Environmental compensation method and system for detection data processing

Abstract

The present invention relates to fields related to data environment compensation, and discloses an environment compensation method and system for detection data processing. The method and system implement environmental compensation for the detection data by quantitatively evaluating environmental noise data based on sensor data groups and multi-object collaborative evaluation, thereby achieving the purpose of separating actual object data and environmental noise data in the detection data, so as to obtain more accurate and reliable detection data. Compared with the solution of using environmental model evaluation in the prior art, the method and system have wider adaptability, and can more efficiently and accurately obtain environmental compensation parameters in real time for scenarios where environmental data is changeable and difficult to obtain.

Description

Technical Field

[0001] The present invention relates to the field related to data environment compensation, and in particular to an environment compensation method and system for detecting data processing. Background Art

[0002] When performing non-contact object requirement parameter detection in the environment, because the non-contact sensor is collecting data, changes in the environment and changes in the properties of the information transmission medium in the environment will cause the actual detection data to be mixed with error data caused by environmental factors. Therefore, after obtaining the corresponding detection data through the non-contact sensor, it is also necessary to compensate for the environmental noise of the detection data.

[0003] Most of the compensation methods in the prior art adopt an evaluation method based on environmental parameters, that is, by obtaining relevant meteorological data, such as temperature, humidity, aerogel content, and suspended particle content, the environmental compensation amount to be performed is estimated based on the fitting relationship between the intensity of these parameters and the intensity of environmental noise; however, in actual use, these environmental impact quantities require the use of more sensors to achieve accurate acquisition, so there is a greater difficulty in execution, and the range meteorological data based on platforms such as the Meteorological Bureau has low data reliability for locally installed sensors. Summary of the invention

[0004] The object of the present invention is to provide an environment compensation method and system for detection data processing to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An environmental compensation method for detection data processing, comprising:

[0007] Detecting alignment of the collection of sensing data with initial calibration data to obtain multiple sets of quantitative data of initial environmental noise, wherein the initial calibration data is used to characterize independently measured noise-free standard object data;

[0008] Performing feature determination point matching on the multiple sets of detection sensor data, respectively obtaining noise difference data of the multiple sets of feature determination points, performing matching based on the difference data and corresponding quantized data, and obtaining a conversion mapping relationship between the difference data and the environmental noise, wherein the feature determination points are used to characterize fixed objects in the multiple sets of continuous detection sensor data;

[0009] Acquire real-time detection data, perform matching based on a plurality of the feature determination points, acquire a plurality of matching feature determination points, and read corresponding current detection parameters;

[0010] Calculating the noise difference data of the feature determination point based on the current detection parameter, and obtaining the current environmental noise data according to the conversion mapping relationship;

[0011] An environmental compensation calculation is performed on the real-time detection data according to the environmental noise data to obtain denoised detection data, wherein the environmental compensation calculation is used to subtract the real-time detection data from the environmental noise data to eliminate the environmental noise portion in the detection data.

[0012] As a further solution of the present invention: the step of aligning the collection of the detection sensor data with the initial calibration data to obtain multiple sets of quantized data of the initial environmental noise specifically includes:

[0013] Collect multiple sets of detection sensor data at preset time nodes, wherein the detection sensor data is a data array used to represent the sensor data array of each effective detection point within the detection range;

[0014] Acquiring initial calibration data uploaded by the terminal based on the time node, the initial calibration data is used to characterize the standard data of the object to be detected at a certain time node, the standard data is used to characterize the independently measured data without environmental noise, and the initial calibration data correspondingly includes positioning mark and timestamp information;

[0015] The positioning marks of the initial calibration data are matched with the corresponding detection points of the detection sensor data, and the sensor data of the corresponding detection points are subtracted from the initial calibration data to generate multiple groups of quantized data of the initial environmental noise.

[0016] As a further solution of the present invention: the step of performing feature determination point matching on multiple groups of the detection sensor data, respectively obtaining noise difference data of multiple groups of the feature determination points, performing matching based on the difference data and the corresponding quantized data, and obtaining the conversion mapping relationship between the difference data and the environmental noise specifically includes:

[0017] Positioning and superimposing the multiple sets of detection sensor data by detecting point information, so that the effective detection points of the multiple sets of detection sensor data overlap correspondingly in the spatial coordinates;

[0018] Performing object feature judgment on the area corresponding to the non-initial calibration data of the superimposed multiple sets of detection sensor data, if the object feature corresponding to a certain valid detection point is consistently represented in the multiple sets of detection sensor data, marking the valid detection point as a feature judgment point;

[0019] Calculate noise difference data of adjacent groups of detection sensor data at the feature determination point based on the time axis direction;

[0020] The noise difference data corresponding to the same detection sensor data combination is fitted to the quantized data to generate a plurality of mapping functions of the noise difference data to the quantized data, that is, a conversion mapping relationship between the difference data and the ambient noise.

[0021] As a further solution of the present invention: it also includes a priority weight allocation step, specifically including:

[0022] Taking the point to be evaluated as the center, vertical feature evaluation is performed on adjacent feature determination points respectively to assess the priority weights of the feature determination points. The priority weights are used to determine the selection order of the feature determination points when performing environmental compensation calculations;

[0023] The vertical feature evaluation is used to obtain historical data of multiple feature determination points around the point to be evaluated, and calculate the weight ratio of the corresponding quantized data and noise difference data in the historical data. The smaller the difference between the weight ratio and the value one, the higher the priority weight.

[0024] As a further solution of the present invention: it also includes a background noise correction step, specifically including:

[0025] Obtain denoised detection data of the same point to be evaluated at multiple sensor ends, perform regression analysis on multiple groups of denoised detection data to obtain corresponding regression values, and calculate regression deviations of multiple sensors relative to the regression values;

[0026] Obtain regression deviations corresponding to multiple evaluation points on the sensor end, perform mathematical expectation calculation on the multiple regression deviations, and obtain a background noise correction value of the sensor, wherein the background noise correction value is used to optimize the conversion mapping relationship.

[0027] The embodiment of the present invention aims to provide an environmental compensation system for detecting data processing, comprising:

[0028] A data calibration module, used to detect the alignment of the collection of sensor data with initial calibration data to obtain multiple sets of quantitative data of initial environmental noise, wherein the initial calibration data is used to characterize independently measured noise-free standard object data;

[0029] A detection mapping module, used to match the characteristic decision points of the multiple groups of detection sensor data, respectively obtain the noise difference data of the multiple groups of characteristic decision points, match based on the difference data and the corresponding quantized data, and obtain the conversion mapping relationship between the difference data and the environmental noise, wherein the characteristic decision points are used to characterize the fixed objects in the continuous multiple groups of detection sensor data;

[0030] A determination and selection module is used to obtain real-time detection data, perform matching based on a number of the feature determination points, obtain a plurality of matching feature determination points, and read the corresponding current detection parameters;

[0031] A noise evaluation module, used to calculate the noise difference data of the feature determination point based on the current detection parameters, and to obtain the current environmental noise data according to the conversion mapping relationship;

[0032] The compensation calculation module is used to perform environmental compensation calculation on the real-time detection data according to the environmental noise data to obtain denoised detection data. The environmental compensation calculation is used to subtract the real-time detection data from the environmental noise data to eliminate the environmental noise part in the detection data.

[0033] As a further solution of the present invention: the data calibration module includes:

[0034] A data acquisition unit, used to collect multiple sets of detection sensor data at preset time nodes, wherein the detection sensor data is a data array, which is used to represent the sensor data array of each effective detection point within the detection range;

[0035] A calibration acquisition unit, used for acquiring initial calibration data uploaded by the terminal based on the time node, wherein the initial calibration data is used to characterize the standard data of the object to be detected at a certain time node, and the standard data is used to characterize the independently measured data without environmental noise, and the initial calibration data correspondingly includes positioning mark and timestamp information;

[0036] The calibration execution unit is used to match the positioning marks of the initial calibration data with the corresponding detection points of the detection sensor data, and to generate multiple groups of quantized data of the initial environmental noise by subtracting the sensor data of the corresponding detection points from the initial calibration data.

[0037] As a further solution of the present invention: the detection mapping module includes:

[0038] An object superposition unit, used for locating and superimposing the multiple groups of detection sensor data by detecting point information, so that the effective detection points of the multiple groups of detection sensor data overlap correspondingly in the spatial coordinates;

[0039] A superposition selection unit is used to perform object feature judgment on the area corresponding to the non-initial calibration data of the superimposed multiple sets of detection sensor data, and if the object feature corresponding to a certain valid detection point is consistently represented in the multiple sets of detection sensor data, the valid detection point is marked as a feature judgment point;

[0040] A difference calculation unit, used for calculating noise difference data of adjacent groups of detection sensor data at the feature determination point based on the time axis direction;

[0041] The fitting mapping unit is used to fit the noise difference data corresponding to the same detection sensor data combination to the quantized data to generate multiple groups of mapping functions of the noise difference data to the quantized data, that is, the conversion mapping relationship between the difference data and the environmental noise.

[0042] As a further solution of the present invention: it also includes a priority weight allocation module, specifically including:

[0043] A vertical evaluation unit, used to perform vertical feature evaluation on adjacent feature determination points with the point to be evaluated as the center, so as to assess the priority weights of the feature determination points, wherein the priority weights are used to determine the selection order of the feature determination points when performing environmental compensation calculations;

[0044] The weight evaluation unit is used for the vertical feature evaluation to obtain historical data of multiple feature judgment points around the point to be evaluated, and calculate the weight ratio of the corresponding quantized data and noise difference data in the historical data. The smaller the difference between the weight ratio and the value one, the higher the priority weight.

[0045] As a further solution of the present invention: it also includes a background noise correction module, specifically including:

[0046] A multi-object evaluation unit, used to obtain denoised detection data of the same point to be evaluated at multiple sensor ends, and perform regression analysis on multiple groups of denoised detection data to obtain corresponding regression values, and calculate regression deviations of multiple sensors relative to the regression values;

[0047] The multi-sample correction unit is used to obtain the regression deviations corresponding to multiple evaluation points on the sensor end, and perform mathematical expectation calculation on the multiple regression deviations to obtain the background noise correction value of the sensor, and the background noise correction value is used to optimize the conversion mapping relationship.

[0048] Compared with the prior art, the beneficial effects of the present invention are: by adopting a method based on sensor data groups and multi-object collaborative evaluation to quantitatively evaluate environmental noise data, thereby realizing environmental compensation for the detection data, and achieving the purpose of separating the actual object data and environmental noise data in the detection data, so as to obtain more accurate and reliable detection data. Compared with the solution of using environmental model evaluation in the prior art, it has a wider adaptability and can more efficiently and accurately obtain environmental compensation parameters in real time for scenarios where environmental data is changeable and difficult to obtain. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The present invention is a flowchart of an environmental compensation method for detecting data processing.

[0050] Figure 2A flowchart for obtaining quantitative data in an environmental compensation method for detection data processing.

[0051] Figure 3 The present invention is a flowchart of the steps of obtaining a conversion mapping relationship in an environmental compensation method for detecting data processing.

[0052] Figure 4 The figure is a block diagram of the environmental compensation system used for detecting data processing. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments.

[0055] like Figure 1 The environmental compensation method for detecting data processing provided by an embodiment of the present invention comprises the following steps:

[0056] S10, detecting the alignment of the collection of sensing data with the initial calibration data to obtain multiple sets of quantitative data of initial environmental noise, wherein the initial calibration data is used to characterize independently measured noise-free standard object data;

[0057] S20, performing feature determination point matching on the multiple sets of detection sensor data, respectively obtaining noise difference data of the multiple sets of feature determination points, performing matching based on the difference data and corresponding quantized data, and obtaining a conversion mapping relationship between the difference data and the environmental noise, wherein the feature determination points are used to characterize fixed objects in the multiple sets of continuous detection sensor data;

[0058] S30, acquiring real-time detection data, performing matching based on a plurality of the feature determination points, acquiring a plurality of matching feature determination points, and reading corresponding current detection parameters;

[0059] S40, calculating the noise difference data of the feature determination point based on the current detection parameter, and obtaining the current environmental noise data according to the conversion mapping relationship;

[0060] S50, performing environmental compensation calculation on the real-time detection data according to the environmental noise data to obtain denoised detection data, wherein the environmental compensation calculation is used to subtract the real-time detection data from the environmental noise data to eliminate the environmental noise portion in the detection data.

[0061] In this embodiment, an environmental compensation method for detection data processing is provided. Environmental noise data is quantitatively evaluated by adopting a method based on sensor data groups and multi-object collaborative evaluation, thereby realizing environmental compensation for the detection data, and achieving the purpose of separating actual object data and environmental noise data in the detection data, so as to obtain more accurate and reliable detection data. Compared with the solution of using environmental model evaluation in the prior art, it has wider adaptability and can more efficiently and accurately obtain environmental compensation parameters in real time for scenes where environmental data is changeable and difficult to obtain. In the prior art, when performing demand parameter detection of non-contact objects in the environment, non-contact sensors are used. After obtaining the corresponding detection data, it is also necessary to compensate and correct the environmental noise of the detection data. Because when the non-contact sensor collects data, changes in the environment and changes in the properties of the information transmission medium in the environment will cause the actual detection data to be mixed with error data caused by environmental factors. The current compensation methods in the technology mostly adopt the method of evaluation based on environmental parameters, that is, by obtaining related meteorological data, such as temperature, humidity, aerogel content and suspended particle content, etc., based on the fitting relationship between the intensity of these parameters and the intensity of environmental noise, to obtain the amount of environmental compensation that should be performed; but in actual use, for The acquisition of parameters of these environmental impact quantities requires the cooperation of more sensors to be able to obtain them more accurately, so there is a great difficulty in execution resistance. Therefore, the current technology is mostly used to synchronously obtain the corresponding environmental parameters by connecting to the meteorological system of the current area. However, the relevant environmental data of the meteorological system is collected by distributed nodes within a certain range, and the relevant data of other areas are obtained by fitting. Therefore, there is a large regional error. It is no longer applicable in scenarios with relatively high accuracy requirements, and it is impossible to effectively provide environmental data compensation. At the same time, when multiple environmental variables work together, the impact on the accuracy and difficulty of the evaluation becomes more difficult; the solution of this embodiment That is: in the initial stage of sensor setting, multiple measurement objects are selected to obtain standard data without environmental noise influence through corresponding fixed-point detection, and the set sensors are calibrated through these data to obtain the environmental noise difference of the sensors at these different points; at this time, as long as the corresponding sensor collects the detection data of multiple characteristic judgment points, the relationship of the calibrated environmental noise difference can be recorded (here, the detection data of multiple other nodes is collected at the same time as the calibration data, so in a small area, the environmental influence parameters are consistent and unchanged, so the calibration data can be converted into the mapping relationship of other fixed objects within the detection range), that is, the corresponding conversion mapping relationship is generated;The selection criteria of the feature judgment point are fixed objects in multiple detection feedback data. For example, within the sensor detection range, there is a fixed building object whose own detection data change is basically fixed, and its position and attributes will not change. Therefore, in the subsequent data detection, the change in the current environmental noise can be judged according to the change in the detection data of the feature judgment point, and then the environmental compensation amount of the node to be detected is evaluated through the conversion mapping relationship, so as to achieve the purpose of environmental compensation for the detection data. ;

[0062] like Figure 2 As shown, as another preferred embodiment of the present invention, the step of aligning the collection of the detection sensor data with the initial calibration data to obtain multiple groups of quantized data of the initial environmental noise specifically includes:

[0063] S11, collecting multiple sets of detection sensor data at preset time nodes, wherein the detection sensor data is a data array used to represent the sensor data array of each effective detection point within the detection range;

[0064] S12, based on the time node, obtaining initial calibration data uploaded by the terminal, the initial calibration data is used to characterize the standard data of the object to be detected at a certain time node, the standard data is used to characterize the independently measured data without environmental noise, and the initial calibration data correspondingly includes positioning mark and timestamp information;

[0065] S13, matching the positioning marks of the initial calibration data with the corresponding detection points of the detection sensor data, and subtracting the sensor data of the corresponding detection points from the initial calibration data to generate multiple groups of quantized data of the initial environmental noise.

[0066] In this embodiment, the process of aligning the initial calibration data is further described in detail. Specifically, at the initial stage of assembly of the sensor equipment, multiple time nodes are reserved, and the sensors collect multiple groups of detection sensor data respectively, and the management personnel perform zero-distance detection on the detection object to obtain initial calibration data without the influence of environmental noise. Then, the difference calculation can be performed based on different data of the same detection point at the same time node to calculate the initial environmental noise quantification data, that is, to characterize the environmental noise value generated by the environment between the current detection point and the sensor at that time.

[0067] like Figure 3 As shown, as another preferred embodiment of the present invention, the step of performing feature determination point matching on multiple groups of detection sensor data, respectively obtaining multiple groups of noise difference data of the feature determination points, performing matching based on the difference data and the corresponding quantized data, and obtaining the conversion mapping relationship between the difference data and the environmental noise specifically includes:

[0068] S21, positioning and superimposing the multiple groups of detection sensor data by detecting point information, so that the effective detection points of the multiple groups of detection sensor data overlap correspondingly in the spatial coordinates;

[0069] S22, performing object feature judgment on the area corresponding to the non-initial calibration data of the superimposed multiple sets of detection sensor data, if the object feature corresponding to a certain valid detection point is consistently represented in the multiple sets of detection sensor data, marking the valid detection point as a feature judgment point;

[0070] S23, calculating noise difference data of adjacent groups of detection sensor data at the feature determination point based on the time axis direction;

[0071] S24, fitting the noise difference data corresponding to the same detection sensor data combination to the quantized data to generate a plurality of mapping functions of the noise difference data to the quantized data, that is, a conversion mapping relationship between the difference data and the ambient noise.

[0072] In this embodiment, the process of obtaining the mapping relationship is supplemented, and its main function is to associate and map the quantified data in the previous embodiment, and convert it into a mapping function relationship that can be transmitted and used for a long time, so that when the subsequent objects of the initially calibrated points disappear, the mapping relationship can be converted and inherited. Specifically, the method adopted is to perform feature judgment on the continuously collected data, select the objects that exist stably for a long time as the feature judgment points, and convert and inherit the noise correlation between the quantified data and the detection data to the feature judgment points. In this way, even if the current feature judgment points and the objects at the corresponding points of the initial calibration data disappear, new feature judgment points can be evaluated through the most recent multiple sets of historical data to convert the mapping function, which can effectively ensure the continuation of the environmental compensation judgment standard.

[0073] As another preferred embodiment of the present invention, it also includes a priority weight allocation step, specifically including:

[0074] Taking the point to be evaluated as the center, vertical feature evaluation is performed on adjacent feature determination points respectively to assess the priority weights of the feature determination points. The priority weights are used to determine the selection order of the feature determination points when performing environmental compensation calculations;

[0075] The vertical feature evaluation is used to obtain historical data of multiple feature determination points around the point to be evaluated, and calculate the weight ratio of the corresponding quantized data and noise difference data in the historical data. The smaller the difference between the weight ratio and the value one, the higher the priority weight.

[0076] In this embodiment, in actual use, the farther the distance between the feature judgment point and the point to be detected, the greater the difference in the numerical degree of the impact of environmental noise between the two. This is because when the distance becomes longer, the uniformity of environmental factors between the two and the sensor may change more. Therefore, a priority weight judgment step is set here. The main scheme is: judging through historical data, within a certain range, the closer the ratio of quantitative data to the environmental noise of the feature judgment point is to 1, the closer the environmental similarity between the two is (or the distance between the two in space is closer), and therefore the corresponding priority weight is higher.

[0077] As another preferred embodiment of the present invention, it also includes a background noise correction step, specifically including:

[0078] Obtain denoised detection data of the same point to be evaluated at multiple sensor ends, perform regression analysis on multiple groups of denoised detection data to obtain corresponding regression values, and calculate regression deviations of multiple sensors relative to the regression values;

[0079] Obtain regression deviations corresponding to multiple evaluation points on the sensor end, perform mathematical expectation calculation on the multiple regression deviations, and obtain a background noise correction value of the sensor, wherein the background noise correction value is used to optimize the conversion mapping relationship.

[0080] In this embodiment, in the actual correction, the environmental noise actually includes two parts (based on the difference algorithm of the aforementioned embodiment). One is the background noise of the sensor device, which is caused by current, heat and other reasons. The other is the noise of the surrounding environment in the detection data collection. For the background noise of the sensor device, it may gradually increase with the reduction of device performance in long-term use. Therefore, correction is required to ensure the accuracy of environmental compensation. Because in the mapping function, the background noise is expressed as a constant, and this constant does not change much in a short period of time, the background noise is corrected by multi-object evaluation in this embodiment.

[0081] like Figure 4 As shown, the present invention also provides an environmental compensation system for detection data processing, which comprises:

[0082] A data calibration module 100 is used to detect the alignment of the collection of sensor data with initial calibration data to obtain multiple sets of quantitative data of initial environmental noise, wherein the initial calibration data is used to characterize independently measured noise-free standard object data;

[0083] The detection mapping module 200 is used to match the feature decision points of the multiple sets of detection sensor data, respectively obtain the noise difference data of the multiple sets of feature decision points, match based on the difference data and the corresponding quantized data, and obtain the conversion mapping relationship between the difference data and the environmental noise, wherein the feature decision points are used to characterize the fixed objects in the continuous multiple sets of detection sensor data;

[0084] The determination and selection module 300 is used to obtain real-time detection data, perform matching based on a number of the feature determination points, obtain a plurality of matching feature determination points, and read the corresponding current detection parameters;

[0085] The noise evaluation module 400 is used to calculate the noise difference data of the feature determination point based on the current detection parameters, and obtain the current environmental noise data according to the conversion mapping relationship;

[0086] The compensation calculation module 500 is used to perform environmental compensation calculation on the real-time detection data according to the environmental noise data to obtain denoised detection data. The environmental compensation calculation is used to subtract the real-time detection data from the environmental noise data to eliminate the environmental noise part in the detection data.

[0087] As another preferred embodiment of the present invention, the data calibration module includes:

[0088] A data acquisition unit, used to collect multiple sets of detection sensor data at preset time nodes, wherein the detection sensor data is a data array, which is used to represent the sensor data array of each effective detection point within the detection range;

[0089] A calibration acquisition unit, used for acquiring initial calibration data uploaded by the terminal based on the time node, wherein the initial calibration data is used to characterize the standard data of the object to be detected at a certain time node, and the standard data is used to characterize the independently measured data without environmental noise, and the initial calibration data correspondingly includes positioning mark and timestamp information;

[0090] The calibration execution unit is used to match the positioning marks of the initial calibration data with the corresponding detection points of the detection sensor data, and to generate multiple groups of quantized data of the initial environmental noise by subtracting the sensor data of the corresponding detection points from the initial calibration data.

[0091] As another preferred embodiment of the present invention, the detection mapping module includes:

[0092] An object superposition unit, used for locating and superimposing the multiple groups of detection sensor data by detecting point information, so that the effective detection points of the multiple groups of detection sensor data overlap correspondingly in the spatial coordinates;

[0093] A superposition selection unit is used to perform object feature judgment on the area corresponding to the non-initial calibration data of the superimposed multiple sets of detection sensor data, and if the object feature corresponding to a certain valid detection point is consistently represented in the multiple sets of detection sensor data, the valid detection point is marked as a feature judgment point;

[0094] A difference calculation unit, used for calculating noise difference data of adjacent groups of detection sensor data at the feature determination point based on the time axis direction;

[0095] The fitting mapping unit is used to fit the noise difference data corresponding to the same detection sensor data combination to the quantized data to generate multiple groups of mapping functions of the noise difference data to the quantized data, that is, the conversion mapping relationship between the difference data and the environmental noise.

[0096] As another preferred embodiment of the present invention, it also includes a priority weight allocation module, specifically including:

[0097] A vertical evaluation unit, used to perform vertical feature evaluation on adjacent feature determination points with the point to be evaluated as the center, so as to assess the priority weights of the feature determination points, wherein the priority weights are used to determine the selection order of the feature determination points when performing environmental compensation calculations;

[0098] The weight evaluation unit is used for the vertical feature evaluation to obtain historical data of multiple feature judgment points around the point to be evaluated, and calculate the weight ratio of the corresponding quantized data and noise difference data in the historical data. The smaller the difference between the weight ratio and the value one, the higher the priority weight.

[0099] As another preferred embodiment of the present invention, it also includes a background noise correction module, specifically including:

[0100] A multi-object evaluation unit, used to obtain denoised detection data of the same point to be evaluated at multiple sensor ends, and perform regression analysis on multiple groups of denoised detection data to obtain corresponding regression values, and calculate regression deviations of multiple sensors relative to the regression values;

[0101] The multi-sample correction unit is used to obtain the regression deviations corresponding to multiple evaluation points on the sensor end, and perform mathematical expectation calculation on the multiple regression deviations to obtain the background noise correction value of the sensor, and the background noise correction value is used to optimize the conversion mapping relationship.

[0102] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0103] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the disclosure in the specification and examples. This application is intended to cover any variations, uses or adaptations of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the claims.

[0104] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. An environmental compensation method for detection data processing, characterized in that: Include: Detecting alignment of the collection of sensing data with initial calibration data to obtain multiple sets of quantitative data of initial environmental noise, wherein the initial calibration data is used to characterize independently measured noise-free standard object data; Performing feature determination point matching on the multiple sets of detection sensor data, respectively obtaining noise difference data of the multiple sets of feature determination points, performing matching based on the difference data and corresponding quantized data, and obtaining a conversion mapping relationship between the difference data and the environmental noise, wherein the feature determination points are used to characterize fixed objects in the multiple sets of continuous detection sensor data; Acquire real-time detection data, perform matching based on a plurality of the feature determination points, acquire a plurality of matching feature determination points, and read corresponding current detection parameters; Calculating the noise difference data of the feature determination point based on the current detection parameter, and obtaining the current environmental noise data according to the conversion mapping relationship; Performing environmental compensation calculation on the real-time detection data according to the environmental noise data to obtain denoised detection data, wherein the environmental compensation calculation is used to subtract the real-time detection data from the environmental noise data to eliminate the environmental noise portion in the detection data; The step of aligning the collection of the detection sensor data with the initial calibration data to obtain multiple groups of quantized data of the initial environmental noise specifically includes: Collect multiple sets of detection sensor data at preset time nodes, wherein the detection sensor data is a data array used to represent the sensor data array of each effective detection point within the detection range; Acquiring initial calibration data uploaded by the terminal based on the time node, the initial calibration data is used to characterize the standard data of the object to be detected at a certain time node, the standard data is used to characterize the independently measured data without environmental noise, and the initial calibration data correspondingly includes positioning mark and timestamp information; Matching the positioning marks of the initial calibration data with the corresponding detection points of the detection sensor data, and subtracting the sensor data of the corresponding detection points from the initial calibration data to generate multiple sets of quantized data of initial environmental noise; The step of matching the characteristic decision points of the plurality of detection sensor data, respectively obtaining the noise difference data of the plurality of characteristic decision points, matching based on the difference data and the corresponding quantized data, and obtaining the conversion mapping relationship between the difference data and the ambient noise specifically includes: Positioning and superimposing the multiple sets of detection sensor data by detecting point information, so that the effective detection points of the multiple sets of detection sensor data overlap correspondingly in the spatial coordinates; Performing object feature judgment on the area corresponding to the non-initial calibration data of the superimposed multiple sets of detection sensor data, if the object feature corresponding to a certain valid detection point is consistently represented in the multiple sets of detection sensor data, marking the valid detection point as a feature judgment point; Calculate noise difference data of adjacent groups of detection sensor data at the feature determination point based on the time axis direction; The noise difference data corresponding to the same detection sensor data combination is fitted to the quantized data to generate a plurality of mapping functions of the noise difference data to the quantized data, that is, a conversion mapping relationship between the difference data and the ambient noise.

2. The environmental compensation method for detection data processing according to claim 1, characterized in that: It also includes a priority weight allocation step, specifically including: Taking the point to be evaluated as the center, vertical feature evaluation is performed on adjacent feature determination points respectively to assess the priority weights of the feature determination points. The priority weights are used to determine the selection order of the feature determination points when performing environmental compensation calculations; The vertical feature evaluation is used to obtain historical data of multiple feature determination points around the point to be evaluated, and calculate the weight ratio of the corresponding quantized data and noise difference data in the historical data. The smaller the difference between the weight ratio and the value one, the higher the priority weight.

3. The environmental compensation method for detection data processing according to claim 1, characterized in that: It also includes the steps of noise correction, including: Obtain denoised detection data of the same point to be evaluated at multiple sensor ends, perform regression analysis on multiple groups of denoised detection data to obtain corresponding regression values, and calculate regression deviations of multiple sensors relative to the regression values; Obtain regression deviations corresponding to multiple evaluation points on the sensor end, perform mathematical expectation calculation on the multiple regression deviations, and obtain a background noise correction value of the sensor, wherein the background noise correction value is used to optimize the conversion mapping relationship.

4. An environmental compensation system for detection data processing, characterized in that: Include: A data calibration module, used to detect the alignment of the collection of sensor data with initial calibration data to obtain multiple sets of quantitative data of initial environmental noise, wherein the initial calibration data is used to characterize independently measured noise-free standard object data; A detection mapping module, used to match the characteristic decision points of the multiple groups of detection sensor data, respectively obtain the noise difference data of the multiple groups of characteristic decision points, match based on the difference data and the corresponding quantized data, and obtain the conversion mapping relationship between the difference data and the environmental noise, wherein the characteristic decision points are used to characterize the fixed objects in the continuous multiple groups of detection sensor data; A determination and selection module is used to obtain real-time detection data, perform matching based on a number of the feature determination points, obtain a plurality of matching feature determination points, and read the corresponding current detection parameters; A noise evaluation module, used to calculate the noise difference data of the feature determination point based on the current detection parameters, and to obtain the current environmental noise data according to the conversion mapping relationship; A compensation calculation module, used for performing environmental compensation calculation on the real-time detection data according to the environmental noise data to obtain denoised detection data, wherein the environmental compensation calculation is used for subtracting the real-time detection data from the environmental noise data to eliminate the environmental noise part in the detection data; The data calibration module comprises: A data acquisition unit, used to collect multiple sets of detection sensor data at preset time nodes, wherein the detection sensor data is a data array, which is used to represent the sensor data array of each effective detection point within the detection range; A calibration acquisition unit, used for acquiring initial calibration data uploaded by the terminal based on the time node, wherein the initial calibration data is used to characterize the standard data of the object to be detected at a certain time node, and the standard data is used to characterize the independently measured data without environmental noise, and the initial calibration data correspondingly includes positioning mark and timestamp information; A calibration execution unit, used for matching the positioning marks of the initial calibration data with the corresponding detection points of the detection sensor data, and performing a subtraction between the sensor data of the corresponding detection points and the initial calibration data to generate multiple groups of quantized data of initial environmental noise; The detection mapping module includes: An object superposition unit, used for locating and superimposing the multiple groups of detection sensor data by detecting point information, so that the effective detection points of the multiple groups of detection sensor data overlap correspondingly in the spatial coordinates; A superposition selection unit is used to perform object feature judgment on the area corresponding to the non-initial calibration data of the superimposed multiple sets of detection sensor data, and if the object feature corresponding to a certain valid detection point is consistently represented in the multiple sets of detection sensor data, the valid detection point is marked as a feature judgment point; A difference calculation unit, used for calculating noise difference data of adjacent groups of detection sensor data at the feature determination point based on the time axis direction; The fitting mapping unit is used to fit the noise difference data corresponding to the same detection sensor data combination to the quantized data to generate multiple groups of mapping functions of the noise difference data to the quantized data, that is, the conversion mapping relationship between the difference data and the environmental noise.

5. The environmental compensation system for detection data processing according to claim 4, characterized in that: It also includes a priority weight allocation module, specifically including: A vertical evaluation unit, used to perform vertical feature evaluation on adjacent feature determination points with the point to be evaluated as the center, so as to assess the priority weights of the feature determination points, wherein the priority weights are used to determine the selection order of the feature determination points when performing environmental compensation calculations; The weight evaluation unit is used for the vertical feature evaluation to obtain historical data of multiple feature judgment points around the point to be evaluated, and calculate the weight ratio of the corresponding quantized data and noise difference data in the historical data. The smaller the difference between the weight ratio and the value one, the higher the priority weight.

6. The environmental compensation system for detection data processing according to claim 4, characterized in that: It also includes a noise correction module, including: A multi-object evaluation unit, used to obtain denoised detection data of the same point to be evaluated at multiple sensor ends, and perform regression analysis on multiple groups of denoised detection data to obtain corresponding regression values, and calculate regression deviations of multiple sensors relative to the regression values; The multi-sample correction unit is used to obtain the regression deviations corresponding to multiple evaluation points on the sensor end, and perform mathematical expectation calculation on the multiple regression deviations to obtain the background noise correction value of the sensor, and the background noise correction value is used to optimize the conversion mapping relationship.

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