Cave resource assessment method and system based on three-dimensional model

Abstract

The present application relates to the technical field of cave resource evaluation, and a cave resource evaluation method and system based on a three-dimensional model, comprising: obtaining a relative factor interval span set and current cave sampling data of a cave three-dimensional sampling point, performing interval control screening on initial cave sampling data according to an absolute factor control interval set to obtain target cave sampling data, performing correlation analysis on an environmental factor sequence and air negative ion concentration to obtain a correlation coefficient sequence, performing weight adaptability analysis on the correlation coefficient sequence to obtain a weight adaptation value, determining whether the weight adaptation value tends to converge, if not, performing interval contraction on the relative factor interval span set to obtain an iteration factor interval span set, and if so, identifying a similar cave sampling data corresponding similar negative ion concentration set, and performing cave resource evaluation according to the similar negative ion concentration set. The present application can improve the evaluation accuracy of air negative ion resources in a cave.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cave resource assessment, and in particular to a cave resource assessment method and system based on a three-dimensional model. BACKGROUND

[0002] In the field of cave environment monitoring and resource assessment, air negative ion concentration is an important indicator for measuring air quality, assessing cave health care value, and ecological tourism potential. However, the traditional air negative ion capacity evaluation method cannot accurately reflect the overall distribution characteristics of air negative ion concentration in the three-dimensional space of the cave.

[0003] The traditional method relies on manual point sampling, which is difficult to cover complex cave structures (such as dissolution channels, underground rivers, and fracture zones), resulting in sparse data and affecting the accuracy of spatial modeling. Moreover, the traditional air negative ion capacity evaluation method lacks a standardized cave environment database, making it difficult to optimize real-time prediction using historical data. Existing systems also rarely incorporate similarity matching algorithms, making it impossible to dynamically adjust the negative ion prediction model based on environmental factors. Therefore, the current evaluation of air negative ion resources in caves has the problem of low evaluation accuracy. SUMMARY

[0004] The present application provides a cave resource assessment method and system based on a three-dimensional model, which aims to improve the evaluation accuracy of air negative ion resources in caves.

[0005] To achieve the above-mentioned purpose, the present application provides a cave resource assessment method based on a three-dimensional model, which comprises:

[0006] Obtaining initial cave sampling data and an environmental factor sequence, wherein the environmental factor sequence includes positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed, and PM2.5 concentration;

[0007] Extracting cave three-dimensional sampling points in the preset current cave three-dimensional model in sequence, obtaining a relative factor interval span set and current cave sampling data of the cave three-dimensional sampling points, wherein the current cave sampling data includes current positive ion concentration, current temperature, current humidity, current carbon dioxide concentration, current wind speed, and current PM2.5 concentration;

[0008] Determining an absolute factor control interval set according to the relative factor interval span set and the current cave sampling data, wherein the relative factor interval span set refers to a set of relative interval spans of environmental factors;

[0009] Interval control filtering of the initial cave sampling data according to the absolute factor control interval set to obtain target cave sampling data;

[0010] According to the target cave sampling data, the sequence of environmental factors is correlated with the preset air negative ion concentration to obtain a sequence of correlation coefficients;

[0011] The sequence of correlation coefficients is subjected to weight fitness analysis by using the target cave sampling data to obtain a weight fitness value;

[0012] It is judged whether the weight fitness value tends to converge;

[0013] If the weight fitness value does not tend to converge, the interval of the relative factor interval span set is contracted to obtain an iterative factor interval span set, the iterative factor interval span set is used to update the relative factor interval span set, and the step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data is returned;

[0014] If the weight fitness value tends to converge, similar cave sampling data of the current cave sampling data is identified in the target cave sampling data;

[0015] Similar negative ion concentrations corresponding to the similar cave sampling data are identified to obtain a similar negative ion concentration set, and cave resource evaluation is performed according to the similar negative ion concentration set.

[0016] Optionally, the obtaining of the relative factor interval span set and the current cave sampling data of the cave three-dimensional sampling point comprises:

[0017] According to the initial cave sampling data, a sequence of environmental factor sampling intervals is identified, wherein the sequence of environmental factor sampling intervals comprises: a positive ion concentration sampling interval, a temperature sampling interval, a humidity sampling interval, a carbon dioxide concentration sampling interval, a wind speed sampling interval, and a PM2.5 concentration sampling interval;

[0018] The interval span of each environmental factor sampling interval in the sequence of environmental factor sampling intervals is identified to obtain a relative factor interval span set;

[0019] According to the sequence of environmental factors, environmental factor monitoring is performed on the cave three-dimensional sampling point to obtain the current cave sampling data.

[0020] Optionally, the determining of the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data comprises:

[0021] The current cave sampling data is subjected to environmental factor sampling sorting to obtain a sequence of current environmental factor sampling values, wherein the sequence of current environmental factor sampling values comprises: a current positive ion concentration, a current temperature, a current humidity, a current carbon dioxide concentration, a current wind speed, and a current PM2.5 concentration;

[0022] The current environmental factor sampling values are sequentially extracted from the sequence of current environmental factor sampling values;

[0023] identifying, in the relative factor interval span set, a relevant relative interval span corresponding to the current environmental factor sampling value, wherein the relevant relative interval span refers to a relative factor interval span corresponding to an environmental factor to which the current environmental factor sampling value belongs;

[0024] According to the current environmental factor sampling value and the relevant relative interval span, the absolute factor control interval is calculated by the following formula to obtain an absolute factor control interval set:

[0025]

[0026] wherein, represents a minimum value of the absolute factor control interval of the i-th environmental factor, represents a current environmental factor sampling value of the i-th environmental factor, represents the relevant relative interval span of the i-th environmental factor, represents a maximum value of the absolute factor control interval of the i-th environmental factor.

[0027] Optionally, the correlation analysis of the environmental factor sequence and the preset air negative ion concentration according to the target cave sampling data obtains a correlation coefficient sequence, comprising:

[0028] obtaining a target three-dimensional sampling point set of the target cave sampling data;

[0029] extracting target three-dimensional sampling points in the target three-dimensional sampling point set in sequence;

[0030] identifying target three-dimensional sampling data corresponding to the target three-dimensional sampling points in the target cave sampling data to obtain a target three-dimensional sampling data set, wherein the target three-dimensional sampling data comprises: target positive ion concentration, target temperature, target humidity, target carbon dioxide concentration, target wind speed, and target PM2.5 concentration;

[0031] extracting environmental factors in the environmental factor sequence in sequence, and calculating a correlation coefficient of the environmental factor and the air negative ion concentration according to the target three-dimensional sampling data set by the following formula to obtain a correlation coefficient sequence:

[0032]

[0033] wherein, represents a correlation coefficient of the i-th environmental factor and the air negative ion concentration, represents a total number of target three-dimensional sampling points, represents a target environmental factor sampling value of the i-th environmental factor of the j-th target three-dimensional sampling point in the target three-dimensional sampling data set, an average environmental factor sampling value representing an i-th environmental factor in the target three-dimensional sampling data set, a target air negative ion concentration of a j-th target three-dimensional sampling point in the target three-dimensional sampling data set, an average air negative ion concentration in the target three-dimensional sampling data set.

[0034] Optionally, the weight adaptation value is obtained by performing weight adaptation analysis on the correlation coefficient sequence by using the target cave sampling data, and the weight adaptation analysis comprises:

[0035] According to the target three-dimensional sampling data of each target three-dimensional sampling point in the target cave sampling data and the current cave sampling data, an environmental factor difference value is calculated by using the following formula to obtain an environmental factor difference value set:

[0036]

[0037] wherein, an environmental factor difference value of a j-th target three-dimensional sampling point, an absolute value symbol;

[0038] The target air negative ion concentration set is obtained by collecting the target air negative ion concentrations corresponding to the target three-dimensional sampling points.

[0039] According to the environmental factor difference value set and the target air negative ion concentration set, an environmental factor-negative ion concentration scatter point set is plotted.

[0040] The correlation correlation coefficient of the environmental factor-negative ion concentration scatter point set is calculated.

[0041] The minimum environmental factor difference value is identified in the environmental factor difference value set, and the minimum difference three-dimensional sampling point corresponding to the minimum environmental factor difference value is identified.

[0042] The minimum difference air negative ion concentration corresponding to the minimum difference three-dimensional sampling point and the current air negative ion concentration corresponding to the cave three-dimensional sampling point are identified.

[0043] The air negative ion concentration difference value is calculated according to the minimum difference air negative ion concentration and the current air negative ion concentration.

[0044] According to the correlation correlation coefficient and the air negative ion concentration difference value, a weight adaptation value is calculated by using the following formula:

[0045]

[0046] wherein, a weight adaptation value corresponding to a p-th relative factor interval span set, a correlation correlation coefficient corresponding to a p-th relative factor interval span set, This represents the difference in air negative ion concentration corresponding to the p-th relative factor interval span set.

[0047] Optionally, determining whether the weight fit value tends to converge includes:

[0048] Identify the current update count of the relative factor interval span set;

[0049] Determine whether the current update count is greater than a preset update threshold;

[0050] If the current update count is not greater than the update threshold, the weight adaptation value will not converge.

[0051] If the current update count is greater than the update threshold, then the weight adaptation values ​​are aggregated to obtain a weight adaptation value set;

[0052] Identify the update sequence number corresponding to each weight adaptation value in the weight adaptation value set to obtain the update sequence number set;

[0053] Draw an adaptation-sequence scatter set based on the weight adaptation value set and the update sequence set;

[0054] Fit the set of fit-number scatter points to obtain the fit-number fitting curve;

[0055] Identify the slope of the terminal curve of the fit-number fitting curve;

[0056] Determine whether the slope of the terminal curve is greater than a preset terminal slope threshold;

[0057] If the slope of the terminal curve is greater than the terminal slope threshold, the weight adaptation value will not converge.

[0058] If the slope of the terminal curve is not greater than the terminal slope threshold, the weight adaptation value tends to converge.

[0059] Optionally, the step of performing interval shrinking on the relative factor interval span set to obtain the iterative factor interval span set includes:

[0060] Based on the current update count, the iteration factor interval span is calculated using the following formula to obtain the iteration factor interval span set:

[0061]

[0062] in, Indicates completion of the first The iterative factor interval span of the i-th environmental factor in the iterative factor interval span set of the next update. Indicates the current update count. a relative factor interval span of an i-th environmental factor in a relative factor interval span set of the relative factor interval span set that has not been updated, an update proportion coefficient.

[0063] Optionally, the identifying similar cave sampling data of the current cave sampling data in the target cave sampling data comprises:

[0064] identifying minimum difference cave sampling data corresponding to the minimum difference three-dimensional sampling point pair in the target cave sampling data, wherein the minimum difference cave sampling data comprises positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed, and PM2.5 concentration of the minimum difference three-dimensional sampling point.

[0065] taking the minimum difference cave sampling data and the minimum difference air negative ion concentration as the similar cave sampling data.

[0066] Optionally, the cave resource evaluation according to the similar air negative ion concentration set comprises:

[0067] performing model region classification on the current cave three-dimensional model to obtain a feature three-dimensional region set, wherein the feature three-dimensional region set comprises a cave entrance three-dimensional region, a ventilation three-dimensional region, an underground waterfall three-dimensional region, an erosion channel three-dimensional region, a cave hall three-dimensional region, and a fissure zone three-dimensional region.

[0068] identifying a region three-dimensional sampling point set of each feature three-dimensional region in the feature three-dimensional region set;

[0069] identifying a minimum difference air negative ion concentration corresponding to each region three-dimensional sampling point in the region three-dimensional sampling point set;

[0070] constructing a region three-dimensional sampling voxel according to the region three-dimensional sampling point to obtain a region three-dimensional sampling voxel set;

[0071] identifying a voxel volume of the region three-dimensional sampling voxel;

[0072] calculating a unit air negative ion amount of the region three-dimensional sampling voxel according to the minimum difference air negative ion concentration and the voxel volume to obtain a unit air negative ion amount set;

[0073] calculating a region air negative ion total amount of the feature three-dimensional region according to the unit air negative ion amount set to obtain a region air negative ion total amount set;

[0074] performing region air negative ion total amount matching in the initial cave sampling data according to the region air negative ion total amount set to obtain a region feature approximate cave;

[0075] Identify the total amount of cave air negative ions corresponding to the cave feature approximate cave, and take the total amount of cave air negative ions as the cave resource evaluation amount of the current cave corresponding to the current cave three-dimensional model.

[0076] To achieve the above object, the application further provides a cave resource evaluation system based on a three-dimensional model, comprising:

[0077] An absolute factor control interval determination module is configured to obtain initial cave sampling data and an environmental factor sequence, wherein the environmental factor sequence comprises positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed, and PM2.5 concentration; and in a preset current cave three-dimensional model, three-dimensional cave sampling points are extracted in sequence to obtain a relative factor interval span set and current cave sampling data of the three-dimensional cave sampling points, wherein the current cave sampling data comprises current positive ion concentration, current temperature, current humidity, current carbon dioxide concentration, current wind speed, and current PM2.5 concentration; and an absolute factor control interval set is determined according to the relative factor interval span set and the current cave sampling data, wherein the relative factor interval span set refers to a set of relative interval spans of environmental factors.

[0078] A sampling data interval control screening module is configured to perform interval control screening on the initial cave sampling data according to the absolute factor control interval set to obtain target cave sampling data; perform correlation analysis on the environmental factor sequence and a preset air negative ion concentration according to the target cave sampling data to obtain a correlation coefficient sequence; perform weight adaptability analysis on the correlation coefficient sequence using the target cave sampling data to obtain a weight adaptation value; determine whether the weight adaptation value converges; if the weight adaptation value does not converge, perform interval contraction on the relative factor interval span set to obtain an iterative factor interval span set, update the relative factor interval span set using the iterative factor interval span set, and return to the step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data;

[0079] A similar cave sampling data identification module is configured to identify similar cave sampling data of the current cave sampling data in the target cave sampling data if the weight adaptation value converges.

[0080] A cave resource evaluation module is configured to identify similar negative ion concentrations corresponding to the similar cave sampling data to obtain a similar negative ion concentration set, and perform cave resource evaluation according to the similar negative ion concentration set.

[0081] To solve the above problems, the application further provides an electronic device, comprising:

[0082] A memory is configured to store at least one instruction; and a processor is configured to execute the instruction stored in the memory to implement the cave resource evaluation method based on a three-dimensional model.

[0083] To solve the above problems, the application further provides a computer readable storage medium, wherein at least one instruction is stored in the computer readable storage medium, and the at least one instruction is executed by a processor in an electronic device to implement the above-mentioned cave resource evaluation method based on a three-dimensional model.

[0084] Beneficial effects: to solve the problems described in the background art, first, the initial cave sampling data is obtained, then the initial cave sampling data is screened by interval control, and finally the similar cave sampling data of the cave three-dimensional sampling point is identified in the target cave sampling data after the interval control screening, thereby realizing the purpose of evaluating the cave resources by referring to the similar cave sampling data. In the process of interval control screening, first, the initial cave sampling data and the environmental factor sequence need to be obtained, wherein the environmental factor sequence includes positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed and PM2.5 concentration. Then, in order to independently analyze the cave three-dimensional sampling point, the cave three-dimensional sampling point needs to be extracted in the current cave three-dimensional model in sequence. In order to screen the initial cave sampling data by interval control, the relative factor interval span set needs to be obtained first, and then the absolute factor control interval set is determined according to the relative factor interval span set and the current cave sampling data, wherein the relative factor interval span set refers to the relative interval span set of the environmental factors. At this time, the initial cave sampling data can be screened by interval control according to the absolute factor control interval set, and the target cave sampling data is obtained. In order to judge the adaptability of the relative factor interval span, the correlation analysis of the environmental factor sequence and the preset air negative ion concentration is carried out according to the target cave sampling data, and the correlation coefficient sequence is obtained. Then, the weight adaptability of the correlation coefficient sequence is analyzed by using the target cave sampling data, and the weight adaptation value is obtained. Since the smaller the relative factor interval span is, the higher the similarity between the target cave sampling data and the current cave sampling data is, therefore, the weight adaptation value gradually increases with the contraction of the relative factor interval span set, and the adaptability of the correlation coefficient sequence also gradually increases and gradually tends to a certain value. Therefore, it can be judged whether the weight adaptation value converges or not. If the weight adaptation value does not converge, the relative factor interval span set is shrunk, and the iterative factor interval span set is obtained, and the relative factor interval span set is updated by using the iterative factor interval span set. At this time, the step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data needs to be returned. If the weight adaptation value converges, the similar cave sampling data of the current cave sampling data can be identified in the target cave sampling data. When the similar cave sampling data corresponding to the cave three-dimensional sampling point is obtained, the similar negative ion concentration corresponding to the similar cave sampling data can be identified, and the similar negative ion concentration set is obtained. Finally, the cave resource evaluation is carried out according to the similar negative ion concentration set. Therefore, the evaluation accuracy of the air negative ion resources in the cave can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0085] Figure 1 The flowchart of the cave resource evaluation method based on the three-dimensional model provided by an embodiment of the present application;

[0086] Figure 2A functional module diagram of a cave resource evaluation system based on a three-dimensional model provided by an embodiment of the present application is shown in the figure.

[0087] Figure 3 A structural schematic diagram of an electronic device for implementing the cave resource evaluation method based on a three-dimensional model provided by an embodiment of the present application is shown in the figure.

[0088] Legend of reference signs:

[0089] 1, electronic device; 10, processor; 11, memory; 12, bus.

[0090] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0091] It should be understood that the specific embodiments described herein are merely intended to explain the present application and not to limit the present application.

[0092] An embodiment of the present application provides a cave resource evaluation method based on a three-dimensional model. The execution subject of the cave resource evaluation method based on a three-dimensional model includes but is not limited to at least one of electronic devices such as a server and a terminal which can be configured to execute the method provided by the embodiment of the present application. In other words, the cave resource evaluation method based on a three-dimensional model can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0093] Reference Figure 1 A flowchart of a cave resource evaluation method based on a three-dimensional model provided by an embodiment of the present application is shown in the figure. In the embodiment, the cave resource evaluation method based on a three-dimensional model includes:

[0094] S1, obtaining initial cave sampling data and environmental factor sequence.

[0095] The initial cave sampling data is interpretable, which refers to environmental factor data and cave resource data about each cave obtained after data collection on multiple caves. The environmental factor data can be monitoring data of each regional three-dimensional sampling point about environmental factors in a preset characteristic three-dimensional region, and the cave resource data can be data such as total amount of air negative ion concentration of the cave and air negative ion concentration of each regional three-dimensional sampling point. The characteristic three-dimensional region can be a preset cave characteristic region, for example, a cave entrance three-dimensional region, a ventilation three-dimensional region, an underground waterfall three-dimensional region, a dissolution channel three-dimensional region, a cave hall three-dimensional region, a fissure zone three-dimensional region, etc. The regional three-dimensional sampling point refers to a sampling point in the characteristic three-dimensional region, which will be described in detail in the following embodiments.

[0096] In detail, the environmental factor sequence comprises positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed, and PM2.5 concentration.

[0097] S2, extracting cave three-dimensional sampling points in a preset current cave three-dimensional model in sequence to obtain a relative factor interval span set and current cave sampling data of the cave three-dimensional sampling points.

[0098] Understandably, the current cave three-dimensional model refers to a spatial three-dimensional model of a current cave to be evaluated, and the cave three-dimensional sampling point refers to a regional three-dimensional sampling point of each characteristic three-dimensional region in the current cave three-dimensional model. The relative factor interval span set refers to a set of data screening relative interval spans of each environmental factor. For example, when the environmental factor is the current temperature, the interval about the temperature in the relative factor interval span set can be [Tmin, Tmax], and the relative factor interval span of the temperature is Tspan = Tmax-Tmin. ] when the environmental factor is the temperature.

[0099] In detail, the current cave sampling data comprises current positive ion concentration, current temperature, current humidity, current carbon dioxide concentration, current wind speed, and current PM2.5 concentration.

[0100] In the embodiment of the application, the obtaining of the relative factor interval span set and the current cave sampling data of the cave three-dimensional sampling points comprises:

[0101] identifying an environmental factor sampling interval sequence according to the initial cave sampling data, wherein the environmental factor sampling interval sequence comprises positive ion concentration sampling interval, temperature sampling interval, humidity sampling interval, carbon dioxide concentration sampling interval, wind speed sampling interval, and PM2.5 concentration sampling interval;

[0102] identifying an interval span of each environmental factor sampling interval in the environmental factor sampling interval sequence to obtain a relative factor interval span set;

[0103] monitoring environmental factors of the cave three-dimensional sampling points according to the environmental factor sequence to obtain current cave sampling data.

[0104] Further, the environmental factor sampling interval sequence refers to a sampling range about an environmental factor in the initial cave sampling data. For example, the maximum temperature in the initial cave sampling data is Tmax, and the minimum temperature is Tmin, and when the environmental factor is the temperature, the environmental factor sampling interval is [Tmin, Tmax]. The environmental factor sampling interval sequence refers to a sequence composed of each environmental factor sampling interval. The interval span refers to a span of the environmental factor sampling interval.​​​​​

[0105] S3, determining an absolute factor control interval set according to the relative factor interval span set and the current cave sampling data.

[0106] In detail, the relative factor interval span set refers to a set of relative interval spans of environmental factors.

[0107] Further, the absolute factor control interval set refers to a set of absolute control intervals of each environmental factor, for example, when the environmental factor is temperature, and the corresponding relative factor interval span is , the absolute factor control interval can be , ], [ , ], [ , ], etc.

[0108] In the embodiment of the application, the absolute factor control interval set is determined according to the relative factor interval span set and the current cave sampling data, comprising:

[0109] The current cave sampling data is subjected to environmental factor sampling sequencing to obtain a current environmental factor sampling value sequence, wherein the current environmental factor sampling value sequence is: current positive ion concentration, current temperature, current humidity, current carbon dioxide concentration, current wind speed, and current PM2.5 concentration.

[0110] The current environmental factor sampling value is extracted from the current environmental factor sampling value sequence in sequence.

[0111] The current environmental factor sampling value is identified in the relative factor interval span set to obtain an associated relative interval span.

[0112] According to the current environmental factor sampling value and the associated relative interval span, the absolute factor control interval is calculated by using the following formula to obtain the absolute factor control interval set:

[0113]

[0114] wherein, represents the minimum value of the absolute factor control interval of the i-th environmental factor, represents the current environmental factor sampling value of the i-th environmental factor, represents the associated relative interval span of the i-th environmental factor, represents the maximum value of the absolute factor control interval of the i-th environmental factor.

[0115] Further, the current environmental factor sampling value sequence refers to a sequence composed of sampling values of respective environmental factors.

[0116] S4, interval control screening of the initial cave sampling data according to the absolute factor control interval set, to obtain target cave sampling data.

[0117] Further, the interval control screening refers to screening sampling data meeting the absolute factor control interval set from the initial cave sampling data.

[0118] For example, when the sampling data about temperature in the initial cave sampling data is: 、 、 、 、 , and the absolute factor control interval of temperature in the absolute factor control interval set is: , , the temperature in the target cave sampling data can be: 、 、 、 . The data screening process of other environmental factors is the same, and will not be repeated here.

[0119] S5, correlation analysis of the environmental factor sequence and the preset air negative ion concentration according to the target cave sampling data, to obtain a correlation coefficient sequence.

[0120] It can be understood that the correlation analysis refers to analyzing the correlation between the environmental factors and the air negative ion concentration, which can be Pearson correlation analysis. The correlation coefficient sequence refers to a sequence composed of correlation coefficients of respective environmental factors and the air negative ion concentration.

[0121] In the embodiment of the application, the correlation analysis of the environmental factor sequence and the preset air negative ion concentration according to the target cave sampling data, to obtain a correlation coefficient sequence, includes:

[0122] Obtaining a target three-dimensional sampling point set of the target cave sampling data;

[0123] Extracting target three-dimensional sampling points in the target three-dimensional sampling point set in sequence;

[0124] Identifying target three-dimensional sampling data corresponding to the target three-dimensional sampling points in the target cave sampling data, to obtain a target three-dimensional sampling data set, wherein the target three-dimensional sampling data includes: target positive ion concentration, target temperature, target humidity, target carbon dioxide concentration, target wind speed, and target PM2.5 concentration.

[0125] The environmental factors are extracted in sequence in the environmental factor sequence, and the correlation coefficient of the environmental factor and the concentration of the negative air ion is calculated according to the target three-dimensional sampling data set by using the following formula to obtain a correlation coefficient sequence:

[0126]

[0127] wherein, represents the correlation coefficient of the i-th environmental factor and the concentration of the negative air ion, represents the total number of the target three-dimensional sampling points, represents the target environmental factor sampling value of the i-th environmental factor of the j-th target three-dimensional sampling point in the target three-dimensional sampling data set, represents the average environmental factor sampling value of the i-th environmental factor in the target three-dimensional sampling data set, represents the target concentration of the negative air ion of the j-th target three-dimensional sampling point in the target three-dimensional sampling data set, represents the average concentration of the negative air ion in the target three-dimensional sampling data set.

[0128] In detail, the target environmental factor sampling value and the average environmental factor sampling value may be equal, and when this situation occurs, the effective number of digits of the target environmental factor sampling value and the average environmental factor sampling value can be adjusted, for example, when calculating the correlation coefficient for the first time, the effective number of digits of the target environmental factor sampling value and the average environmental factor sampling value can be kept to one digit after the decimal point, and when the target environmental factor sampling value and the average environmental factor sampling value are equal, the effective number of digits of the target environmental factor sampling value and the average environmental factor sampling value can be kept to two digits after the decimal point, and so on. When the target concentration of the negative air ion and the average concentration of the negative air ion are equal, the processing method is the same, which will not be described here.

[0129] Further, the target three-dimensional sampling point set refers to a set of cave three-dimensional sampling points recorded in the target cave sampling data. The target three-dimensional sampling data refers to the sampling data of each environmental factor of the target three-dimensional sampling point.

[0130] S6, performing weight adaptation analysis on the correlation coefficient sequence by using the target cave sampling data to obtain a weight adaptation value.

[0131] Further, the weight adaptation value refers to the adaptation degree of the correlation coefficient as a matching calculation weight of the corresponding environmental factor, which will be described in the following embodiments.

[0132] In the embodiments of the present application, the weight adaptation value is obtained by performing weight adaptation analysis on the correlation coefficient sequence by using the target cave sampling data, which comprises:

[0133] According to the target three-dimensional sampling data of each target three-dimensional sampling point in the target cave sampling data and the current cave sampling data, an environmental factor difference value is calculated by using the following formula to obtain an environmental factor difference value set:

[0134]

[0135] wherein, represents the environmental factor difference value of the jth target three-dimensional sampling point, represents an absolute value symbol;

[0136] The target air negative ion concentration corresponding to the target three-dimensional sampling point is collected to obtain a target air negative ion concentration set.

[0137] According to the environmental factor difference value set and the target air negative ion concentration set, an environmental factor-negative ion concentration scatter point set is drawn.

[0138] The correlation correlation coefficient of the environmental factor-negative ion concentration scatter point set is calculated.

[0139] In the environmental factor difference value set, the minimum environmental factor difference value is identified, and the minimum difference three-dimensional sampling point corresponding to the minimum environmental factor difference value is identified.

[0140] The minimum difference air negative ion concentration corresponding to the minimum difference three-dimensional sampling point and the current air negative ion concentration corresponding to the cave three-dimensional sampling point are identified.

[0141] According to the minimum difference air negative ion concentration and the current air negative ion concentration, an air negative ion concentration difference value is calculated.

[0142] According to the correlation correlation coefficient and the air negative ion concentration difference value, a weight adaptation value is calculated by using the following formula:

[0143]

[0144] wherein, represents the weight adaptation value corresponding to the pth relative factor interval span set, represents the correlation correlation coefficient corresponding to the pth relative factor interval span set, represents the air negative ion concentration difference value corresponding to the pth relative factor interval span set.

[0145] The environmental factor difference value can be understood as a weighted difference degree of the environmental factor of the target three-dimensional sampling point and the cave three-dimensional sampling point. The target air negative ion concentration refers to the air negative ion concentration of the target three-dimensional sampling point. The target air negative ion concentration set refers to a collection of target air negative ion concentrations of respective target three-dimensional sampling points. The environmental factor-negative ion concentration scatter point set refers to a collection of coordinate points determined according to the correspondence between the environmental factor difference value and the target air negative ion concentration. The environmental factor-negative ion concentration scatter point set is plotted in a coordinate system with the environmental factor difference value as the horizontal coordinate and the target air negative ion concentration as the vertical coordinate. The correlation correlation coefficient refers to a coefficient representing the correlation degree of the environmental factor difference value and the air negative ion concentration calculated according to the environmental factor-negative ion concentration scatter point set. The minimum difference three-dimensional sampling point refers to the target three-dimensional sampling point corresponding to the minimum environmental factor difference value. The minimum difference air negative ion concentration refers to the air negative ion concentration of the minimum difference three-dimensional sampling point. The current air negative ion concentration refers to the air negative ion concentration of the cave three-dimensional sampling point. The air negative ion concentration difference value refers to the concentration difference between the minimum difference air negative ion concentration and the current air negative ion concentration.

[0146] In detail, when the air negative ion concentration difference value is 0, the effective number of bits of the minimum difference air negative ion concentration and the current air negative ion concentration can be adjusted. The adjustment manner is the same as that when the target environmental factor sampling value and the average environmental factor sampling value are equal, which will not be described here.

[0147] S7, judging whether the weight adaptation value tends to converge.

[0148] It can be understood that when the weight adaptation value tends to converge, it means that the weight adaptation value tends to be stable.

[0149] In the embodiment of the application, the judgment of whether the weight adaptation value tends to converge comprises:

[0150] identifying a current update number of the relative factor interval span set;

[0151] judging whether the current update number is greater than a preset update threshold;

[0152] if the current update number is not greater than the update threshold, the weight adaptation value does not tend to converge;

[0153] if the current update number is greater than the update threshold, the weight adaptation values are collected to obtain a weight adaptation value set;

[0154] identifying an update serial number corresponding to each weight adaptation value in the weight adaptation value set to obtain an update serial number set;

[0155] drawing an adaptation-number scatter point set according to the weight adaptation value set and the update serial number set;

[0156] fitting the adaptation-number scatter point set to obtain an adaptation-number fitting curve;

[0157] identifying a terminal curve slope of the adaptation-number fitting curve;

[0158] judging whether the terminal curve slope is greater than a preset terminal slope threshold value;

[0159] if the terminal curve slope is greater than the terminal slope threshold value, the weight adaptation value does not tend to converge;

[0160] if the terminal curve slope is not greater than the terminal slope threshold value, the weight adaptation value tends to converge.

[0161] Understandably, the current update number refers to the number of times of the current update of the relative factor interval span set. The update threshold refers to a preset minimum update number for convergence judgment, for example, 5. The weight adaptation value set refers to a weight adaptation value set corresponding to the relative factor interval span set after each update. The update serial number refers to the update order of the relative factor interval span set corresponding to the weight adaptation value. The update serial number set refers to a set of update serial numbers corresponding to each weight adaptation value. The adaptation-number scatter point set refers to a set of coordinate points determined according to the correspondence between the weight adaptation value and the update serial number. The adaptation-number scatter point set can be obtained by dotting in a coordinate system with the update serial number as the horizontal coordinate and the weight adaptation value as the vertical coordinate. The adaptation-number fitting curve refers to a curve fitted according to the adaptation-number scatter point set. The terminal curve slope refers to the slope of the terminal end point of the adaptation-number fitting curve. The terminal slope threshold value refers to a preset threshold value for judging whether the weight adaptation value converges, which can be 0.1.

[0162] If the weight adaptation value does not tend to converge, S8 is executed, the relative factor interval span set is interval-contracted to obtain an iteration factor interval span set, and the relative factor interval span set is updated by using the iteration factor interval span set.

[0163] Understandably, the interval contraction refers to compression of each relative factor interval span in the relative factor interval span set. The iteration factor interval span set refers to the relative factor interval span set after the interval contraction.

[0164] In the embodiment of the application, the interval contraction of the relative factor interval span set to obtain the iteration factor interval span set comprises:

[0165] According to the current update number, the iteration factor interval span is calculated by using the following formula to obtain the iteration factor interval span set:

[0166]

[0167] wherein, denotes the iteration factor interval span of the i-th environmental factor in the iteration factor interval span set of the completion of the n-th update, denotes the current update number, denotes the relative factor interval span of the i-th environmental factor in the relative factor interval span set of the non-updated, denotes the update proportion coefficient.

[0168] It can be understood that when the relative factor interval span of the relative factor of the temperature which is not updated is , the update proportion coefficient is 0.5, the relative factor interval span of the temperature which is completed the first update is , the relative factor interval span of the temperature which is completed the second update is , and so on.

[0169] Return to the above step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data.

[0170] If the weight adaptation value converges, S9 is executed, and similar cave sampling data of the current cave sampling data is identified in the target cave sampling data.

[0171] It can be understood that the similar cave sampling data refers to the sampling data in the target cave sampling data which is most similar to the sampling environment of the current cave sampling data.

[0172] In the embodiment of the application, the similar cave sampling data of the current cave sampling data is identified in the target cave sampling data, comprising:

[0173] The minimum difference cave sampling data corresponding to the minimum difference three-dimensional sampling point is identified in the target cave sampling data, wherein the minimum difference cave sampling data comprises: the positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed and PM2.5 concentration of the minimum difference three-dimensional sampling point.

[0174] The minimum difference cave sampling data and the minimum difference air negative ion concentration are taken as the similar cave sampling data.

[0175] It can be understood that the minimum difference cave sampling data refers to the sampling environment data corresponding to the minimum difference three-dimensional sampling point.

[0176] S10, the similar negative ion concentration corresponding to the similar cave sampling data is identified, a similar negative ion concentration set is obtained, and cave resource evaluation is performed according to the similar negative ion concentration set. ​

[0177] The similar negative ion concentration refers to the air negative ion concentration of the minimum difference three-dimensional sampling point corresponding to the similar cave sampling data. The similar negative ion concentration set refers to a set of similar negative ion concentrations corresponding to each cave three-dimensional sampling point.

[0178] In the embodiments of the present application, the cave resource evaluation according to the similar negative ion concentration set comprises:

[0179] The current cave three-dimensional model is subjected to model region classification to obtain a feature three-dimensional region set, wherein the feature three-dimensional region set comprises a cave mouth three-dimensional region, a ventilation place three-dimensional region, an underground waterfall three-dimensional region, an erosion channel three-dimensional region, a cave hall three-dimensional region, and a fissure zone three-dimensional region.

[0180] The region three-dimensional sampling point set of each feature three-dimensional region in the feature three-dimensional region set is identified.

[0181] The minimum difference air negative ion concentration corresponding to each region three-dimensional sampling point in the region three-dimensional sampling point set is identified.

[0182] A region three-dimensional sampling voxel is constructed according to the region three-dimensional sampling point to obtain a region three-dimensional sampling voxel set.

[0183] The voxel volume of the region three-dimensional sampling voxel is identified.

[0184] The unit air negative ion amount of the region three-dimensional sampling voxel is calculated according to the minimum difference air negative ion concentration and the voxel volume to obtain a unit air negative ion amount set.

[0185] The region negative ion total amount of the feature three-dimensional region is calculated according to the unit air negative ion amount set to obtain a region negative ion total amount set.

[0186] The region negative ion total amount set is subjected to region negative ion total amount matching in the initial cave sampling data to obtain a region feature approximate cave.

[0187] The cave air negative ion total amount corresponding to the region feature approximate cave is identified, and the cave air negative ion total amount is taken as the cave resource evaluation amount of the current cave three-dimensional model corresponding to the current cave to be evaluated.

[0188] The feature three-dimensional region set refers to a preset cave space region set. The cave mouth three-dimensional region can be a space three-dimensional region of 1 cubic meter at a distance of 1 m from the cave mouth in the cave. The ventilation place three-dimensional region, the underground waterfall three-dimensional region, the erosion channel three-dimensional region, the cave hall three-dimensional region, and the fissure zone three-dimensional region can be set according to actual conditions, and details are not described herein.

[0189] Further, the regional three-dimensional sampling point set refers to a set composed of cave three-dimensional sampling points in the feature three-dimensional region. The regional three-dimensional sampling voxel refers to a volume unit, for example, 0.1 cubic meters, in the feature three-dimensional region. The voxel volume refers to the volume of the regional three-dimensional sampling voxel. The unit air negative ion amount refers to the estimated amount of air negative ions in the regional three-dimensional sampling voxel. The unit air negative ion amount set refers to a set of unit air negative ion amounts corresponding to respective regional three-dimensional sampling voxels. The regional total air negative ion amount refers to the total amount of air negative ions in the feature three-dimensional region. The regional total air negative ion amount set refers to a set of regional total air negative ion amounts corresponding to respective feature three-dimensional regions. The regional feature approximate cave refers to a cave in the initial cave sampling data that is most similar to the regional total air negative ion amount set in each feature three-dimensional region. The cave total air negative ion amount refers to the total amount of air negative ions in the regional feature approximate cave, which is recorded in the initial cave sampling data. The cave resource evaluation amount refers to the evaluation value of the air negative ion amount of the current cave to be evaluated.

[0190] Further, the embodiment of the present application identifies similar cave sampling data of the current cave sampling data in the target cave sampling data, then identifies similar negative ion concentrations corresponding to the similar cave sampling data, obtains a similar negative ion concentration set, and finally performs cave resource evaluation according to the similar negative ion concentration set. The current cave to be evaluated is not completely equal to the cave total air negative ion amount corresponding to the regional feature approximate cave, so there is a certain error. The wider the cave and the smoother the wall of the cave, the smaller the error, and the higher the reference value of the cave total air negative ion amount corresponding to the regional feature approximate cave.

[0191] The present application is to solve the problems described in the background art, first, the initial cave sampling data is obtained, then the initial cave sampling data is screened by interval control, and finally the similar cave sampling data of the cave three-dimensional sampling point is identified in the target cave sampling data after the interval control screening, so as to realize the purpose of evaluating the cave resources by referring to the similar cave sampling data. In the process of interval control screening, first, the initial cave sampling data and the environmental factor sequence need to be obtained, wherein the environmental factor sequence includes positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed and PM2.5 concentration. Then, in order to independently analyze the cave three-dimensional sampling point, the cave three-dimensional sampling point needs to be extracted in the current cave three-dimensional model in sequence. In order to screen the initial cave sampling data by interval control, the relative factor interval span set needs to be obtained first, and then the absolute factor control interval set is determined according to the relative factor interval span set and the current cave sampling data, wherein the relative factor interval span set refers to the relative interval span set of the environmental factor. At this time, the initial cave sampling data can be screened by interval control according to the absolute factor control interval set, and the target cave sampling data is obtained. In order to judge the adaptability of the relative factor interval span, the correlation analysis of the environmental factor sequence and the preset air negative ion concentration is carried out according to the target cave sampling data, and the correlation coefficient sequence is obtained. Then, the weight adaptability of the correlation coefficient sequence is analyzed by using the target cave sampling data, and the weight adaptation value is obtained. Since the smaller the relative factor interval span is, the higher the similarity between the target cave sampling data and the current cave sampling data is, therefore, the weight adaptation value gradually increases with the contraction of the relative factor interval span set, and the adaptability of the correlation coefficient sequence also gradually increases and gradually tends to a certain value. Therefore, it can be judged whether the weight adaptation value converges or not. If the weight adaptation value does not converge, the relative factor interval span set is shrunk, and the iterative factor interval span set is obtained, and the relative factor interval span set is updated by using the iterative factor interval span set. At this time, the step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data needs to be returned. If the weight adaptation value converges, the similar cave sampling data of the current cave sampling data can be identified in the target cave sampling data. When the similar cave sampling data corresponding to the cave three-dimensional sampling point is obtained, the similar negative ion concentration corresponding to the similar cave sampling data can be identified, and the similar negative ion concentration set is obtained. Finally, the cave resource evaluation is carried out according to the similar negative ion concentration set. Therefore, the evaluation accuracy of the air negative ion resources in the cave can be improved.

[0192] As Figure 2 shown, it is a functional module diagram of the cave resource evaluation system based on the three-dimensional model provided by an embodiment of the present application.

[0193] The three-dimensional model-based cave resource evaluation system 100 can be installed in an electronic device. According to the functions implemented, the three-dimensional model-based cave resource evaluation system 100 can include an absolute factor control interval determination module 101, a sampling data interval control screening module 102, a similar cave sampling data identification module 103, and a cave resource evaluation module 104. The modules of the present application can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete a fixed function, which are stored in the memory of the electronic device.

[0194] The absolute factor control interval determination module 101 is configured to obtain initial cave sampling data and an environmental factor sequence, wherein the environmental factor sequence includes positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed, and PM2.5 concentration; sequentially extract cave three-dimensional sampling points in a preset current cave three-dimensional model to obtain a relative factor interval span set and current cave sampling data of the cave three-dimensional sampling points, wherein the current cave sampling data includes current positive ion concentration, current temperature, current humidity, current carbon dioxide concentration, current wind speed, and current PM2.5 concentration; and determine an absolute factor control interval set according to the relative factor interval span set and the current cave sampling data, wherein the relative factor interval span set refers to a set of relative interval spans of environmental factors.

[0195] The sampling data interval control screening module 102 is configured to perform interval control screening on the initial cave sampling data according to the absolute factor control interval set to obtain target cave sampling data; perform correlation analysis on the environmental factor sequence and a preset air negative ion concentration according to the target cave sampling data to obtain a correlation coefficient sequence; perform weight adaptability analysis on the correlation coefficient sequence using the target cave sampling data to obtain a weight adaptation value; determine whether the weight adaptation value converges; if the weight adaptation value does not converge, perform interval contraction on the relative factor interval span set to obtain an iterative factor interval span set, update the relative factor interval span set using the iterative factor interval span set, and return to the step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data.

[0196] The similar cave sampling data identification module 103 is configured to identify similar cave sampling data of the current cave sampling data in the target cave sampling data if the weight adaptation value converges.

[0197] The cave resource evaluation module 104 is configured to identify similar negative ion concentrations corresponding to the similar cave sampling data to obtain a similar negative ion concentration set, and perform cave resource evaluation according to the similar negative ion concentration set.

[0198] In detail, the modules in the three-dimensional model based cave resource assessment system 100 in the embodiments of the present application adopt the same technical means as the three-dimensional model based cave resource assessment method in the above Figure 1 and can produce the same technical effects, which will not be described here again.

[0199] As shown in Figure 3 , it is a structural schematic diagram of an electronic device for implementing the three-dimensional model based cave resource assessment method according to an embodiment of the present application.

[0200] The electronic device 1 can include a processor 10, a memory 11 and a bus 12, and can further include a computer program stored in the memory 11 and executable on the processor 10, such as a three-dimensional model based cave resource assessment method program.

[0201] The memory 11 includes at least one type of readable storage medium, such as a flash memory, a mobile hard disk, a multimedia card, a card type memory (such as an SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Further, the memory 11 includes both an internal storage unit and an external storage device of the electronic device 1. The memory 11 can be used not only to store application software and various data installed in the electronic device 1, such as the code of the three-dimensional model based cave resource assessment method program, but also to temporarily store data that has been output or will be output.

[0202] The processor 10 can be composed of integrated circuits in some embodiments, for example, can be composed of a single packaged integrated circuit, or can be composed of multiple packaged integrated circuits with the same function or different functions, including one or more central processing units (CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, which connects various components of the entire electronic device through various interfaces and lines, executes programs or modules stored in the memory 11 (such as a three-dimensional model-based cave resource evaluation method program, etc.), and calls data stored in the memory 11 to perform various functions and process data of the electronic device 1.

[0203] The bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 can be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize the connection and communication between the memory 11, the at least one processor 10, etc.

[0204] Figure 3 Only the electronic device with components is shown, and those skilled in the art can understand that, Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and can include fewer or more components than shown, or combine certain components, or different component arrangements.

[0205] For example, although not shown, the electronic device 1 can also include a power supply (such as a battery) for powering various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, so as to realize functions such as charge management, discharge management, and power consumption management through the power management device. The power supply can also include one or more direct current or alternating current power supplies, recharging devices, power supply fault detection circuits, power supply converters or inverters, power supply status indicators, etc. The electronic device 1 can also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which are not described here.

[0206] Further, the electronic device 1 can also include a network interface, which can optionally include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), and is usually used to establish a communication connection between the electronic device 1 and other electronic devices.

[0207] Optionally, the electronic device 1 can also include a user interface, which can be a display, an input unit such as a keyboard, and optionally a standard wired interface, a wireless interface. Optionally, in some embodiments, the display can be an LED display, a liquid crystal display, a touch liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, etc. The display can also be appropriately referred to as a display screen or a display unit, for displaying information processed in the electronic device 1 and for displaying a visualized user interface.

[0208] The three-dimensional model-based cave resource evaluation method program stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, which, when running in the processor 10, can realize:

[0209] Obtaining initial cave sampling data and an environmental factor sequence, wherein the environmental factor sequence includes positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed, and PM2.5 concentration;

[0210] Extracting cave three-dimensional sampling points in a preset current cave three-dimensional model in sequence, obtaining a relative factor interval span set and current cave sampling data of the cave three-dimensional sampling points, wherein the current cave sampling data includes current positive ion concentration, current temperature, current humidity, current carbon dioxide concentration, current wind speed, and current PM2.5 concentration;

[0211] Determining an absolute factor control interval set according to the relative factor interval span set and the current cave sampling data, wherein the relative factor interval span set refers to a set of relative interval spans of environmental factors;

[0212] Interval control screening of the initial cave sampling data according to the absolute factor control interval set to obtain target cave sampling data;

[0213] Correlation analysis of the environmental factor sequence and the preset air negative ion concentration according to the target cave sampling data to obtain a correlation coefficient sequence;

[0214] Weight adaptability analysis of the correlation coefficient sequence using the target cave sampling data to obtain a weight adaptation value;

[0215] Judging whether the weight adaptation value tends to converge;

[0216] If the weight adaptation value does not tend to converge, interval contraction is performed on the relative factor interval span set to obtain an iteration factor interval span set, the iteration factor interval span set is used to update the relative factor interval span set, and the step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data is returned.

[0217] If the weight adaptation value tends to converge, similar cave sampling data of the current cave sampling data is identified in the target cave sampling data.

[0218] The similar negative ion concentration corresponding to the similar cave sampling data is identified to obtain a similar negative ion concentration set, and cave resource evaluation is performed according to the similar negative ion concentration set.

[0219] Specifically, the specific implementation method of the processor 10 to the above instructions can refer to Figures 1 to 3 The description of related steps in the corresponding embodiments will not be repeated here.

[0220] Further, the modules / units integrated in the electronic device 1 are implemented in the form of software function units and sold or used as independent products, which can be stored in a computer readable storage medium. The computer readable storage medium can be volatile or non-volatile. For example, the computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory).

[0221] The application also provides a computer readable storage medium, which stores a computer program, and the computer program can realize the following steps when executed by a processor of an electronic device:

[0222] Obtain initial cave sampling data and an environmental factor sequence, wherein the environmental factor sequence includes positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed, and PM2.5 concentration.

[0223] In a preset current cave three-dimensional model, extract cave three-dimensional sampling points in sequence to obtain a relative factor interval span set and current cave sampling data of the cave three-dimensional sampling points, wherein the current cave sampling data includes current positive ion concentration, current temperature, current humidity, current carbon dioxide concentration, current wind speed, and current PM2.5 concentration.

[0224] Determine an absolute factor control interval set according to the relative factor interval span set and the current cave sampling data, wherein the relative factor interval span set refers to a relative interval span set of environmental factors.

[0225] According to the absolute factor control interval set, the initial cave sampling data is controlled and screened in an interval, and target cave sampling data is obtained;

[0226] According to the target cave sampling data, correlation analysis is performed on the environmental factor sequence and the preset air negative ion concentration, and a correlation coefficient sequence is obtained.

[0227] The target cave sampling data is used for weight adaptability analysis on the correlation coefficient sequence, and a weight adaptation value is obtained.

[0228] It is judged whether the weight adaptation value tends to converge.

[0229] If the weight adaptation value does not tend to converge, the relative factor interval span set is shrunk in an interval, an iterative factor interval span set is obtained, the iterative factor interval span set is used to update the relative factor interval span set, and the step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data is returned.

[0230] If the weight adaptation value tends to converge, similar cave sampling data of the current cave sampling data is identified in the target cave sampling data.

[0231] The similar negative ion concentration corresponding to the similar cave sampling data is identified, a similar negative ion concentration set is obtained, and cave resource evaluation is performed according to the similar negative ion concentration set.

[0232] In several embodiments provided by the present application, it should be understood that the disclosed devices, systems and methods can be implemented in other manners. For example, the system embodiments described above are merely illustrative. For example, the division of the system can be different from the above.

[0233] The modules described as separate components can or can not be physically separated, and the components shown as modules can or can not be physical units, i.e. they can be located in one place or distributed on multiple network units. Part or all of the modules can be selected to achieve the purpose of the embodiment according to actual needs.

[0234] In addition, the functional modules in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of hardware plus software functional modules.

[0235] For those skilled in the art, it is obvious that the present application is not limited to the details of the above exemplary embodiments, and the present application can be realized in other specific forms without departing from the spirit or essential characteristics of the present application.

[0236] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A method for cave resource assessment based on a three-dimensional model, characterized by, The method comprises: obtaining initial cave sampling data and an environmental factor sequence, wherein the environmental factor sequence comprises positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed and PM2.5 concentration; extracting cave three-dimensional sampling points in a preset current cave three-dimensional model in sequence, obtaining a relative factor interval span set according to the initial cave sampling data, and obtaining current cave sampling data of the cave three-dimensional sampling points according to the environmental factor sequence; determining an absolute factor control interval set according to the relative factor interval span set and the current cave sampling data, wherein the relative factor interval span set refers to a relative interval span set of environmental factors, and the absolute factor control interval set refers to a set of absolute control intervals of each environmental factor; screening sampling data meeting the absolute factor control interval set from the initial cave sampling data to obtain target cave sampling data; performing correlation analysis on the environmental factor sequence and a preset air negative ion concentration according to the target cave sampling data to obtain a correlation coefficient sequence; According to the target three-dimensional sampling data of each target three-dimensional sampling point in the target cave sampling data and the current cave sampling data, the environmental factor difference value of the jth target three-dimensional sampling point is calculated by using the following formula , and an environmental factor difference value set is obtained: ； wherein, denotes a current environmental factor sampling value of the i-th environmental factor, denotes a target environmental factor sampling value of the i-th environmental factor of the j-th target three-dimensional sampling point in the target three-dimensional sampling data set, denotes a correlation coefficient of the i-th environmental factor and the air negative ion concentration, denotes an absolute value symbol; collecting target air negative ion concentrations corresponding to target three-dimensional sampling points to obtain a target air negative ion concentration set; drawing an environmental factor-negative ion concentration scatter set according to the environmental factor difference value set and the target air negative ion concentration set; calculating a correlation correlation coefficient of the environmental factor-negative ion concentration scatter set; identifying a minimum difference three-dimensional sampling point corresponding to a minimum environmental factor difference value in the environmental factor difference value set; identifying a minimum difference air negative ion concentration corresponding to the minimum difference three-dimensional sampling point and a current air negative ion concentration corresponding to the cave three-dimensional sampling point; calculating an air negative ion concentration difference value according to the minimum difference air negative ion concentration and the current air negative ion concentration; calculating a weight adaptation value according to a product of the correlation correlation coefficient and a reciprocal of the air negative ion concentration difference value; judging whether the weight adaptation value tends to converge; if the weight adaptation value does not tend to converge, performing interval contraction on the relative factor interval span set to obtain an iterative factor interval span set, updating the relative factor interval span set by using the iterative factor interval span set, and returning to the step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data; if the weight adaptation value tends to converge, identifying similar cave sampling data of the current cave sampling data in the target cave sampling data; identifying similar negative ion concentrations corresponding to the similar cave sampling data to obtain a similar negative ion concentration set, and performing cave resource evaluation according to the similar negative ion concentration set.

2. The three-dimensional model-based cave resource assessment method of claim 1, wherein, The method comprises: identifying an environmental factor sampling interval sequence according to the initial cave sampling data, wherein the environmental factor sampling interval sequence comprises positive ion concentration sampling interval, temperature sampling interval, humidity sampling interval, carbon dioxide concentration sampling interval, wind speed sampling interval and PM2.5 concentration sampling interval; identifying an interval span of each environmental factor sampling interval in the environmental factor sampling interval sequence to obtain a relative factor interval span set; According to the environmental factor sequence, the cave three-dimensional sampling points are monitored to obtain current cave sampling data.

3. The three-dimensional model-based cave resource assessment method of claim 2, wherein, The determining of the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data comprises: The current cave sampling data is sorted according to environmental factors to obtain a current environmental factor sampling value sequence, wherein the current environmental factor sampling value sequence comprises: current positive ion concentration, current temperature, current humidity, current carbon dioxide concentration, current wind speed, and current PM2.5 concentration. In the current environmental factor sampling value sequence, a current environmental factor sampling value is extracted. In the relative factor interval span set, an associated relative interval span corresponding to the current environmental factor sampling value is identified, wherein the associated relative interval span refers to a relative factor interval span corresponding to an environmental factor to which the current environmental factor sampling value belongs. According to the current environmental factor sampling value and the associated relative interval span, an absolute factor control interval is calculated by using the following formula to obtain an absolute factor control interval set: ； wherein, represents the minimum value of the absolute factor control interval of the i-th environmental factor, represents the current environmental factor sample value of the i-th environmental factor, represents the relative interval span of the i-th environmental factor, represents the maximum value of the absolute factor control interval of the i-th environmental factor.

4. The three-dimensional model-based cave resource assessment method of claim 3, wherein, The correlation analysis of the environmental factor sequence and the preset air negative ion concentration according to the target cave sampling data comprises: A target three-dimensional sampling point set of the target cave sampling data is obtained. In the target three-dimensional sampling point set, a target three-dimensional sampling point is extracted. In the target cave sampling data, target three-dimensional sampling data corresponding to the target three-dimensional sampling point is identified to obtain a target three-dimensional sampling data set, wherein the target three-dimensional sampling data comprises: target positive ion concentration, target temperature, target humidity, target carbon dioxide concentration, target wind speed, and target PM2.5 concentration. In the environmental factor sequence, an environmental factor is extracted, and according to the target three-dimensional sampling data set, a correlation coefficient of the environmental factor and the air negative ion concentration is calculated by using the following formula to obtain a correlation coefficient sequence: ； wherein, represents a correlation coefficient of the i-th environmental factor and the concentration of negative air ions, represents the total number of target three-dimensional sampling points, represents a target environmental factor sampling value of the i-th environmental factor of the j-th target three-dimensional sampling point in the target three-dimensional sampling data set, represents an average environmental factor sampling value of the i-th environmental factor in the target three-dimensional sampling data set, represents a target concentration of negative air ions of the j-th target three-dimensional sampling point in the target three-dimensional sampling data set, represents an average concentration of negative air ions in the target three-dimensional sampling data set.

5. The three-dimensional model-based cave resource assessment method of claim 4, wherein, The weight adaptation value is calculated by using the following formula: ； wherein, represents a weight adaptation value corresponding to the pth relative factor interval span set, represents a correlation coefficient corresponding to the pth relative factor interval span set, represents a difference value of the air negative ion concentration corresponding to the pth relative factor interval span set.

6. The method of cave resource assessment based on a three-dimensional model according to claim 5, wherein, The judgment of whether the weight adaptation value tends to converge comprises: The current update number of the relative factor interval span set is identified. It is judged whether the current update number is greater than a preset update threshold. If the current update number is not greater than the update threshold, the weight adaptation value does not tend to converge. If the current update number is greater than the update threshold, the weight adaptation values are collected to obtain a weight adaptation value set. An update serial number set corresponding to each weight adaptation value in the weight adaptation value set is identified to obtain an update serial number set, wherein the update serial number refers to an update order of the relative factor interval span set corresponding to the weight adaptation value, and the update serial number set refers to a collection of update serial numbers corresponding to each weight adaptation value. An adaptation-serial number scatter point set is drawn according to the weight adaptation value set and the update serial number set. The adaptation-serial number scatter point set is fitted to obtain an adaptation-serial number fitting curve. A terminal curve slope of the adaptation-serial number fitting curve is identified. It is judged whether the terminal curve slope is greater than a preset terminal slope threshold. If the terminal curve slope is greater than the terminal slope threshold, the weight adaptation value does not tend to converge. If the terminal curve slope is not greater than the terminal slope threshold, the weight adaptation value tends to converge.

7. The method of cave resource assessment based on a three-dimensional model according to claim 6, wherein, The interval contraction on the relative factor interval span set is performed to obtain an iterative factor interval span set, including: According to the current update number, an iterative factor interval span is calculated by using the following formula to obtain an iterative factor interval span set: ； wherein, denotes the iteration factor interval span of the i-th environmental factor in the iteration factor interval span set of the k-th update, denotes the current update number, denotes the relative factor interval span of the i-th environmental factor in the relative factor interval span set of the current update, denotes the update proportion coefficient.

8. The three-dimensional model-based cave resource assessment method of claim 7, wherein, The similar cave sampling data of the current cave sampling data is identified in the target cave sampling data, including: The minimum difference cave sampling data corresponding to the minimum difference three-dimensional sampling point is identified in the target cave sampling data, wherein the minimum difference cave sampling data includes: positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed, PM2.5 concentration of the minimum difference three-dimensional sampling point; The minimum difference cave sampling data and the minimum difference air negative ion concentration are taken as the similar cave sampling data.

9. The three-dimensional model-based cave resource assessment method of claim 8, wherein, The cave resource evaluation according to the similar negative ion concentration set, including: The model region classification is performed on the current cave three-dimensional model to obtain a feature three-dimensional region set, wherein the feature three-dimensional region set includes: a cave mouth three-dimensional region, a ventilation three-dimensional region, an underground waterfall three-dimensional region, a dissolution channel three-dimensional region, a cave hall three-dimensional region, and a fracture zone three-dimensional region; The region three-dimensional sampling point set of each feature three-dimensional region in the feature three-dimensional region set is identified; The minimum difference air negative ion concentration corresponding to each region three-dimensional sampling point in the region three-dimensional sampling point set is identified; The region three-dimensional sampling voxel is constructed according to the region three-dimensional sampling point to obtain a region three-dimensional sampling voxel set; The voxel volume of the region three-dimensional sampling voxel is identified; The unit air negative ion amount of the region three-dimensional sampling voxel is calculated according to the minimum difference air negative ion concentration and the voxel volume to obtain a unit air negative ion amount set; The region negative ion total amount of the feature three-dimensional region is calculated according to the unit air negative ion amount set to obtain a region negative ion total amount set; The region negative ion total amount set is matched in the initial cave sampling data to obtain a region feature approximate cave; The cave air negative ion total amount corresponding to the region feature approximate cave is identified, and the cave air negative ion total amount is taken as the cave resource evaluation amount of the current cave three-dimensional model corresponding to the current cave to be evaluated.

10. A three-dimensional model based cave resource assessment system, characterized by, The system includes: An absolute factor control interval determination module is configured to obtain initial cave sampling data and an environmental factor sequence, wherein the environmental factor sequence includes: positive ion concentration, temperature, humidity, carbon dioxide concentration, wind speed, and PM2.5 concentration; three-dimensional cave sampling points are sequentially extracted in a preset current cave three-dimensional model, relative factor interval span sets are obtained according to the initial cave sampling data, and current cave sampling data of the three-dimensional cave sampling points are obtained according to the environmental factor sequence; and absolute factor control interval sets are determined according to the relative factor interval span sets and the current cave sampling data, wherein the relative factor interval span set refers to a relative interval span set of environmental factors, and the absolute factor control interval set refers to a set of absolute control intervals of each environmental factor; The sampling data interval control screening module is configured to screen sampling data meeting the absolute factor control interval set from the initial cave sampling data to obtain target cave sampling data; perform correlation analysis on the environmental factor sequence and the preset air negative ion concentration according to the target cave sampling data to obtain a correlation coefficient sequence; and calculate an environmental factor difference value of a jth target three-dimensional sampling point according to target three-dimensional sampling data of each target three-dimensional sampling point in the target cave sampling data and the current cave sampling data by using the following formula to obtain an environmental factor difference value set: ​ ； wherein, represents a current environmental factor sampling value of the i-th environmental factor, represents a target environmental factor sampling value of the i-th environmental factor of the j-th target three-dimensional sampling point in the target three-dimensional sampling data set, represents a correlation coefficient of the i-th environmental factor and the air negative ion concentration, represents an absolute value symbol; a target air negative ion concentration set is obtained by collecting the target air negative ion concentrations corresponding to the target three-dimensional sampling points; an environmental factor-negative ion concentration scatter point set is plotted according to the environmental factor difference value set and the target air negative ion concentration set; a correlation correlation coefficient of the environmental factor-negative ion concentration scatter point set is calculated; a minimum difference three-dimensional sampling point corresponding to a minimum environmental factor difference value in the environmental factor difference value set is identified; a minimum difference air negative ion concentration corresponding to the minimum difference three-dimensional sampling point and a current air negative ion concentration corresponding to the cave three-dimensional sampling point are identified; an air negative ion concentration difference value is calculated according to the minimum difference air negative ion concentration and the current air negative ion concentration; a weight adaptation value is calculated according to the product of the correlation correlation coefficient and the reciprocal of the air negative ion concentration difference value; it is judged whether the weight adaptation value tends to converge; if the weight adaptation value does not tend to converge, the interval contraction is performed on the relative factor interval span set to obtain an iterative factor interval span set, the relative factor interval span set is updated by using the iterative factor interval span set, and the step of determining the absolute factor control interval set according to the relative factor interval span set and the current cave sampling data is returned. A similar cave sampling data identification module is configured to identify similar cave sampling data of the current cave sampling data in target cave sampling data if the weight adaptation value tends to converge. A cave resource evaluation module is configured to identify similar negative ion concentrations corresponding to similar cave sampling data, obtain a similar negative ion concentration set, and perform cave resource evaluation based on the similar negative ion concentration set.

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