device after remote wake-up;

[0034] if the device state of the Internet of Things device after remote wake-up is device offline, the Internet of Things device after remote wake-up needs to be overhauled.

[0035] Preferably, the determining of the worker closest to the Internet of Things device to overhaul the Internet of Things device comprises:

[0036] obtaining a second ranging scheme when the worker overhauls and a first device identifier of the Internet of Things device;

[0037] sequentially traversing the Internet of Things devices corresponding to the first device identifier, and each time the traversal is performed, obtaining a first device identifier of the Internet of Things device currently being traversed and taking the first device identifier as a second device identifier;

[0038] determining, based on the second ranging scheme, a second point position distance of the worker from the Internet of Things device corresponding to the second device identifier;

[0039] determining the worker corresponding to the shortest second point position distance in the second point position distance as a target overhaul worker;

[0040] guiding the target overhaul worker to the Internet of Things device corresponding to the second device identifier for overhaul;

[0041] When the traversal of all the first device identifiers corresponding to the Internet of Things devices is completed, the determination is completed.

[0042] Preferably, the guiding the target maintenance personnel to the corresponding Internet of Things device corresponding to the second device identifier for maintenance comprises:

[0043] Obtaining three-dimensional space data of a device placement space where the Internet of Things device corresponding to the second device identifier is located;

[0044] According to the three-dimensional space data, a space model corresponding to the device placement space is constructed;

[0045] The space model is spatially divided to obtain a subspace model;

[0046] Determining a target subspace model in the subspace model that can accommodate the Internet of Things device corresponding to the second device identifier;

[0047] Based on a preset labeling rule, the target subspace model is labeled in the space model, and image information of the labeled space model is sent to the target maintenance personnel.

[0048] Preferably, the determining a target subspace model in the subspace model that can accommodate the Internet of Things device corresponding to the second device identifier comprises:

[0049] Obtaining first shape description information of the subspace model;

[0050] Based on a preset second feature extraction template, a second feature value of the first shape description information is extracted;

[0051] Based on the second feature value, a first shape description vector is constructed;

[0052] Obtaining second shape description information of the Internet of Things device corresponding to the second device identifier;

[0053] Based on the second feature extraction template, a third feature value of the second shape description information is extracted;

[0054] Based on the third feature value, a second shape description vector is constructed;

[0055] Matching the first shape description vector and the second shape description vector, determining a subspace model corresponding to the first shape description vector that matches, and taking it as the target subspace model.

[0056] The embodiment of the application provides a kind of Internet of Things device point distance calculation system, comprising:

[0057] The acquisition module is configured to acquire calculation requirement information of the target personnel for the point-to-distance calculation of the Internet of Things device.

[0058] The training module is configured to train a ranging scheme determination model, input the calculation requirement information into the ranging scheme determination model, and obtain a first ranging scheme.

[0059] The calculation module is configured to calculate a first point-to-distance between the target personnel and the Internet of Things device based on the first ranging scheme.

[0060] Preferably, the acquisition module acquires the calculation requirement information of the target personnel for the point-to-distance calculation of the Internet of Things device, including:

[0061] acquiring calculation purpose information of the target personnel;

[0062] determining the calculation requirement information based on the calculation purpose information;

[0063] and / or,

[0064] acquiring the calculation requirement information input by the target personnel into a ranging calculation precision requirement input interface.

[0065] Other features and advantages of the present application will be described in the following description and become apparent from the description, or can be learned from the practice of the present application. The objectives and other advantages of the present application can be achieved and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0066] The technical solutions of the present application will be further described in detail below with the help of the drawings and examples. BRIEF DESCRIPTION OF DRAWINGS

[0067] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, and are used to explain the present application together with the embodiments of the present application, and do not constitute a limitation on the present application. In the drawings:

[0068] Figure 1 is a schematic diagram of a point-to-distance calculation method of an Internet of Things device in an embodiment of the present application;

[0069] Figure 2 is a schematic diagram of a point-to-distance calculation system of an Internet of Things device in an embodiment of the present application. DETAILED DESCRIPTION

[0070] The preferred embodiments of the present application will be described below in conjunction with the accompanying drawings, and it should be understood that the preferred embodiments described herein are only used to explain and illustrate the present application, and do not constitute a limitation on the present application.

[0071] The embodiment of the application provides a kind of Internet of Things equipment point distance calculation method, as shown in figure Figure 1 Including:

[0072] Step 1: obtaining the first calculation accuracy requirement information of the Internet of Things equipment that the target personnel needs to carry out point distance calculation;

[0073] Step 2: training distance measurement scheme determination model, input the first calculation accuracy requirement information into the distance measurement scheme determination model, and obtain the first distance measurement scheme;

[0074] Step 3: based on the first distance measurement scheme, the first point distance between the target personnel and the Internet of Things equipment is calculated.

[0075] The working principle and beneficial effects of the above technical solution are:

[0076] The target personnel is the personnel who needs to carry out the point distance of Internet of Things equipment, and the Internet of Things equipment is, for example: agricultural monitoring robot, industrial automation robot, etc., and the calculation requirement information is, for example: which Internet of Things equipment in which distance range needs to carry out point distance calculation in which accuracy range, for example: the accuracy error range of Internet of Things equipment A within 3km distance range is within 50 meters, and the point distance calculation. Generally speaking, the higher the accuracy requirement of equipment positioning, the greater the system calculation amount and the longer the calculation time, therefore, according to the adaptability of the calculation accuracy requirement information to determine the distance measurement scheme, it can not only meet the user's demand, but also make the system's power consumption more reasonable, therefore, the distance measurement scheme determination model is trained, and the distance measurement scheme determination model is a neural network model obtained by inputting the determination process of manual distance measurement scheme determination into the neural network model for model training, which can replace manual distance measurement scheme determination. When determining, input the calculation requirement information, obtain the first distance measurement scheme output by the distance measurement scheme determination model, for example: infrared distance measurement technology, laser distance measurement and ultrasonic distance measurement. Based on the first distance measurement scheme, the first point distance between the target personnel and the Internet of Things equipment is calculated.

[0077] The Internet of Things equipment point distance calculation method and system of the application introduce the calculation requirement information and the distance measurement scheme determination model, determine the first distance measurement scheme according to the adaptability of the calculation requirement information, avoid the situation that using the same distance measurement scheme for point distance calculation is difficult to meet all user's demand and all high-precision distance measurement scheme for point distance calculation causes system's calculation delay and high energy consumption, improve the suitability of the first distance measurement scheme determination, and also more intelligent.

[0078] In one embodiment, the calculation requirement information of the Internet of Things equipment that the target personnel needs to carry out point distance calculation includes:

[0079] Obtain the calculation purpose information of the target personnel.

[0080] determine the computing requirement information based on the computing usage information;

[0081] and / or,

[0082] obtain the computing requirement information of the target personnel input ranging calculation precision requirement input interface.

[0083] The working principle and beneficial effects of the above technical solutions are:

[0084] There are two methods for obtaining computing requirement information. The first method is to obtain the computing usage information of the target personnel point distance calculation, for example: used to determine the position distribution relationship of the Internet of Things device, and for example: used to find the Internet of Things device for maintenance. Based on the computing usage information of the target personnel, the computing requirement information is determined, for example: when the distance relationship needs to be determined, the precision requirement is low, and when the personnel needs to be arranged to go to the specific position of the Internet of Things device for maintenance, the precision requirement is high; The second method is to obtain the computing precision information of the target personnel input ranging calculation precision requirement input interface.

[0085] The present application introduces two ways to obtain computing requirement information, which improves the comprehensiveness of computing requirement information acquisition.

[0086] In one embodiment, the training ranging scheme determination model comprises:

[0087] Obtain a plurality of artificial ranging scheme determination pre-selection determination process records from a pre-set big data platform;

[0088] Reasonably analyze the ranging scheme determination process corresponding to the pre-selection determination process record to obtain a rationality degree;

[0089] If the rationality degree is greater than or equal to a pre-set rationality degree threshold, the pre-selection determination process record corresponding to the pre-selection determination process record is used as a target determination process record;

[0090] Input the target determination process record into a pre-set neural network model for model training to obtain the ranging scheme determination model trained to a convergence state.

[0091] The working principle and beneficial effects of the above technical solutions are:

[0092] The preset big data platform is, for example, an Internet of Things device management experience exchange platform. A pre-selected determination process record obtained from the big data platform for manual ranging scheme determination is determined, for example, a manual calculation method for point distance calculation based on calculation requirements. The pre-selected determination process record obtained from the big data platform is not necessarily reasonable, so it needs to be screened. The rationality of the ranging scheme determination process corresponding to the pre-selected determination process record is analyzed, and the rationality (the greater the rationality, the more reasonable the corresponding ranging scheme determination process, and the more it can be used as a training sample) is obtained. If the rationality is greater than or equal to the preset rationality threshold (the rationality threshold is set by a human being in advance), the corresponding pre-selected determination process record is used as the target determination process record. The target determination process record is input into the preset neural network model (for example, a BP neural network model) for model training. When the above neural network model is trained to a convergence state, a ranging scheme determination model is obtained.

[0093] The present application introduces a rationality threshold to determine the target determination process record corresponding to the ranging scheme determination process with high rationality of the ranging scheme determination process as a training sample for model training, thereby improving the quality of model training and further improving the accuracy of subsequent ranging scheme determination of the ranging scheme determination model.

[0094] In one embodiment, the rationality of the ranging scheme determination process corresponding to the pre-selected determination process record is analyzed, and the rationality is obtained, including:

[0095] The ranging scheme determination process is split into multiple ranging scheme determination sub-processes;

[0096] Based on the preset rational value determination template, the rational value of the ranging scheme determination sub-process is determined;

[0097] Based on the preset first feature extraction template, the first feature value of the ranging scheme determination sub-process is extracted;

[0098] Based on the first feature value, a first process description factor is constructed;

[0099] A preset process weight library is obtained, and the process weight library includes: a plurality of one-to-one second process description factors and process weight values;

[0100] The first process description factor is matched with each second process description factor to obtain a matching value. The process weight value corresponding to the second process description factor with the maximum matching value is obtained as the target process weight value;

[0101] The target process weight value is multiplied by the corresponding rational value to obtain a target summation value, and the target summation value is associated with the corresponding ranging scheme determination process;

[0102] The sum value of the target associated with each ranging scheme determination process is accumulated to obtain the rationality.

[0103] The working principle and beneficial effects of the technical solution are as follows:

[0104] The ranging scheme determination sub-process is, for example, determining the ranging range of the ranging method in the prior art. The pre-set rational value determination template is a template for determining the rational value of the ranging scheme determination process, which is set manually in advance. The pre-set first feature extraction template is a template for extracting the process feature of the ranging scheme determination sub-process, which is set manually in advance. Based on the first feature extraction template, the first feature value of the ranging scheme determination sub-process is extracted, for example, the process logic of the ranging scheme determination sub-process. Based on the first feature value, the first process description factor is constructed; the first process description factor is a description vector for describing the process feature of the ranging scheme determination sub-process. The first process description factor is matched with each second process description factor in the process weight library to obtain a matching value (the greater the matching value, the more the corresponding second process description factor can be used to describe the process feature of the ranging scheme determination sub-process). The calculation formula of the matching value of the first process description factor and the second process description factor is as follows: In the formula, M represents the matching value; M 1,i is the i-th data value of the first process description factor of the ranging scheme determination sub-process; M 2,i is the i-th data value of the second process description factor in the process weight library, and N is the total number of data. The process weight value of the corresponding second process description factor with the largest matching value is determined as the target process weight value (the target process weight value represents the importance of the ranging scheme determination sub-process in the ranging scheme determination process, and the greater the target process weight value, the more important the corresponding ranging scheme determination sub-process in the ranging scheme determination process). The rational value of the ranging scheme determination sub-process is multiplied by the corresponding target process weight to determine the target sum value associated with the corresponding ranging scheme determination process. The sum of all target sum values associated with the ranging scheme determination process is accumulated to obtain the rationality.

[0105] The first process description factor and the process weight library are introduced in the application to determine the process weight value of the second process description factor with the largest matching value of the second process description factor in the process weight library, thereby improving the rationality of the process weight value determination. The target sum value is obtained by multiplying the rational value of each ranging scheme determination sub-process by the corresponding process weight value, and the rationality is determined based on the target sum value, thereby further improving the accuracy of the rationality acquisition.

[0106] In one embodiment, the Internet of Things device point distance calculation method further comprises:

[0107] determining whether the Internet of Things device performing the point-to-distance calculation needs to be repaired, and if so, determining the closest staff member to the Internet of Things device to perform the repair;

[0108] The determination of whether the Internet of Things device performing the point-to-distance calculation needs to be repaired includes:

[0109] The device state of the Internet of Things device is obtained, and the device state includes device online and device offline.

[0110] If the device state is device offline, the corresponding Internet of Things device is remotely woken up.

[0111] The device state of the corresponding Internet of Things device after remote wake-up is obtained.

[0112] If the device state of the corresponding Internet of Things device after remote wake-up is device offline, the corresponding Internet of Things device after remote wake-up needs to be repaired.

[0113] The working principle and beneficial effects of the above technical solution are:

[0114] Generally, the Internet of Things device needs to query the device running state regularly to ensure the normal operation of the Internet of Things device. When the device is offline, the control center can generally send a reset instruction directly. However, the reasons for the device being offline are complex, and there are situations where the device cannot be restored to a normal operating state by a simple reset instruction. Therefore, it is urgent to solve this problem.

[0115] Determining whether the Internet of Things device performing the point-to-distance calculation needs to be repaired (for example, when the Internet of Things device is "offline" and cannot be remotely woken up, the corresponding Internet of Things device needs to be repaired), if the repair is needed, the distance between each staff member and the corresponding Internet of Things device is calculated, and the closest staff member is determined to go to the repair.

[0116] The determination process of whether the Internet of Things device needs to be repaired is as follows:

[0117] The device state is obtained, for example, device online and device offline. The device detection signal can be sent to each Internet of Things device. If the detection signal reply signal of the Internet of Things device can be received, the corresponding Internet of Things device is online, otherwise, the device is offline. The offline Internet of Things device is remotely woken up. When remotely woken up, a reset signal can be sent to the offline Internet of Things device. The device state of the Internet of Things device after remote wake-up is obtained. If the device state is still device offline, it means that a staff member needs to be dispatched to the device placement of the corresponding Internet of Things device to perform the repair.

[0118] The application introduces the device state of the Internet of Things device, determines whether the Internet of Things device needs to be overhauled based on the device state, improves the normativeness of overhaul determination, and determines the manual overhaul requirement based on the device state of the Internet of Things device after remote wake-up, which is more reasonable.

[0119] In one embodiment, the determining of the distance corresponding to the closest worker of the Internet of Things device for overhaul comprises:

[0120] obtaining a second ranging scheme when the worker performs the overhaul and a first device identifier corresponding to the Internet of Things device;

[0121] sequentially traversing the Internet of Things device corresponding to the first device identifier, and each time the first device identifier of the Internet of Things device being currently traversed is obtained and used as a second device identifier;

[0122] determining, based on the second ranging scheme, a second point distance between the worker and the Internet of Things device corresponding to the second device identifier;

[0123] determining the worker corresponding to the shortest second point distance in the second point distance as a target overhaul worker;

[0124] guiding the target overhaul worker to the Internet of Things device corresponding to the second device identifier for overhaul;

[0125] When the traversal of the Internet of Things device corresponding to all the first device identifiers is completed, the determination is completed.

[0126] The working principle and beneficial effects of the above technical solution are as follows:

[0127] The second ranging scheme is a ranging scheme applied to worker overhaul. The first device identifier is a unique device ID of the Internet of Things device that needs to be overhauled. The first device identifier corresponding to the Internet of Things device is sequentially traversed, and the first device identifier of the Internet of Things device being currently traversed is used as a second device identifier. According to the second ranging scheme, a second point distance between the worker and the Internet of Things device of the second device identifier (the distance between the worker and the Internet of Things device corresponding to the second device identifier) is calculated. The worker corresponding to the shortest second point distance in the second point distance is determined as a target overhaul worker, and the target overhaul worker is guided to find the Internet of Things device corresponding to the second device identifier and perform overhaul.

[0128] The application introduces the second ranging scheme to determine the second point distance between the worker and the Internet of Things device that needs to be overhauled, improves the accuracy of the second point distance acquisition, and determines the worker corresponding to the shortest second point distance in the second point distance as a target overhaul worker, which improves the rationality of the target overhaul worker determination.

[0129] In one embodiment, the guiding the target maintenance personnel to the corresponding Internet of Things device corresponding to the second device identifier for maintenance comprises:

[0130] Obtaining three-dimensional space data of a device placement space where the Internet of Things device corresponding to the second device identifier is located;

[0131] According to the three-dimensional space data, a space model corresponding to the device placement space is constructed;

[0132] The space model is spatially divided to obtain a subspace model;

[0133] A target subspace model capable of accommodating the Internet of Things device corresponding to the second device identifier in the subspace model is determined;

[0134] Based on a preset annotation rule, the target subspace model is annotated in the space model, and image information of the annotated space model is sent to the target maintenance personnel.

[0135] The working principle and beneficial effects of the above technical solution are:

[0136] When the staff goes to the Internet of Things device for maintenance, the placement space of the device may have multiple floors and large space. When the staff arrives at the entrance of the placement space of the Internet of Things device to be maintained, there is a situation that the positioning accuracy is insufficient and the Internet of Things device is difficult to find. Therefore, a solution is urgently needed.

[0137] The device placement space is, for example, the placement space of the Internet of Things device corresponding to the second device identifier. The three-dimensional space data is, for example, the three-dimensional data of the placement room of the Internet of Things device corresponding to the second device identifier. The space model is a three-dimensional model corresponding to the placement space. When constructing the space model, the model can be constructed based on three-dimensional model construction technology according to the three-dimensional space data. The three-dimensional model construction technology belongs to the prior art, and its principle is not described in detail. The subspace model is a local space model obtained by spatially dividing the space model. The division rule is set by the staff in advance. The target subspace model capable of accommodating the Internet of Things device to be maintained in the subspace model is determined. The local space corresponding to the target subspace model is the area where the Internet of Things device to be maintained can be placed. It is necessary to mark and remind the target maintenance personnel to find it. Therefore, based on the preset annotation rule, the target subspace model is annotated in the space model. The preset annotation rule is a rule for annotating the space model, for example, the target subspace model is annotated as red in the space model. The image information is the annotation image of the annotated space model. The image information is sent to the terminal (such as a mobile phone) carried by the target maintenance personnel.

[0138] The application introduces a space model, highlights a sub-space model in the space model that can accommodate an Internet of Things device requiring maintenance, and sends image information corresponding to the labeled space model to the target maintenance personnel, so that the maintenance process is more appropriate and more humanized.

[0139] In one embodiment, the determining of the target sub-space model capable of accommodating the Internet of Things device corresponding to the second device identifier comprises:

[0140] Obtaining first shape description information of the sub-space model;

[0141] Extracting a second feature value of the first shape description information based on a preset second feature extraction template;

[0142] Constructing a first shape description vector based on the second feature value;

[0143] Obtaining second shape description information of the Internet of Things device corresponding to the second device identifier;

[0144] Extracting a third feature value of the second shape description information based on the second feature extraction template;

[0145] Constructing a second shape description vector based on the third feature value;

[0146] Matching the first shape description vector and the second shape description vector to determine a sub-space model corresponding to the first shape description vector that matches, and taking the sub-space model as the target sub-space model.

[0147] The working principle and beneficial effects of the above technical solution are:

[0148] The first shape description information is, for example, a cube with a length and a width of 1.2 m and a height of 1.5 m. The preset second feature extraction template is a template for extracting shape description features, and the second feature value of the first shape description information is extracted based on the second feature extraction template, such as the shape and size of the sub-space model. The first shape description vector is constructed based on the second feature value, and is used to represent the shape of the sub-space model. The second shape description information is the description information describing the shape of the device corresponding to the second device identifier. The third feature value is the shape and size of the Internet of Things device. The second shape description vector is used to represent the shape of the Internet of Things device. The first shape description vector and the second shape description vector are matched, and if the match is correct, it means that the matched first shape description vector can accommodate the Internet of Things device, and the corresponding sub-space model is obtained and taken as the target sub-space model.

[0149] The application introduces the first shape description vector and the second shape description vector and performs vector matching, determines a target subspace model suitable for placing Internet of Things equipment for maintenance by a target maintainer based on a matching result, and improves the accuracy of the target subspace model determination.

[0150] The embodiment of the application provides a system for calculating the distance between a point and a position of Internet of Things equipment, as shown in the accompanying drawings, comprising: Figure 2

[0151] The acquisition module 1 is configured to acquire calculation requirement information of a target person for calculating the distance between a point and a position of Internet of Things equipment.

[0152] The training module 2 is configured to train a ranging scheme determination model, input the calculation requirement information into the ranging scheme determination model, and obtain a first ranging scheme.

[0153] The calculation module 3 is configured to calculate a first point-to-position distance between the target person and the Internet of Things equipment based on the first ranging scheme.

[0154] In one embodiment, the acquisition module acquires calculation requirement information of Internet of Things equipment that needs to perform point-to-position distance calculation, comprising:

[0155] Acquiring calculation use information of the target person;

[0156] Determining the calculation requirement information based on the calculation use information;

[0157] And / or,

[0158] Acquiring the calculation requirement information input by the target person into a ranging calculation accuracy requirement input interface.

[0159] Obviously, those skilled in the art can make various modifications and variations to the application without departing from the spirit and scope of the application. Thus, if these modifications and variations of the application belong to the scope of the claims of the application and equivalent technologies thereof, the application also intends to include these modifications and variations.​

Claims

1. A method for calculating the distance between IoT device locations, characterized in that, The method comprises the following steps: Step 1: obtaining the calculation requirement information of the target personnel for the point-to-point distance calculation of the Internet of Things device; Step 2: training a distance measurement scheme determination model, inputting the calculation requirement information into the distance measurement scheme determination model to obtain a first distance measurement scheme; Step 3: calculating the first point-to-point distance between the target personnel and the Internet of Things device based on the first distance measurement scheme; The training distance measurement scheme determination model comprises: Obtaining a plurality of artificial distance measurement scheme determination pre-selection determination process records from a pre-set big data platform; Reasonably analyzing the distance measurement scheme determination process corresponding to the pre-selection determination process record to obtain a rationality degree; If the rationality degree is greater than or equal to a pre-set rationality threshold, the pre-selection determination process record corresponding to the pre-selection determination process record is taken as a target determination process record; Inputting the target determination process record into a pre-set neural network model for model training to obtain the distance measurement scheme determination model trained to a convergence state; The reasonably analyzing the distance measurement scheme determination process corresponding to the pre-selection determination process record to obtain a rationality degree comprises: Process splitting of the distance measurement scheme determination process to obtain a plurality of distance measurement scheme determination sub-processes; Determining the rationality value of the distance measurement scheme determination sub-process based on a pre-set rationality value determination template; Extracting a first feature value of the distance measurement scheme determination sub-process based on a pre-set first feature extraction template; Constructing a first process description factor based on the first feature value; Obtaining a pre-set process weight library, the process weight library comprising: a plurality of one-to-one second process description factors and process weight values; Matching the first process description factor with each second process description factor to obtain a matching value, and taking the process weight value corresponding to the second process description factor with the largest matching value as a target process weight value; Multiplying the target process weight value by the corresponding rationality value to obtain a target summation value, and associating the target summation value with the distance measurement scheme determination process; Accumulatively calculating the target summation value associated with each distance measurement scheme determination process to obtain the rationality degree.

2. The method of claim 1, wherein, The obtaining of the calculation requirement information of the target personnel for the point-to-point distance calculation of the Internet of Things device comprises: Obtaining the calculation purpose information of the target personnel; Determining the calculation requirement information based on the calculation purpose information; And / or, Obtaining the calculation requirement information input by the target personnel into a distance measurement calculation precision requirement input interface.

3. The method of claim 1, wherein, Further comprising: Determining whether the Internet of Things device for the point-to-point distance calculation needs to be maintained, and if so, determining the nearest worker to the Internet of Things device for maintenance; The determination of whether the Internet of Things device for the point-to-point distance calculation needs to be maintained comprises: Obtaining the device state of the Internet of Things device, the device state comprising: device online and device offline; If the device state is device offline, attempting to remotely wake up the corresponding Internet of Things device; Obtaining the device state of the corresponding Internet of Things device after remote wake-up; If the device state corresponding to the Internet of Things device after remote wake-up is that the device is offline, the corresponding Internet of Things device after remote wake-up needs to be overhauled.

4. The IoT device point location distance calculation method of claim 3, wherein, The determination of the worker closest to the Internet of Things device includes: Obtaining a second ranging scheme when the worker overhauls and a first device identifier corresponding to the Internet of Things device; Iterating through the Internet of Things devices corresponding to the first device identifier in turn, and each time iterating, obtaining the first device identifier of the Internet of Things device currently being iterated and taking it as a second device identifier; Based on the second ranging scheme, determining the second point distance between the worker and the Internet of Things device corresponding to the second device identifier; Determining the worker corresponding to the shortest second point distance in the second point distance as a target maintenance personnel; Guiding the target maintenance personnel to the Internet of Things device corresponding to the second device identifier for maintenance; After all the Internet of Things devices corresponding to the first device identifier are iterated, the determination is completed.

5. The method of claim 4, wherein the method further comprises: The guiding the target maintenance personnel to the Internet of Things device corresponding to the second device identifier for maintenance includes: Obtaining three-dimensional space data of a device placement space where the Internet of Things device corresponding to the second device identifier is located; According to the three-dimensional space data, a space model corresponding to the device placement space is constructed; Spatially dividing the space model to obtain a subspace model; Determining a target subspace model in the subspace model that can accommodate the Internet of Things device corresponding to the second device identifier; Based on a preset labeling rule, the target subspace model is labeled in the space model, and image information of the labeled space model is sent to the target maintenance personnel.

6. The method of claim 5, wherein the method comprises: The determination of the target subspace model in the subspace model that can accommodate the Internet of Things device corresponding to the second device identifier includes: Obtaining first shape description information of the subspace model; Based on a preset second feature extraction template, extracting a second feature value of the first shape description information; Based on the second feature value, a first shape description vector is constructed; Obtaining second shape description information of the Internet of Things device corresponding to the second device identifier; Based on the second feature extraction template, extracting a third feature value of the second shape description information; Based on the third feature value, a second shape description vector is constructed; Matching the first shape description vector and the second shape description vector, determining the subspace model corresponding to the first shape description vector that matches the first shape description vector as the target subspace model.

7. An Internet of Things device point location distance calculation system, characterized by, It includes: An acquisition module is configured to acquire calculation requirement information of a target personnel for Internet of Things device point distance calculation; A training module is configured to train a ranging scheme determination model, input the calculation requirement information into the ranging scheme determination model, and obtain a first ranging scheme; A calculation module is configured to calculate a first point distance between the target personnel and the Internet of Things device based on the first ranging scheme; The training ranging scheme determination model includes: obtain a pre-selected determination process record of a plurality of artificial ranging scheme determinations from a pre-set big data platform; perform rationality analysis on a ranging scheme determination process corresponding to the pre-selected determination process record to obtain a rationality degree; if the rationality degree is greater than or equal to a pre-set rationality threshold, take the pre-selected determination process record as a target determination process record; input the target determination process record into a pre-set neural network model to perform model training to obtain the ranging scheme determination model trained to a convergence state; wherein the rationality analysis on the ranging scheme determination process corresponding to the pre-selected determination process record to obtain the rationality degree comprises: perform process splitting on the ranging scheme determination process to obtain a plurality of ranging scheme determination sub-processes; determine rational values of the ranging scheme determination sub-processes based on a pre-set rational value determination template; extract first feature values of the ranging scheme determination sub-processes based on a pre-set first feature extraction template; construct first process description factors based on the first feature values; obtain a pre-set process weight library, the process weight library comprising a plurality of one-to-one second process description factors and process weight values; match the first process description factor with each second process description factor to obtain a matching value, and take the process weight value corresponding to the second process description factor with the largest matching value as a target process weight value; multiply the target process weight value by the corresponding rational value to obtain a target summation value, and associate the target summation value with the ranging scheme determination process; cumulatively calculate the target summation values associated with each ranging scheme determination process to obtain the rationality degree.

8. The Internet of Things device point location distance calculation system of claim 7, wherein, The obtaining module obtains the calculation requirement information of the target personnel for the Internet of Things device point distance calculation, comprising: obtain the calculation use information of the target personnel; determine the calculation requirement information based on the calculation use information; and / or, obtain the calculation requirement information of the target personnel inputting the ranging calculation precision requirement input interface.

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