Data acquisition and analysis method and system based on humidity sensor

Abstract

The invention relates to a data acquisition and analysis method and system based on a humidity sensor, and belongs to the technical field of humidity sensor data acquisition. Constructing a performance data prediction model according to the performance change characteristic data of the humidity sensors under the environmental factors within a preset time, and predicting the performance data of the humidity sensors under the environmental factors of the target area through the performance data prediction model, and further screening credible sensor data in each timestamp according to the performance data of each humidity sensor under the environmental factors of the target area to obtain final sensor data. By optimizing the wireless sensor network, the energy consumption of the wireless sensor during data acquisition can be further reduced, abnormal data can be further cleared during data acquisition, and the authenticity of the data acquired by the humidity sensor is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of humidity sensor acquisition technology, and in particular to a humidity sensor-based data acquisition and analysis method and system. Background Art

[0002] Social activities are closely related to humidity. With the realization of modernization, it is difficult to find a field that is not related to humidity. Due to different application fields, the technical requirements for humidity sensors are also different. Humidity is the percentage of water contained in the air. To indicate the humidity of the air. Humidity-sensitive elements are the simplest humidity sensors. Humidity-sensitive elements are mainly divided into two categories: resistive and capacitive. The characteristic of humidity-sensitive resistors is that a film made of humidity-sensitive material is covered on the substrate. When water vapor in the air is adsorbed on the humidity-sensitive film, the resistivity and resistance value of the element change. This characteristic can be used to measure humidity. Humidity-sensitive capacitors are generally made of polymer film capacitors. Commonly used polymer materials include polystyrene, polyimide, butyric acid acetate fiber, etc. When the ambient humidity changes, the dielectric constant of the humidity-sensitive capacitor changes, causing its capacitance to change as well. The change in capacitance is proportional to the relative humidity. Sensors have many application scenarios, such as agriculture and meteorology. However, sensors are often used in clusters to form a wireless sensor network. Nowadays, when collecting data through wireless sensors, the energy consumption is too high, and the collected data may have certain anomalies. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a data acquisition and analysis method and system based on a humidity sensor.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A first aspect of the present invention provides a data collection and analysis method based on a humidity sensor, comprising:

[0006] A sensor network is constructed by arranging humidity sensors in a target area, and an optimized sensor network is obtained by optimizing data acquisition energy consumption of the sensor network;

[0007] The sensor data of each timestamp is obtained through the optimized sensor network, and the reliable sensor data in each timestamp is obtained by evaluating and analyzing the sensor data of each timestamp;

[0008] Collecting performance change characteristic data of the humidity sensor under various environmental factors within a preset time, and building a performance data prediction model according to the performance change characteristic data of the humidity sensor under various environmental factors within the preset time;

[0009] The performance data of each humidity sensor under the environmental factors of the target area is predicted by the performance data prediction model, and the reliable sensor data in each timestamp is further screened according to the performance data of each humidity sensor under the environmental factors of the target area to obtain the final sensor data.

[0010] Furthermore, in the data collection and analysis method based on humidity sensors, humidity sensors are arranged in the target area to construct a sensor network, and the data collection energy consumption of the sensor network is optimized to obtain an optimized sensor network, specifically:

[0011] By arranging humidity sensors in the target area, building a sensor network, obtaining the arrangement position of each humidity sensor, and initializing the installation position of the data aggregation node, the Euclidean distance value is calculated based on the arrangement position of the humidity sensor and the installation position of the data aggregation node;

[0012] Obtaining the data collection energy consumption value per unit Euclidean distance value through big data, and calculating the data collection energy consumption value of each data aggregation node based on the data collection energy consumption value per unit Euclidean distance value and the Euclidean distance value, and calculating the total data collection energy consumption value;

[0013] Setting a total data collection energy consumption threshold, and determining whether the total data collection energy consumption value is greater than the total data collection energy consumption threshold, if not, outputting the installation position of the data aggregation node, optimizing according to the installation position of the data aggregation node, and obtaining an optimized sensor network;

[0014] If the total data collection energy consumption value is greater than the total data collection energy consumption threshold, re-plan the installation position of the data aggregation node until it is no greater than the total data collection energy consumption threshold, optimize according to the new installation position of the data aggregation node, and obtain the optimized sensor network.

[0015] Furthermore, in the data collection and analysis method based on the humidity sensor, the sensor data of each timestamp is obtained through the optimized sensor network, and the sensor data of each timestamp is evaluated and analyzed to obtain the reliable sensor data in each timestamp, which specifically includes:

[0016] The sensor data of each timestamp is obtained through the optimized sensor network, and the collection delay of the sensor data of each timestamp is obtained during collection, and the collection delay threshold is set;

[0017] If the collection delay of the sensor data of each timestamp during collection is greater than the collection delay threshold, the corresponding sensor data is regarded as untrustworthy sensor data;

[0018] If the collection delay of the sensor data of each timestamp during collection is not greater than the collection delay threshold, the corresponding sensor data is taken as credible sensor data, and the credible sensor data is output.

[0019] Furthermore, in the data collection and analysis method based on the humidity sensor, a performance data prediction model is constructed according to the performance change characteristic data of the humidity sensor under various environmental factors within the preset time, specifically:

[0020] Acquire performance change characteristic data of the humidity sensor under various environmental factors within a preset time, introduce a graph neural network, and input the performance change characteristic data of the humidity sensor under various environmental factors within the preset time into the graph neural network;

[0021] Taking the environmental factor as the first node and the performance characteristic data as the second node, constructing a directed description relationship, and connecting the first node and the second node based on the directed description relationship to construct a topological structure diagram;

[0022] Acquire a relevant adjacency matrix based on the topological structure graph, construct a performance data prediction model based on a deep neural network, and input the relevant adjacency matrix into the performance data prediction model for training;

[0023] When the prediction accuracy of the performance data prediction model is greater than a preset accuracy threshold, the training of the performance data prediction model is completed.

[0024] Furthermore, in the data collection and analysis method based on the humidity sensor, the performance data of each humidity sensor under the environmental factors of the target area is predicted by the performance data prediction model, specifically including:

[0025] Acquire performance change characteristic data of each temperature sensor within a preset time and the current working environment factors, and input the performance change characteristic data of each temperature sensor within the preset time and the current working environment factors into the performance data prediction model for prediction;

[0026] Through prediction, the performance data of each humidity sensor under the environmental factors of the target area at the current timestamp is obtained, and the performance data of each humidity sensor under the environmental factors of the target area at the current timestamp is output.

[0027] Furthermore, in the data collection and analysis method based on the humidity sensor, the reliable sensor data in each timestamp is further screened according to the performance data of each humidity sensor under the environmental factors of the target area to obtain the final sensor data, which specifically includes:

[0028] Setting performance threshold data, and determining whether the performance data of the humidity sensor under the environmental factors of the target area is greater than the performance threshold data;

[0029] When the performance data of the humidity sensor under the environmental factors of the target area is greater than the performance threshold data, the corresponding credible sensor data is output as the final sensor data;

[0030] When the performance data of the humidity sensor under the environmental factors of the target area is not greater than the performance threshold data, the corresponding credible sensor data is discarded, the credible sensor data is updated, and is output as the final sensor data.

[0031] A second aspect of the present invention provides a data acquisition and analysis system based on a humidity sensor, comprising a memory and a processor, wherein the memory comprises a data acquisition and analysis method program based on a humidity sensor, and when the data acquisition and analysis method program based on a humidity sensor is executed by the processor, any step of the data acquisition and analysis method based on a humidity sensor is implemented.

[0032] A third aspect of the present invention provides a computer-readable storage medium, characterized in that it includes a data acquisition and analysis method program based on a humidity sensor, and when the data acquisition and analysis method program based on a humidity sensor is executed by a processor, it implements any step of the data acquisition and analysis method based on a humidity sensor.

[0033] The present invention solves the defects existing in the background technology and has the following beneficial effects:

[0034] The present invention arranges humidity sensors in a target area, constructs a sensor network, optimizes the data acquisition energy consumption of the sensor network, obtains the optimized sensor network, and then obtains sensor data of each timestamp through the optimized sensor network, evaluates and analyzes the sensor data of each timestamp, obtains reliable sensor data in each timestamp, thereby collecting performance change characteristic data of the humidity sensor under various environmental factors within a preset time, and constructs a performance data prediction model according to the performance change characteristic data of the humidity sensor under various environmental factors within a preset time, and finally predicts the performance data of each humidity sensor under the environmental factors of the target area through the performance data prediction model, and further screens the reliable sensor data in each timestamp according to the performance data of each humidity sensor under the environmental factors of the target area, and obtains the final sensor data. The present invention can further reduce the energy consumption of wireless sensors during data acquisition by optimizing the wireless sensor network, and can further clear abnormal data during data acquisition, thereby ensuring the authenticity of the data collected by the humidity sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0036] Figure 1 The overall flow chart of the data acquisition and analysis method based on humidity sensor is shown;

[0037] Figure 2 A partial flow chart of a method for data collection and analysis based on a humidity sensor is shown;

[0038] Figure 3 The system block diagram of the data acquisition and analysis system based on humidity sensor is shown. DETAILED DESCRIPTION

[0039] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying 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.

[0040] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0041] like Figure 1 As shown, the first aspect of the present invention provides a data collection and analysis method based on a humidity sensor, comprising:

[0042] S102: constructing a sensor network by arranging humidity sensors in the target area, and optimizing the data acquisition energy consumption of the sensor network to obtain an optimized sensor network;

[0043] S104: acquiring sensor data of each timestamp through the optimized sensor network, and acquiring credible sensor data in each timestamp by evaluating and analyzing the sensor data of each timestamp;

[0044] S106: collecting performance change characteristic data of the humidity sensor under various environmental factors within a preset time, and building a performance data prediction model according to the performance change characteristic data of the humidity sensor under various environmental factors within the preset time;

[0045] S108: Predicting the performance data of each humidity sensor under the environmental factors of the target area through the performance data prediction model, and further screening the credible sensor data in each timestamp according to the performance data of each humidity sensor under the environmental factors of the target area to obtain the final sensor data.

[0046] It should be noted that the present invention can further reduce the energy consumption of wireless sensors during data collection by optimizing the wireless sensor network, and can further clear abnormal data during data collection, thereby ensuring the authenticity of the data collected by the humidity sensor.

[0047] Furthermore, in the data collection and analysis method based on humidity sensors, humidity sensors are arranged in the target area to construct a sensor network, and the data collection energy consumption of the sensor network is optimized to obtain an optimized sensor network, specifically:

[0048] By arranging humidity sensors in the target area, building a sensor network, obtaining the arrangement position of each humidity sensor, and initializing the installation position of the data aggregation node, the Euclidean distance value is calculated based on the arrangement position of the humidity sensor and the installation position of the data aggregation node;

[0049] The data collection energy consumption value per unit Euclidean distance value is obtained through big data, and the data collection energy consumption value per data aggregation node is calculated based on the data collection energy consumption value per unit Euclidean distance value and the Euclidean distance value, and the total data collection energy consumption value is calculated;

[0050] Set a total data collection energy consumption threshold, and determine whether the total data collection energy consumption value is greater than the total data collection energy consumption threshold. If not, output the installation position of the data aggregation node, optimize according to the installation position of the data aggregation node, and obtain the optimized sensor network;

[0051] If the total data collection energy consumption value is greater than the total data collection energy consumption threshold, re-plan the installation position of the data aggregation node until it is no greater than the total data collection energy consumption threshold, optimize according to the new data aggregation node installation position, and obtain the optimized sensor network.

[0052] It should be noted that, during use, the data collected by several wireless sensors are collected at a data aggregation node and then uniformly transmitted to a computer remote control terminal. The greater the Euclidean distance between the layout position of the humidity sensor and the installation position of the data aggregation node, the higher the energy consumption. By optimizing the energy consumption of the sensor network through this method, the energy consumption during data collection can be reduced.

[0053] Furthermore, in the data collection and analysis method based on the humidity sensor, the sensor data of each timestamp is obtained through the optimized sensor network, and the sensor data of each timestamp is evaluated and analyzed to obtain the reliable sensor data in each timestamp, which specifically includes:

[0054] The sensor data of each timestamp is obtained through the optimized sensor network, and the collection delay of the sensor data of each timestamp is obtained during collection, and the collection delay threshold is set;

[0055] If the collection delay of the sensor data of each timestamp is greater than the collection delay threshold, the corresponding sensor data will be regarded as untrustworthy sensor data;

[0056] If the collection delay of the sensor data of each timestamp is not greater than the collection delay threshold when it is collected, the corresponding sensor data is regarded as credible sensor data and the credible sensor data is output.

[0057] like Figure 2 As shown, further, in the data collection and analysis method based on the humidity sensor, a performance data prediction model is constructed according to the performance change characteristic data of the humidity sensor under various environmental factors within a preset time, specifically:

[0058] S202: Obtaining performance change characteristic data of the humidity sensor under various environmental factors within a preset time, and introducing a graph neural network to input the performance change characteristic data of the humidity sensor under various environmental factors within the preset time into the graph neural network;

[0059] S204: Taking the environmental factor as the first node and the performance characteristic data as the second node, constructing a directed description relationship, connecting the first node and the second node based on the directed description relationship, and constructing a topological structure diagram;

[0060] S206: Obtaining a relevant adjacency matrix based on the topological structure graph, building a performance data prediction model based on a deep neural network, and inputting the relevant adjacency matrix into the performance data prediction model for training;

[0061] S208: When the prediction accuracy of the performance data prediction model is greater than the preset accuracy threshold, the training of the performance data prediction model is completed.

[0062] It should be noted that the performance of humidity sensors under different environmental factors is different. For example, in acidic soil environment, alkaline soil environment, and acid rain environment, the performance in acidic soil environment, alkaline soil environment, and acid rain environment is often abnormal, that is, the collected data may be abnormal. The performance data includes sensitivity performance data, response time, humidity sensing characteristic quantity, humidity temperature coefficient and other data.

[0063] Furthermore, in the data collection and analysis method based on the humidity sensor, the performance data of each humidity sensor under the environmental factors of the target area is predicted by the performance data prediction model, specifically including:

[0064] Acquire performance change characteristic data of each temperature sensor within a preset time and the current working environment factors, and input the performance change characteristic data of each temperature sensor within the preset time and the current working environment factors into the performance data prediction model for prediction;

[0065] Through prediction, the performance data of each humidity sensor under the environmental factors of the target area at the current timestamp is obtained, and the performance data of each humidity sensor under the environmental factors of the target area at the current timestamp is output.

[0066] Furthermore, in the data collection and analysis method based on the humidity sensor, the reliable sensor data in each timestamp is further screened according to the performance data of each humidity sensor under the environmental factors of the target area to obtain the final sensor data, which specifically includes:

[0067] Setting performance threshold data, and determining whether the performance data of the humidity sensor under the environmental factors of the target area is greater than the performance threshold data;

[0068] When the performance data of the humidity sensor under the environmental factors of the target area is greater than the performance threshold data, the corresponding reliable sensor data is output as the final sensor data;

[0069] When the performance data of the humidity sensor under the environmental factors of the target area is not greater than the performance threshold data, the corresponding credible sensor data is discarded, the credible sensor data is updated, and is output as the final sensor data.

[0070] It should be noted that this method can eliminate abnormal data and ensure the authenticity of the sensor data.

[0071] In addition, the method further comprises:

[0072] Obtain network fluctuation data information of sensor networks under various influencing factors during data transmission through big data, and build a network fluctuation data prediction model based on deep neural networks;

[0073] Inputting the network fluctuation data information of the sensor network when performing data transmission under the influencing factors into the network fluctuation data prediction model for training, and obtaining the trained network fluctuation data prediction model;

[0074] Acquire influencing factor data in a target area, and input the influencing factor data in the target area into the trained network fluctuation data prediction model for prediction, so as to acquire network fluctuation data in the target area;

[0075] The collection delay of the sensor data of each timestamp during collection is updated according to the network fluctuation data in the target area, and the sensor data to be eliminated is updated.

[0076] It should be noted that due to different influencing factors, the network fluctuation data information (information transmission speed) of the sensor network during data transmission is different, including temperature, humidity and other data. Different information transmission speeds will lead to different data collection delays. This method fully considers the network fluctuation problem and timely updates the collection delay of the sensor data of each timestamp during collection, further ensuring the authenticity of the sensor data obtained.

[0077] like Figure 3 As shown, the second aspect of the present invention provides a data acquisition and analysis system 4 based on a humidity sensor, including a memory 41 and a processor 42. The memory 41 includes a data acquisition and analysis method program based on a humidity sensor. When the data acquisition and analysis method program based on a humidity sensor is executed by the processor 42, it includes:

[0078] By arranging humidity sensors in the target area, a sensor network is constructed, and by optimizing the data acquisition energy consumption of the sensor network, an optimized sensor network is obtained;

[0079] The sensor data of each timestamp is obtained through the optimized sensor network, and the reliable sensor data in each timestamp is obtained by evaluating and analyzing the sensor data of each timestamp;

[0080] Collect performance change characteristic data of the humidity sensor under various environmental factors within a preset time, and build a performance data prediction model based on the performance change characteristic data of the humidity sensor under various environmental factors within the preset time;

[0081] The performance data of each humidity sensor under the environmental factors of the target area is predicted through the performance data prediction model, and the reliable sensor data in each timestamp is further screened according to the performance data of each humidity sensor under the environmental factors of the target area to obtain the final sensor data.

[0082] Furthermore, in the data acquisition and analysis system based on humidity sensors, humidity sensors are arranged in the target area to construct a sensor network, and the data acquisition energy consumption of the sensor network is optimized to obtain an optimized sensor network, specifically:

[0083] By arranging humidity sensors in the target area, building a sensor network, obtaining the arrangement position of each humidity sensor, and initializing the installation position of the data aggregation node, the Euclidean distance value is calculated based on the arrangement position of the humidity sensor and the installation position of the data aggregation node;

[0084] The data collection energy consumption value per unit Euclidean distance value is obtained through big data, and the data collection energy consumption value per data aggregation node is calculated based on the data collection energy consumption value per unit Euclidean distance value and the Euclidean distance value, and the total data collection energy consumption value is calculated;

[0085] Set a total data collection energy consumption threshold, and determine whether the total data collection energy consumption value is greater than the total data collection energy consumption threshold. If not, output the installation position of the data aggregation node, optimize according to the installation position of the data aggregation node, and obtain the optimized sensor network;

[0086] If the total data collection energy consumption value is greater than the total data collection energy consumption threshold, re-plan the installation position of the data aggregation node until it is no greater than the total data collection energy consumption threshold, optimize according to the new data aggregation node installation position, and obtain the optimized sensor network.

[0087] It should be noted that, during use, the data collected by several wireless sensors are collected at a data aggregation node and then uniformly transmitted to a computer remote control terminal. The greater the Euclidean distance between the layout position of the humidity sensor and the installation position of the data aggregation node, the higher the energy consumption. By optimizing the energy consumption of the sensor network through this method, the energy consumption during data collection can be reduced.

[0088] Furthermore, in the data acquisition and analysis system based on humidity sensors, the sensor data of each timestamp is obtained through the optimized sensor network, and the sensor data of each timestamp is evaluated and analyzed to obtain the reliable sensor data in each timestamp, which specifically includes:

[0089] The sensor data of each timestamp is obtained through the optimized sensor network, and the collection delay of the sensor data of each timestamp is obtained during collection, and the collection delay threshold is set;

[0090] If the collection delay of the sensor data of each timestamp is greater than the collection delay threshold, the corresponding sensor data will be regarded as untrustworthy sensor data;

[0091] If the collection delay of the sensor data of each timestamp is not greater than the collection delay threshold when it is collected, the corresponding sensor data is regarded as credible sensor data and the credible sensor data is output.

[0092] Furthermore, in the data collection and analysis system based on the humidity sensor, a performance data prediction model is constructed according to the performance change characteristic data of the humidity sensor under various environmental factors within a preset time, specifically:

[0093] Obtain performance change characteristic data of the humidity sensor under various environmental factors within a preset time, and introduce a graph neural network to input the performance change characteristic data of the humidity sensor under various environmental factors within a preset time into the graph neural network;

[0094] Taking the environmental factor as the first node and the performance characteristic data as the second node, constructing a directed description relationship, connecting the first node and the second node based on the directed description relationship, and constructing a topological structure diagram;

[0095] Obtain the relevant adjacency matrix based on the topological structure graph, build a performance data prediction model based on the deep neural network, and input the relevant adjacency matrix into the performance data prediction model for training;

[0096] When the prediction accuracy of the performance data prediction model is greater than a preset accuracy threshold, the training of the performance data prediction model is completed.

[0097] It should be noted that the performance of humidity sensors in different environments is different, for example, in acidic soil environment, alkaline soil environment, and acid rain environment. Among them, in acidic soil environment, alkaline soil environment, and acid rain environment, the performance is often abnormal, that is, the collected data may be abnormal. The performance data includes sensitivity performance data, response time, humidity sensing characteristic quantity, humidity temperature coefficient and other data.

[0098] Furthermore, in the data collection and analysis system based on the humidity sensor, the performance data of each humidity sensor under the environmental factors of the target area is predicted by the performance data prediction model, specifically including:

[0099] Acquire performance change characteristic data of each temperature sensor within a preset time and the current working environment factors, and input the performance change characteristic data of each temperature sensor within the preset time and the current working environment factors into the performance data prediction model for prediction;

[0100] Through prediction, the performance data of each humidity sensor under the environmental factors of the target area at the current timestamp is obtained, and the performance data of each humidity sensor under the environmental factors of the target area at the current timestamp is output.

[0101] Furthermore, in the data acquisition and analysis system based on the humidity sensor, the reliable sensor data in each timestamp is further screened according to the performance data of each humidity sensor under the environmental factors of the target area to obtain the final sensor data, including:

[0102] Setting performance threshold data, and determining whether the performance data of the humidity sensor under the environmental factors of the target area is greater than the performance threshold data;

[0103] When the performance data of the humidity sensor under the environmental factors of the target area is greater than the performance threshold data, the corresponding reliable sensor data is output as the final sensor data;

[0104] When the performance data of the humidity sensor under the environmental factors of the target area is not greater than the performance threshold data, the corresponding credible sensor data is discarded, the credible sensor data is updated, and is output as the final sensor data.

[0105] It should be noted that this method can eliminate abnormal data and ensure the authenticity of the sensor data.

[0106] The third aspect of the present invention provides a computer-readable storage medium, characterized in that it includes a data acquisition and analysis method program based on a humidity sensor, and when the data acquisition and analysis method program based on a humidity sensor is executed by a processor, it implements any step of the data acquisition and analysis method based on a humidity sensor.

[0107] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0108] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0109] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0110] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks or optical disks, and other media that can store program codes.

[0111] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for 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 each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0112] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A data collection and analysis method based on a humidity sensor, characterized in that: include: A sensor network is constructed by arranging humidity sensors in a target area, and an optimized sensor network is obtained by optimizing data acquisition energy consumption of the sensor network; The sensor data of each timestamp is obtained through the optimized sensor network, and the reliable sensor data in each timestamp is obtained by evaluating and analyzing the sensor data of each timestamp; Collecting performance change characteristic data of the humidity sensor under various environmental factors within a preset time, and building a performance data prediction model according to the performance change characteristic data of the humidity sensor under various environmental factors within the preset time; The performance data of each humidity sensor under the environmental factors of the target area is predicted by the performance data prediction model, and the reliable sensor data in each timestamp is further screened according to the performance data of each humidity sensor under the environmental factors of the target area to obtain the final sensor data.

2. The data acquisition and analysis method based on humidity sensor according to claim 1 is characterized in that: By arranging humidity sensors in the target area, a sensor network is constructed, and by optimizing the data acquisition energy consumption of the sensor network, an optimized sensor network is obtained, specifically: By arranging humidity sensors in the target area, building a sensor network, obtaining the arrangement position of each humidity sensor, and initializing the installation position of the data aggregation node, the Euclidean distance value is calculated based on the arrangement position of the humidity sensor and the installation position of the data aggregation node; Obtaining the data collection energy consumption value per unit Euclidean distance value through big data, and calculating the data collection energy consumption value of each data aggregation node based on the data collection energy consumption value per unit Euclidean distance value and the Euclidean distance value, and calculating the total data collection energy consumption value; Setting a total data collection energy consumption threshold, and determining whether the total data collection energy consumption value is greater than the total data collection energy consumption threshold, if not, outputting the installation position of the data aggregation node, optimizing according to the installation position of the data aggregation node, and obtaining an optimized sensor network; If the total data collection energy consumption value is greater than the total data collection energy consumption threshold, re-plan the installation position of the data aggregation node until it is no greater than the total data collection energy consumption threshold, optimize according to the new installation position of the data aggregation node, and obtain the optimized sensor network.

3. The data acquisition and analysis method based on humidity sensor according to claim 1 is characterized in that: The sensor data of each timestamp is obtained through the optimized sensor network. The sensor data of each timestamp is evaluated and analyzed to obtain the reliable sensor data in each timestamp, including: The sensor data of each timestamp is obtained through the optimized sensor network, and the collection delay of the sensor data of each timestamp is obtained during collection, and the collection delay threshold is set; If the collection delay of the sensor data of each timestamp during collection is greater than the collection delay threshold, the corresponding sensor data is regarded as untrustworthy sensor data; If the collection delay of the sensor data of each timestamp during collection is not greater than the collection delay threshold, the corresponding sensor data is taken as credible sensor data, and the credible sensor data is output.

4. The data acquisition and analysis method based on humidity sensor according to claim 1 is characterized in that: A performance data prediction model is constructed based on the performance change characteristic data of the humidity sensor under various environmental factors within the preset time, specifically: Acquire performance change characteristic data of the humidity sensor under various environmental factors within a preset time, introduce a graph neural network, and input the performance change characteristic data of the humidity sensor under various environmental factors within the preset time into the graph neural network; Taking the environmental factor as the first node and the performance characteristic data as the second node, constructing a directed description relationship, and connecting the first node and the second node based on the directed description relationship to construct a topological structure diagram; Acquire a relevant adjacency matrix based on the topological structure graph, construct a performance data prediction model based on a deep neural network, and input the relevant adjacency matrix into the performance data prediction model for training; When the prediction accuracy of the performance data prediction model is greater than a preset accuracy threshold, the training of the performance data prediction model is completed.

5. The data acquisition and analysis method based on humidity sensor according to claim 1 is characterized in that: Predicting the performance data of each humidity sensor under the environmental factors of the target area by the performance data prediction model specifically includes: Acquire performance change characteristic data of each temperature sensor within a preset time and the current working environment factors, and input the performance change characteristic data of each temperature sensor within the preset time and the current working environment factors into the performance data prediction model for prediction; Through prediction, the performance data of each humidity sensor under the environmental factors of the target area at the current timestamp is obtained, and the performance data of each humidity sensor under the environmental factors of the target area at the current timestamp is output.

6. The data acquisition and analysis method based on humidity sensor according to claim 1 is characterized in that: Further screening the reliable sensor data in each timestamp according to the performance data of each humidity sensor under the environmental factors of the target area to obtain the final sensor data, specifically including: Setting performance threshold data, and determining whether the performance data of the humidity sensor under the environmental factors of the target area is greater than the performance threshold data; When the performance data of the humidity sensor under the environmental factors of the target area is greater than the performance threshold data, the corresponding credible sensor data is output as the final sensor data; When the performance data of the humidity sensor under the environmental factors of the target area is not greater than the performance threshold data, the corresponding credible sensor data is discarded, the credible sensor data is updated, and is output as the final sensor data.

7. The data acquisition and analysis system based on humidity sensor is characterized by: It comprises a memory and a processor, wherein the memory comprises a data acquisition and analysis method program based on a humidity sensor, and when the data acquisition and analysis method program based on a humidity sensor is executed by the processor, the steps of the data acquisition and analysis method based on a humidity sensor as described in any one of claims 1-6 are implemented.

8. A computer-readable storage medium, characterized in that: It comprises a data collection and analysis method program based on a humidity sensor, and when the data collection and analysis method program based on a humidity sensor is executed by a processor, the steps of the data collection and analysis method based on a humidity sensor as described in any one of claims 1 to 6 are implemented.

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