A humidity sensor configuration method and system based on environmental feature analysis

Abstract

The present application relates to the technical field of sensor installation, and particularly relates to a humidity sensor configuration method and system based on environmental feature analysis. Predicted characteristic environmental parameters of each sub-region are obtained, and the predicted characteristic environmental parameters are introduced into an anticorrosion capability prediction model for prediction. Each sub-region is analyzed by screening and judging according to a predicted anticorrosion capability change data set corresponding to the humidity sensor to be configured when performing a preset monitoring task in each sub-region, and a configurable sub-region is screened out. The installation adaptability of each configurable sub-region is analyzed according to each actual scene three-dimensional model diagram, and the final configuration sub-region of the humidity sensor to be configured is obtained by analysis. The method helps to improve the service life, working reliability and accuracy of monitoring data of the humidity sensor, and ensures that the sensor can be installed at a suitable spatial position, thereby realizing efficient humidity monitoring.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of sensor installation, in particular to a humidity sensor configuration method and system based on environmental feature analysis. BACKGROUND

[0002] With the rapid development of technologies such as Internet of Things, smart city, and smart agriculture, the demand for environmental monitoring is increasing. As an important environmental parameter, accurate monitoring of humidity is crucial for many fields. Traditional humidity sensor configuration methods often rely on experience and manual judgment, lacking scientific basis, resulting in unreasonable sensor configuration locations, insufficient monitoring data accuracy, and affecting the monitoring effect and service life of the sensor. For example, in some corrosive environments, humidity sensors are prone to failure, leading to abnormal monitoring data or system failure. Therefore, there is an urgent need for a method that can scientifically and reasonably configure humidity sensors based on environmental features to improve monitoring accuracy, extend sensor service life, and reduce maintenance costs.

[0003] In the prior art, some studies focus on the performance optimization and anti-interference ability of humidity sensors, but few studies focus on sensor configuration methods based on environmental feature analysis. Environmental features such as temperature, humidity, pH, and salinity have a significant impact on the corrosion resistance and stability of humidity sensors. Therefore, a humidity sensor configuration method that considers the influence of environmental features is needed, which can analyze environmental feature parameters to predict the corrosion resistance of sensors in different environments and select suitable installation areas. SUMMARY

[0004] The present application overcomes the shortcomings of the prior art and provides a humidity sensor configuration method and system based on environmental feature analysis.

[0005] To achieve the above-mentioned purposes, the technical solution adopted by the present application is as follows:

[0006] The present application discloses a humidity sensor configuration method based on environmental feature analysis, comprising the following steps:

[0007] Obtain the corrosion resistance change data of the humidity sensor to be configured when working under various characteristic environmental parameter conditions, and construct a corrosion resistance prediction model according to the corrosion resistance change data when working under various characteristic environmental parameter conditions;

[0008] Obtain the preset configuration range area of the humidity sensor to be configured, and divide the preset configuration range area into several sub-areas;

[0009] The predicted characteristic environmental parameters of each sub-region are obtained, the predicted characteristic environmental parameters are introduced into the corrosion resistance prediction model for prediction, and a predicted corrosion resistance change data set corresponding to the to-be-configured humidity sensor when performing a preset monitoring task in each sub-region is obtained;

[0010] Each sub-region is screened, judged, and analyzed according to the predicted corrosion resistance change data set corresponding to the to-be-configured humidity sensor when performing a preset monitoring task in each sub-region, and a configurable sub-region is screened out.

[0011] The actual scene three-dimensional model graph of each configurable sub-region is obtained, the installation adaptability of each configurable sub-region is analyzed according to each actual scene three-dimensional model graph, and the final configuration sub-region of the to-be-configured humidity sensor is obtained.

[0012] Preferably, the corrosion resistance change data of the to-be-configured humidity sensor when working under various characteristic environmental parameter conditions is obtained, and the corrosion resistance prediction model is constructed according to the corrosion resistance change data when working under various characteristic environmental parameter conditions, specifically:

[0013] The corrosion resistance change data of the to-be-configured humidity sensor when working under various characteristic environmental parameter conditions is obtained through a big data network;

[0014] The decision tree algorithm is introduced, the characteristic environmental parameters are taken as decision attributes, the corrosion resistance change data is taken as a target attribute, and the root node is initialized according to the decision attributes and the target attribute;

[0015] Starting from the root node, the value range of different characteristic environmental parameters is split, each branch corresponds to a value interval, and the hierarchical structure of the tree is gradually constructed; wherein, in the construction process, the best split attribute is selected based on information gain;

[0016] Finally, the decision tree is converted into a topological structure graph, and a relationship matrix between various characteristic environmental parameters and corresponding corrosion resistance change data is obtained according to the topological structure graph; wherein, the nodes of the decision tree correspond to the nodes in the topological structure graph, and the branches correspond to the edges;

[0017] The prediction model is constructed based on a deep learning network, and the relationship matrix is embedded in the prediction model for coding learning and training until the model prediction accuracy meets the preset requirements, the final parameters of the model are saved, and the corrosion resistance prediction model is output.

[0018] Preferably, the predicted characteristic environmental parameters of each sub-region are obtained, the predicted characteristic environmental parameters are introduced into the corrosion resistance prediction model for prediction, and a predicted corrosion resistance change data set corresponding to the to-be-configured humidity sensor when performing a preset monitoring task in each sub-region is obtained, specifically:

[0019] acquire a preset working time period of the to-be-configured humidity sensor, perform discrete time processing on the preset working time period, and obtain a preset working timestamp of the to-be-configured humidity sensor performing a preset monitoring task;

[0020] acquire, based on meteorological software, a predicted characteristic environmental parameter corresponding to each sub-region at each preset working timestamp;

[0021] import the predicted characteristic environmental parameter corresponding to each sub-region at each preset working timestamp into the corrosion resistance prediction model for prediction;

[0022] acquire, through prediction, a predicted corrosion resistance change dataset corresponding to the to-be-configured humidity sensor performing the preset monitoring task in each sub-region;

[0023] The predicted corrosion resistance change dataset is a data collection of predicted corrosion resistance data corresponding to the to-be-configured humidity sensor at each preset working timestamp.

[0024] Preferably, each sub-region is screened and analyzed according to the predicted corrosion resistance change dataset corresponding to the to-be-configured humidity sensor performing the preset monitoring task in each sub-region, and a configurable sub-region is screened out, specifically:

[0025] acquire a preset corrosion resistance threshold of the to-be-configured humidity sensor, and acquire a predicted corrosion resistance change dataset corresponding to the to-be-configured humidity sensor performing the preset monitoring task in each sub-region;

[0026] compare the predicted corrosion resistance data corresponding to each preset working timestamp in the predicted corrosion resistance change dataset corresponding to each sub-region performing the preset monitoring task with the corrosion resistance threshold;

[0027] mark the preset working timestamp corresponding to the predicted corrosion resistance data less than the corrosion resistance threshold as a potential corrosion risk timestamp, and mark the preset working timestamp corresponding to the predicted corrosion resistance data greater than the corrosion resistance threshold as a safe working timestamp;

[0028] by analogy, until the predicted corrosion resistance data in each predicted corrosion resistance change dataset is analyzed and compared, the total number of potential corrosion risk timestamps and the total number of safe working timestamps of the to-be-configured humidity sensor performing the preset monitoring task in each sub-region are counted;

[0029] perform ratio processing on the total number of potential corrosion risk timestamps and the total number of safe working timestamps of the to-be-configured humidity sensor performing the preset monitoring task in each sub-region, and obtain a potential corrosion risk timestamp proportion of the to-be-configured humidity sensor performing the preset monitoring task in each sub-region.

[0030] Preferably, each sub-region is screened and analyzed according to the predicted anticorrosion ability change data set corresponding to the humidity sensor to be configured to perform the preset monitoring task in each sub-region, and the configurable sub-region is screened out, and the method further comprises the following steps:

[0031] The potential corrosion risk timestamp proportion of the humidity sensor to be configured to perform the preset monitoring task in each sub-region is compared with the preset proportion value;

[0032] If the potential corrosion risk timestamp proportion of the humidity sensor to be configured to perform the preset monitoring task in a certain sub-region is greater than the preset proportion value, the sub-region is marked as a corrosive sub-region;

[0033] If the potential corrosion risk timestamp proportion of the humidity sensor to be configured to perform the preset monitoring task in a certain sub-region is not greater than the preset proportion value, the sub-region is marked as a safe sub-region;

[0034] The safe sub-region is marked as a configurable sub-region, and the corrosive sub-region is marked as a non-configurable sub-region.

[0035] Preferably, the actual scene three-dimensional model graph of each configurable sub-region is obtained, and installation adaptability analysis is performed on each configurable sub-region according to each actual scene three-dimensional model graph, and the final configuration sub-region of the humidity sensor to be configured is analyzed and obtained, and specifically:

[0036] The actual scene image information of each configurable sub-region is obtained, and the actual scene three-dimensional model graph of each configurable sub-region is constructed according to the actual scene image information;

[0037] A standard installation scene model graph of the humidity sensor to be configured is obtained; a cosine similarity algorithm is introduced, and the cosine similarity between each actual scene three-dimensional model graph and the standard installation scene model graph is calculated based on the cosine similarity algorithm;

[0038] A sorting table is constructed, each calculated cosine similarity is introduced into the sorting table for sorting, the maximum cosine similarity is sorted out, and the configurable sub-region corresponding to the maximum cosine similarity is obtained;

[0039] The configurable sub-region corresponding to the maximum cosine similarity is marked as the final configuration sub-region and output.

[0040] The application also discloses a humidity sensor configuration system based on environmental feature analysis, which comprises a memory and a processor, and the memory stores a humidity sensor configuration method program.

[0041] The present application solves the technical defects in the background art, and has the following beneficial effects: the humidity sensor configuration method based on environmental feature analysis can comprehensively consider the influence of environmental features on the corrosion resistance of the humidity sensor and the installation adaptability requirement. First, an anti-corrosion capability prediction model is constructed to predict the corrosion resistance of the sensor in different sub-regions, and then the sub-regions feasible in terms of corrosion resistance are selected, and then the most suitable final configuration sub-region in terms of installation is determined according to the actual scene three-dimensional model. This helps to improve the service life, working reliability and monitoring data accuracy of the humidity sensor, while ensuring that the sensor can be installed at a suitable spatial position to achieve efficient humidity monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0043] Figure 1 The first method flowchart of the humidity sensor configuration method based on environmental feature analysis;

[0044] Figure 2 The second method flowchart of the humidity sensor configuration method based on environmental feature analysis;

[0045] Figure 3 The system block diagram of the humidity sensor configuration system based on environmental feature analysis. DETAILED DESCRIPTION

[0046] In order to more clearly illustrate the above-mentioned purposes, features and advantages of the present application, the present application will be further described in detail below in combination with the drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0047] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, however, the present application can also be implemented in other ways different from those described herein, therefore, the scope of protection of the present application is not limited by the specific embodiments disclosed below.

[0048] As shown in Figure 1 The present application discloses a humidity sensor configuration method based on environmental feature analysis, comprising the following steps:

[0049] S102: Obtain the corrosion resistance change data of the to-be-configured humidity sensor when working under various characteristic environmental parameter conditions, and construct a corrosion resistance prediction model according to the corrosion resistance change data when working under various characteristic environmental parameter conditions;

[0050] S104: Obtain a preset configuration range area of the to-be-configured humidity sensor, and divide the preset configuration range area into a plurality of subareas;

[0051] S106: Obtain predicted characteristic environmental parameters of each subarea, input the predicted characteristic environmental parameters into the corrosion resistance prediction model for prediction, and obtain a predicted corrosion resistance change data set corresponding to the to-be-configured humidity sensor when performing a preset monitoring task in each subarea;

[0052] S108: Perform screening judgment analysis on each subarea according to the predicted corrosion resistance change data set corresponding to the to-be-configured humidity sensor when performing a preset monitoring task in each subarea, and screen out configurable subareas;

[0053] S110: Obtain actual scene three-dimensional model graphs of each configurable subarea, perform installation adaptability analysis on each configurable subarea according to each actual scene three-dimensional model graph, and obtain a final configuration subarea of the to-be-configured humidity sensor through analysis.

[0054] It is necessary to first obtain the corrosion resistance change data of the humidity sensor to be configured under various characteristic environmental parameter conditions. Environmental parameters include temperature, humidity, pH, chemical concentration, etc., because different combinations of environmental parameters will have different effects on the corrosion resistance of the sensor. Then, a corrosion resistance prediction model is constructed based on these data. This model is the basis of the entire configuration method, and it can predict the corrosion resistance of the sensor based on the input environmental parameters, providing a basis for subsequent selection of suitable configuration areas. The predetermined configuration range area of the humidity sensor to be configured is obtained and divided into several sub-areas. This division helps to more carefully analyze the influence of environmental characteristics in different local areas on the humidity sensor. Different sub-areas have different environmental characteristics, such as terrain, vegetation coverage, pollution source distribution, etc., which may cause differences in environmental parameters between sub-areas. For each sub-area, its predicted characteristic environmental parameters are obtained, and then these parameters are input into the corrosion resistance prediction model for prediction, thereby obtaining a predicted corrosion resistance change data set of the humidity sensor to be configured in each sub-area when performing the predetermined monitoring task. This step links the environmental characteristics of the sub-area to the corrosion resistance of the sensor, and quantifies the change in the corrosion resistance of the sensor over time (during the execution of the predetermined monitoring task) in different sub-areas through the prediction model. According to the predicted corrosion resistance change data set, each sub-area is screened and analyzed. By setting certain screening criteria, such as comparing whether the corrosion resistance meets the minimum requirement or whether the corrosion resistance change is within an acceptable range, etc., the configurable sub-areas are selected. This step excludes sub-areas that may cause serious corrosion to the humidity sensor, affecting its normal operation and service life. For the selected configurable sub-areas, their actual scene three-dimensional model graphs are obtained. These three-dimensional model graphs contain information such as the actual spatial layout of the sub-area and the distribution of obstacles. Then, the configurable sub-areas are analyzed for installation adaptability based on these three-dimensional model graphs, such as considering the convenience of sensor installation, compatibility with the surrounding environment, etc. Through this analysis, the final configuration sub-area of the humidity sensor to be configured is finally determined, which is the optimal choice in terms of corrosion resistance and installation adaptability.

[0055] The humidity sensor configuration method based on environmental feature analysis can comprehensively consider the influence of environmental features on the corrosion resistance of the humidity sensor and the installation adaptability requirements. First, by constructing a corrosion resistance prediction model, the corrosion resistance of the sensor in different sub-areas is predicted, and sub-areas that are feasible in terms of corrosion resistance are selected, and then the most suitable final configuration sub-area in terms of installation is determined based on the actual scene three-dimensional model graph. This helps to improve the service life, working reliability and monitoring data accuracy of the humidity sensor, while ensuring that the sensor can be installed in the appropriate spatial position to achieve efficient humidity monitoring.

[0056] Preferably, the corrosion resistance change data of the humidity sensor to be configured under various characteristic environmental parameter conditions is acquired, and a corrosion resistance prediction model is constructed according to the corrosion resistance change data under various characteristic environmental parameter conditions, such as Figure 2 as shown, specifically:

[0057] S202: Acquire the corrosion resistance change data of the humidity sensor to be configured under various characteristic environmental parameter conditions through a big data network;

[0058] S204: Introduce a decision tree algorithm, take the characteristic environmental parameters as decision attributes, take the corrosion resistance change data as target attributes, and initialize a root node according to the decision attributes and the target attributes;

[0059] S206: Start from the root node, split according to the value range of different characteristic environmental parameters, each branch corresponds to a value interval, and gradually build the hierarchical structure of the tree; wherein, in the construction process, the best split attribute is selected based on information gain;

[0060] S208: Finally, convert the decision tree into a topological structure diagram, and acquire the relationship matrix between various characteristic environmental parameters and corresponding corrosion resistance change data according to the topological structure diagram; wherein, the nodes of the decision tree correspond to the nodes in the topological structure diagram, and the branches correspond to the edges;

[0061] S210: Construct a prediction model based on a deep learning network, embed the relationship matrix in the prediction model for coding learning training, save the final parameters of the model after the prediction accuracy of the model meets the preset requirements, and output the corrosion resistance prediction model.

[0062] It should be noted that the decision tree algorithm is introduced, the characteristic environmental parameters are taken as the decision attributes, and the corrosion resistance change data are taken as the target attributes to initialize the root node. This step is the starting point of building the decision tree, and the relationship between the input (characteristic environmental parameters) and the output (corrosion resistance change data) in the decision tree is clear. Starting from the root node, the value range of different characteristic environmental parameters is split. Each branch corresponds to a value interval, and the hierarchical structure of the tree is gradually constructed. In this process, the best split attribute is selected based on information gain. Information gain can measure the degree of influence of an attribute on the classification result, and selecting the attribute with the maximum information gain for splitting can make the decision tree more effectively classify the data, thereby better reflecting the relationship between the characteristic environmental parameters and the corrosion resistance change data. The decision tree is converted into a topological structure diagram, and the nodes of the decision tree correspond to the nodes in the topological structure diagram, and the branches correspond to the edges. Then, the relationship matrix between various characteristic environmental parameters and the corresponding corrosion resistance change data is obtained according to the topological structure diagram. This relationship matrix is a quantitative representation of the relationship between the characteristic environmental parameters and the corrosion resistance change data, which can clearly show the internal relationship between different environmental parameters and the corrosion resistance. Based on the deep learning network, a prediction model is constructed, and the relationship matrix is embedded in the prediction model for coding learning and training. The deep learning network has strong learning ability and can learn and optimize the complex relationships in the relationship matrix. During the training process, the parameters of the model are continuously adjusted until the prediction accuracy of the model meets the preset requirements, and the final parameters of the model are saved and the corrosion resistance prediction model is output. This prediction model can accurately predict the corrosion resistance change of the humidity sensor according to the input characteristic environmental parameters.

[0063] The corrosion resistance prediction model constructed by this step can fully utilize the data resources in the big data network, mine the internal relationship between the characteristic environmental parameters and the corrosion resistance change data by means of the decision tree algorithm, and quantify this relationship in the form of a relationship matrix. Then, the powerful learning ability of the deep learning network is used to learn and train this relationship, and the final corrosion resistance prediction model can accurately predict the corrosion resistance change of the humidity sensor according to different characteristic environmental parameters, providing a key basis for subsequent reasonable configuration of the humidity sensor, and helping to improve the reliability of the humidity sensor in different environments, prolong the service life of the humidity sensor, and optimize the configuration and layout of the sensor.

[0064] Preferably, the predicted characteristic environmental parameters of each sub-region are obtained, the predicted characteristic environmental parameters are input into the corrosion resistance prediction model for prediction, and a predicted corrosion resistance change data set corresponding to the to-be-configured humidity sensor when performing a preset monitoring task in each sub-region is obtained. Specifically,

[0065] acquire a preset working time period of the humidity sensor to be configured, perform discretization time processing on the preset working time period, and obtain a preset working timestamp of the humidity sensor to be configured when performing a preset monitoring task;

[0066] acquire a predicted characteristic environmental parameter corresponding to each sub-region at each preset working timestamp based on meteorological software;

[0067] introduce the predicted characteristic environmental parameter corresponding to each sub-region at each preset working timestamp into the corrosion resistance prediction model for prediction;

[0068] acquire a predicted corrosion resistance change dataset corresponding to the humidity sensor to be configured when performing a preset monitoring task in each sub-region through prediction;

[0069] The predicted corrosion resistance change dataset is a data collection of predicted corrosion resistance data corresponding to the humidity sensor to be configured at each preset working timestamp.

[0070] It should be noted that first, the preset working time period of the humidity sensor to be configured is acquired, and the preset working time period is discretized to obtain preset working time stamps. Discretization time processing is to divide continuous time periods into discrete time points. These time points (preset working time stamps) help to more accurately analyze the situation of the humidity sensor at different times. This is because environmental parameters may change at different times. In this way, the influence of time factors on the operation of the humidity sensor can be considered in more detail. The predicted characteristic environmental parameters corresponding to each sub-region at each preset working time stamp are obtained based on the meteorological software. The meteorological software can provide prediction information about the environmental parameters of each sub-region. These predicted characteristic environmental parameters are factors closely related to the working environment of the humidity sensor, such as temperature, humidity, air pressure, and other environmental factors that may affect the corrosion resistance of the sensor. By obtaining these parameters at each preset working time stamp, the environmental conditions of the sub-region at different times can be comprehensively mastered. The predicted characteristic environmental parameters corresponding to each sub-region at each preset working time stamp are imported into the corrosion resistance prediction model for prediction. Since the corrosion resistance prediction model is based on the previously constructed model that reflects the relationship between the characteristic environmental parameters and the change in corrosion resistance, by inputting these predicted characteristic environmental parameters, the predicted corrosion resistance of the humidity sensor to be configured in each sub-region when performing the preset monitoring task can be obtained. The final predicted corrosion resistance change data set is a collection of predicted corrosion resistance data of the humidity sensor to be configured at each preset working time stamp. This data set comprehensively reflects the predicted corrosion resistance of the humidity sensor in different sub-regions and at different preset working time stamps, providing detailed data support for subsequent screening and analysis. Through the above steps, the predicted corrosion resistance change data set of the humidity sensor to be configured in each sub-region when performing the preset monitoring task can be comprehensively and accurately obtained.

[0071] Preferably, the sub-regions are screened and analyzed according to the predicted corrosion resistance change data set corresponding to the humidity sensor to be configured in each sub-region when performing the preset monitoring task, and the configurable sub-regions are screened out, specifically:

[0072] A preset corrosion resistance threshold of the humidity sensor to be configured is set, and a predicted corrosion resistance change data set corresponding to the humidity sensor to be configured in each sub-region when performing the preset monitoring task is obtained;

[0073] The predicted corrosion resistance data corresponding to each preset working time stamp in the predicted corrosion resistance change data set corresponding to each sub-region when performing the preset monitoring task is compared with the corrosion resistance threshold;

[0074] mark the preset working time stamp corresponding to the predicted corrosion resistance capability data less than the corrosion resistance capability threshold as a potential corrosion risk time stamp, and mark the preset working time stamp corresponding to the predicted corrosion resistance capability data greater than the corrosion resistance capability threshold as a safe working time stamp;

[0075] By analogy, after the predicted corrosion resistance capability data in each predicted corrosion resistance capability change data set is analyzed and compared, the total number of potential corrosion risk time stamps and the total number of safe working time stamps when the humidity sensor to be configured performs the preset monitoring task in each sub-region are counted;

[0076] The total number of potential corrosion risk time stamps and the total number of safe working time stamps when the humidity sensor to be configured performs the preset monitoring task in each sub-region are processed by ratio, to obtain the proportion of potential corrosion risk time stamps when the humidity sensor to be configured performs the preset monitoring task in each sub-region.

[0077] It should be noted that first, the corrosion resistance capability threshold of the humidity sensor to be configured is preset, which is a key standard for determining whether the sub-region is suitable for configuring the humidity sensor. Meanwhile, the predicted corrosion resistance capability change data set corresponding to the humidity sensor to be configured when performing the preset monitoring task in each sub-region is obtained, which contains the predicted corrosion resistance capability data of the sensor under different preset working time stamps. For the predicted corrosion resistance capability change data set of each sub-region, the predicted corrosion resistance capability data corresponding to each preset working time stamp is compared with the corrosion resistance capability threshold. If the predicted corrosion resistance capability data is less than the threshold, the corresponding preset working time stamp is marked as a potential corrosion risk time stamp; if it is greater than the threshold, it is marked as a safe working time stamp. This marking method can clearly distinguish the corrosion risk that the sensor may face at different times. After all the predicted corrosion resistance capability data in each predicted corrosion resistance capability change data set is analyzed and compared, the total number of potential corrosion risk time stamps and the total number of safe working time stamps when the humidity sensor to be configured performs the preset monitoring task in each sub-region are counted. Then the total number of potential corrosion risk time stamps and the total number of safe working time stamps are processed by ratio to obtain the proportion of potential corrosion risk time stamps. This proportion can comprehensively reflect the relative degree of corrosion risk that the humidity sensor faces in each sub-region during the entire preset monitoring task.

[0078] By the above steps, according to the comparison result of the corrosion resistance prediction data of each timestamp in the predicted corrosion resistance change data set with the preset threshold, the total number of potential corrosion risk timestamps and the total number of safe working timestamps of each sub-region can be counted, and then the proportion of potential corrosion risk timestamps is obtained. This proportion can quantitatively evaluate the degree of corrosion risk faced by the humidity sensor when performing the preset monitoring task in each sub-region, thereby screening out the configurable sub-regions, i.e. those sub-regions with relatively low potential corrosion risk, providing an effective screening basis for the reasonable configuration of the humidity sensor based on the corrosion resistance, and helping to improve the service life of the humidity sensor and the reliability of the monitoring data.

[0079] Preferably, the configurable sub-regions are screened out by screening and analyzing each sub-region according to the predicted corrosion resistance change data set corresponding to the humidity sensor to be configured performing the preset monitoring task in each sub-region, and the method further comprises the following steps:

[0080] Comparing the proportion of potential corrosion risk timestamps of the humidity sensor to be configured performing the preset monitoring task in each sub-region with a preset proportion value;

[0081] If the proportion of potential corrosion risk timestamps of the humidity sensor to be configured performing the preset monitoring task in a certain sub-region is greater than the preset proportion value, the sub-region is marked as a corrosive sub-region;

[0082] If the proportion of potential corrosion risk timestamps of the humidity sensor to be configured performing the preset monitoring task in a certain sub-region is not greater than the preset proportion value, the sub-region is marked as a safe sub-region;

[0083] The safe sub-region is marked as a configurable sub-region, and the corrosive sub-region is marked as a non-configurable sub-region.

[0084] It should be noted that first, the proportion of the potential corrosion risk time stamp of the to-be-configured humidity sensor when performing the preset monitoring task in each sub-region is compared with a preset proportion value. This preset proportion value is a standard set in advance to distinguish the corrosion degree of the sub-regions. Through this comparison, the situation of each sub-region relative to this standard can be determined. If the proportion of the potential corrosion risk time stamp of the to-be-configured humidity sensor when performing the preset monitoring task in a certain sub-region is greater than the preset proportion value, it means that the sensor faces a relatively long corrosion risk time in this sub-region, so the sub-region is marked as a corrosive sub-region. This marking indicates that the sub-region may have a greater challenge to the corrosion resistance of the humidity sensor and is not suitable for the configuration of the sensor. Conversely, if the proportion of the potential corrosion risk time stamp is not greater than the preset proportion value, it means that the sensor faces a relatively short corrosion risk time in this sub-region, so the sub-region is marked as a safety sub-region. This indicates that the sub-region has a smaller impact on the corrosion resistance of the humidity sensor and is a relatively safe region. Finally, the safety sub-region is designated as a configurable sub-region, and the corrosive sub-region is designated as a non-configurable sub-region. In this way, the sub-regions are classified according to the potential corrosion risk degree of the humidity sensor, and it is determined which sub-regions are suitable for configuring the humidity sensor and which are not.

[0085] Through the above steps, the sub-regions can be accurately divided into corrosive sub-regions and safety sub-regions according to the comparison results of the proportion of the potential corrosion risk time stamp of the to-be-configured humidity sensor in each sub-region and the preset proportion value, and then the configurable sub-regions and non-configurable sub-regions are determined. This method provides a clear basis for the reasonable configuration of the humidity sensor, which helps to avoid configuring the humidity sensor in areas with strong corrosion, thereby improving the service life of the humidity sensor, reducing the performance degradation or failure of the sensor caused by corrosion, and ensuring the accuracy and reliability of the humidity monitoring data.

[0086] Preferably, the actual scene three-dimensional model graph of each configurable sub-region is obtained, and installation adaptability analysis is performed on each configurable sub-region according to each actual scene three-dimensional model graph, and the final configuration sub-region of the to-be-configured humidity sensor is obtained through analysis, specifically:

[0087] The actual scene image information of each configurable sub-region is obtained, and the actual scene three-dimensional model graph of each configurable sub-region is constructed according to the actual scene image information;

[0088] A standard installation scene model graph of the to-be-configured humidity sensor is obtained; a cosine similarity algorithm is introduced, and the cosine similarity between each actual scene three-dimensional model graph and the standard installation scene model graph is calculated based on the cosine similarity algorithm;

[0089] A sorting table is constructed, and the calculated cosine similarities are introduced into the sorting table for sorting, and the maximum cosine similarity is sorted out, and the configurable sub-region corresponding to the maximum cosine similarity is obtained;

[0090] The configurable sub-region corresponding to the maximum cosine similarity is marked as the final configuration sub-region output.

[0091] It should be noted that the actual scene image information of each configurable sub-region is first obtained, and then the actual scene three-dimensional model graph of each configurable sub-region is constructed according to the information. The actual scene image information contains various spatial information of the sub-region, such as terrain, topography, and distribution of surrounding objects. The construction of the three-dimensional model graph can more intuitively and comprehensively reflect the actual scene of the sub-region, and provide detailed spatial information basis for subsequent installation adaptability analysis. A standard installation scene model graph of the humidity sensor to be configured is obtained, which represents the ideal installation scene of the humidity sensor. Then, the cosine similarity algorithm is introduced, and the cosine similarity between each actual scene three-dimensional model graph and the standard installation scene model graph is calculated based on the algorithm. Cosine similarity is an index for measuring the similarity between two vectors, which is used here to measure the similarity between the actual scene and the standard scene. By calculating this similarity, the closeness of each configurable sub-region to the ideal installation scene can be quantitatively represented. A sorting table is constructed, and the calculated cosine similarities are introduced into the sorting table for sorting, and the maximum cosine similarity is sorted out. The configurable sub-region corresponding to the maximum cosine similarity is the sub-region most similar to the standard installation scene. Finally, this sub-region is marked as the final configuration sub-region and output. This means that this sub-region is the best choice in terms of installation adaptability and can best meet the installation requirements of the humidity sensor. Through the above steps, the final configuration sub-region most suitable for the installation of the humidity sensor can be found according to the cosine similarity between the actual scene three-dimensional model graph of each configurable sub-region and the standard installation scene model graph of the humidity sensor to be configured. This method comprehensively considers the spatial factors of the actual scene using three-dimensional model graphs, and quantitatively compares them through the cosine similarity algorithm, so as to select the sub-region with the best installation adaptability from multiple configurable sub-regions, which helps to ensure the performance of the humidity sensor in the actual installation environment and improve the working efficiency and monitoring accuracy of the sensor.

[0092] In addition, the method further includes the following steps:

[0093] The humidity sensor to be configured is pre-installed on the final configuration sub-region, and after pre-installation, the sensitivity of the pre-installed humidity sensor is tested at a plurality of preset time nodes, the sensitivity data of the humidity sensor at the preset time nodes is obtained, and the sensitivity dynamic change data of the humidity sensor in the preset time period is obtained;

[0094] The Bezier curve algorithm is introduced, and the sensitivity data corresponding to the preset time nodes are regarded as control points of the Bezier curve;

[0095] The sensitivity data of the first time node is taken as the starting point of the curve, and then the influence weight of each control point on the shape of the curve is determined according to the characteristics of the Bezier curve;

[0096] The curve is gradually constructed by the Bezier base function, starting from the starting point, fitting the dynamic change data of the humidity sensor sensitivity into the Bezier curve according to the control point order and the corresponding weight;

[0097] The sensitivity requirement range value of the humidity sensor is obtained, the maximum value of the sensitivity requirement range value is taken as the upper constraint boundary, and the minimum value of the sensitivity requirement range value is taken as the lower constraint boundary;

[0098] The constraint domain of the Bezier curve is determined according to the upper constraint boundary and the lower constraint boundary; the total line segment length of the line segment in the Bezier curve located in the constraint domain is calculated, and the total line segment length of the line segment in the Bezier curve located outside the constraint domain is calculated;

[0099] The total line segment length outside the constraint domain is compared with the total line segment length outside the constraint domain to obtain the constraint deviation rate; the constraint deviation rate is compared with a preset threshold value;

[0100] If the constraint deviation rate is not greater than the preset threshold value, the installation position of the humidity sensor is not adjusted; if the constraint deviation rate is greater than the preset threshold value, the installation position of the humidity sensor is readjusted.

[0101] It should be noted that, firstly, the humidity sensor to be configured is pre-installed on the final configuration sub-area, and then its sensitivity is tested at multiple preset time points to obtain dynamic sensitivity change data within the preset time period. Next, the Bezier curve algorithm is introduced, using the sensitivity data corresponding to the preset time points as control points of the Bezier curve. Taking the sensitivity data of the first time point as the curve's starting point, the influence weight of each control point on the curve shape is determined based on the characteristics of the Bezier curve. Then, a curve is constructed using Bezier basis functions, fitting the dynamic sensitivity change data into a Bezier curve. This series of steps transforms discrete sensitivity data into a continuous curve form for subsequent analysis. The required sensitivity range of the humidity sensor is obtained, and the maximum value is determined as the upper constraint boundary, and the minimum value as the lower constraint boundary, thus determining the constraint domain of the Bezier curve. Then, the total length of the line segments within and outside the constraint domain of the Bezier curve is calculated, and the length of the entire line segment outside the constraint domain is compared with the total length of the entire line segment (within the constraint domain + outside the constraint domain) to obtain the constraint deviation rate. This constraint deviation rate reflects the degree of deviation of the actual sensitivity change of the humidity sensor from the required range. The constraint deviation rate is compared with a preset threshold. If the constraint deviation rate is not greater than the preset threshold, it indicates that the sensitivity change of the humidity sensor is within an acceptable range, and no adjustment of the installation position is needed. If the constraint deviation rate is greater than the preset threshold, it indicates that the sensitivity change of the humidity sensor exceeds the acceptable range, suggesting the presence of interference factors (such as electromagnetic interference) at that location, and the installation position needs to be readjusted. By collecting dynamic sensitivity change data of the humidity sensor within a preset time period and constructing a Bezier curve, the constraint domain is determined according to the sensor's sensitivity requirement range, and the constraint deviation rate is calculated. Finally, it is compared with a preset threshold to determine whether to adjust the humidity sensor's installation position. This method can effectively evaluate the sensitivity stability of the humidity sensor after pre-installation in the final configuration sub-area, ensuring that the sensor's sensitivity change is within a reasonable range, thereby improving the reliability and accuracy of the humidity sensor and ensuring its effectiveness in applications such as environmental monitoring.

[0102] This invention also discloses a humidity sensor configuration system based on environmental feature analysis, such as... Figure 3 As shown, the humidity sensor configuration system includes a memory 20 and a processor 80. The memory 20 stores a humidity sensor configuration method program. When the humidity sensor configuration method program is executed by the processor 80, it implements any of the humidity sensor configuration method steps described above.

[0103] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other manners. The described device embodiments are merely illustrative. For example, the division of the units is only a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling, or direct coupling or communication connection between the components can be indirect coupling or communication connection through some interfaces, devices, or units, and can be electrical, mechanical, or in other forms.

[0104] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units; they can be located in one place, or distributed on multiple network units; and some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.

[0105] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0106] Those of ordinary skill in the art can understand that all or part of the steps of the above-described method embodiments can be completed by a program instructing related hardware, and the foregoing program can be stored in a computer readable storage medium, and when the program is executed, the steps of the method embodiments are executed; and the foregoing storage medium includes mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks or optical disks, and various media that can store program codes.

[0107] Alternatively, the integrated units of the present application, if implemented in the form of software functional modules and sold or used as independent products, can also be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present application can be embodied in the form of a software product, and the computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the embodiments of the present application. The foregoing storage medium includes mobile storage devices, ROMs, RAMs, magnetic disks or optical disks, and various media that can store program codes.

[0108] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A humidity sensor configuration method based on environmental feature analysis, characterized by, The method comprises the following steps: obtaining the corrosion resistance change data of the humidity sensor to be configured under various characteristic environmental parameter conditions, and constructing a corrosion resistance prediction model according to the corrosion resistance change data under various characteristic environmental parameter conditions; obtaining a preset configuration range area of the humidity sensor to be configured, and dividing the preset configuration range area into a plurality of subareas; obtaining the predicted characteristic environmental parameters of each subarea, and inputting the predicted characteristic environmental parameters into the corrosion resistance prediction model for prediction to obtain a predicted corrosion resistance change data set corresponding to the humidity sensor to be configured when performing a preset monitoring task in each subarea; screening and judging each subarea according to the predicted corrosion resistance change data set corresponding to the humidity sensor to be configured when performing the preset monitoring task in each subarea, and screening out a configurable subarea; obtaining an actual scene three-dimensional model diagram of each configurable subarea, and performing installation adaptability analysis on each configurable subarea according to each actual scene three-dimensional model diagram, and obtaining a final configuration subarea of the humidity sensor to be configured; screening and judging each subarea according to the predicted corrosion resistance change data set corresponding to the humidity sensor to be configured when performing the preset monitoring task in each subarea, and screening out a configurable subarea, specifically: presetting a corrosion resistance threshold of the humidity sensor to be configured, and obtaining the predicted corrosion resistance change data set corresponding to the humidity sensor to be configured when performing the preset monitoring task in each subarea; comparing the predicted corrosion resistance data corresponding to each preset working time stamp in each predicted corrosion resistance change data set corresponding to each subarea when performing the preset monitoring task with the corrosion resistance threshold; marking the preset working time stamp corresponding to the predicted corrosion resistance data less than the corrosion resistance threshold as a potential corrosion risk time stamp; marking the preset working time stamp corresponding to the predicted corrosion resistance data greater than the corrosion resistance threshold as a safe working time stamp; by analogy, after the predicted corrosion resistance data in each predicted corrosion resistance change data set is analyzed and compared, the total number of potential corrosion risk time stamps and the total number of safe working time stamps of the humidity sensor to be configured when performing the preset monitoring task in each subarea are counted; performing ratio processing on the total number of potential corrosion risk time stamps and the total number of safe working time stamps of the humidity sensor to be configured when performing the preset monitoring task in each subarea, to obtain the proportion of potential corrosion risk time stamps of the humidity sensor to be configured when performing the preset monitoring task in each subarea; screening and judging each subarea according to the predicted corrosion resistance change data set corresponding to the humidity sensor to be configured when performing the preset monitoring task in each subarea, and screening out a configurable subarea, further comprising the following steps: comparing the proportion of potential corrosion risk time stamps of the humidity sensor to be configured when performing the preset monitoring task in each subarea with a preset proportion value; if the proportion of potential corrosion risk time stamps of the humidity sensor to be configured when performing the preset monitoring task in a subarea is greater than the preset proportion value, marking the subarea as a corrosive subarea. If the proportion of the timestamp of the potential corrosion risk of the humidity sensor to be configured when performing the preset monitoring task in a sub-region is less than or equal to a preset proportion value, the sub-region is marked as a safety sub-region; The safety sub-region is marked as a configurable sub-region, and the corrosive sub-region is marked as a non-configurable sub-region.

2. The method of claim 1, wherein, Obtain the corrosion resistance change data of the humidity sensor to be configured under various characteristic environmental parameter conditions, and construct a corrosion resistance prediction model according to the corrosion resistance change data under various characteristic environmental parameter conditions, specifically as follows: Obtain the corrosion resistance change data of the humidity sensor to be configured under various characteristic environmental parameter conditions through a big data network; Introduce a decision tree algorithm, take the characteristic environmental parameters as decision attributes, and take the corrosion resistance change data as target attributes, and initialize a root node according to the decision attributes and the target attributes; Starting from the root node, split according to the value range of different characteristic environmental parameters, and each branch corresponds to a value interval, and gradually build the hierarchical structure of the tree; wherein, in the construction process, the best split attribute is selected based on information gain; Finally, convert the decision tree into a topology structure diagram, obtain the relationship matrix between various characteristic environmental parameters and corresponding corrosion resistance change data according to the topology structure diagram; wherein, the nodes of the decision tree correspond to the nodes in the topology structure diagram, and the branches correspond to the edges; Based on a deep learning network, a prediction model is constructed, and the relationship matrix is embedded in the prediction model for coding learning and training until the prediction accuracy of the model meets the preset requirements, the final parameters of the model are saved, and the corrosion resistance prediction model is output.

3. The method of claim 1, wherein, Obtain the predicted characteristic environmental parameters of each sub-region, import the predicted characteristic environmental parameters into the corrosion resistance prediction model for prediction, obtain the predicted corrosion resistance change data set of the humidity sensor to be configured when performing the preset monitoring task in each sub-region, specifically as follows: Obtain the preset working time period of the humidity sensor to be configured, perform discrete time processing on the preset working time period, and obtain the preset working timestamp of the humidity sensor to be configured when performing the preset monitoring task; Obtain the predicted characteristic environmental parameters of each sub-region at each preset working timestamp based on meteorological software; Import the predicted characteristic environmental parameters of each sub-region at each preset working timestamp into the corrosion resistance prediction model for prediction; Through prediction, obtain the predicted corrosion resistance change data set of the humidity sensor to be configured when performing the preset monitoring task in each sub-region; The predicted corrosion resistance change data set is a data collection of the predicted corrosion resistance data of the humidity sensor to be configured at each preset working timestamp.

4. The method of claim 1, wherein, Obtain the actual scene three-dimensional model diagram of each configurable sub-region, and analyze the installation adaptability of each configurable sub-region according to the actual scene three-dimensional model diagram, to obtain the final configuration sub-region of the humidity sensor to be configured, specifically as follows: Obtain the actual scene image information of each configurable sub-region, and construct the actual scene three-dimensional model diagram of each configurable sub-region according to the actual scene image information; An acquisition standard installation scene model graph of a humidity sensor to be configured is obtained; a cosine similarity algorithm is introduced, and a cosine similarity between each actual scene three-dimensional model graph and the standard installation scene model graph is calculated based on the cosine similarity algorithm; An ordering table is constructed, each calculated cosine similarity is introduced into the ordering table for ordering, a maximum cosine similarity is ordered out, and a configurable sub-region corresponding to the maximum cosine similarity is obtained; The configurable sub-region corresponding to the maximum cosine similarity is marked as a final configuration sub-region output.

5. A humidity sensor configuration system based on environmental feature analysis, characterized by, The humidity sensor configuration system comprises a memory and a processor, the memory stores a humidity sensor configuration method program, and when the humidity sensor configuration method program is executed by the processor, the humidity sensor configuration method steps in any one of claims 1 to 4 are realized.

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