Indoor environment data analysis method and related apparatus

Abstract

Embodiments of the present application provide an indoor environment data analysis method and related device, belonging to the technical field of data analysis. The method comprises: obtaining first monitoring data of a target object at a first time and second monitoring data of the target object at a second time under monitoring of a plurality of Lora sensors; obtaining a first correlation relationship between any two Lora sensors of the plurality of Lora sensors according to the first monitoring data, and obtaining a second correlation relationship between any two Lora sensors of the plurality of Lora sensors according to the second monitoring data; determining a first analysis result of the target object according to the first correlation relationship and the second correlation relationship; determining a first data change according to the first monitoring data and the second monitoring data, and determining a second analysis result of the target object according to the first data change and a data threshold; and determining a target analysis result by fusing the first analysis result and the second analysis result. The problem that the correlation between Lora sensors is not utilized in related technologies, thereby resulting in poor analysis effect, is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to an indoor environment data analysis method and related devices. Background Art

[0002] Indoor monitoring data analysis can be achieved by using complex algorithms and models after collecting data from Lora sensors. For example, recurrent neural networks are used to process Lora sensor data. These technologies significantly improve the intelligence of indoor monitoring data analysis. However, existing technologies lack the connectivity between Lora sensors, which may require more data to achieve the same analytical results, increasing time and computing resources. Furthermore, if the connectivity between Lora sensors is not utilized, the synergistic effect of Lora sensors cannot be fully utilized, resulting in poor analysis results and further increasing security risks. Summary of the Invention

[0003] The main purpose of the embodiments of the present invention is to provide an indoor environment data analysis method and related devices, aiming to solve the problem in the related technology that there is a lack of association between Lora sensors, the synergistic effect of Lora sensors cannot be exerted, resulting in poor analysis results, and further increasing safety risks.

[0004] In a first aspect, an embodiment of the present invention provides an indoor environment data analysis method, comprising:

[0005] Obtaining first monitoring data corresponding to a first moment and second monitoring data corresponding to a second moment of a target object under monitoring by multiple Lora sensors;

[0006] Obtaining a first association relationship between any two Lora sensors among the plurality of Lora sensors based on the first monitoring data, and obtaining a second association relationship between any two Lora sensors among the plurality of Lora sensors based on the second monitoring data;

[0007] Determine a first analysis result corresponding to the target object according to the first association relationship and the second association relationship;

[0008] Determining a first data change corresponding to the target object according to the first monitoring data and the second monitoring data, and determining a second analysis result corresponding to the target object according to the first data change and a data threshold;

[0009] The first analysis result and the second analysis result are integrated to determine a target analysis result corresponding to the target object.

[0010] In a second aspect, an embodiment of the present invention provides an indoor environment data analysis device, comprising:

[0011] a data collection module configured to obtain first monitoring data corresponding to a first time and second monitoring data corresponding to a second time of a target object under monitoring of a plurality of Lora sensors;

[0012] an association analysis module configured to obtain a first association relationship between any two Lora sensors of the plurality of Lora sensors according to the first monitoring data, and obtain a second association relationship between any two Lora sensors of the plurality of Lora sensors according to the second monitoring data;

[0013] a first analysis module configured to determine a first analysis result corresponding to the target object according to the first association relationship and the second association relationship;

[0014] a second analysis module configured to determine a first data change corresponding to the target object according to the first monitoring data and the second monitoring data, and determine a second analysis result corresponding to the target object according to the first data change and a data threshold;

[0015] a target analysis module configured to determine a target analysis result corresponding to the target object by fusing the first analysis result and the second analysis result.

[0016] In a third aspect, an embodiment of the present application further provides a terminal device, which comprises a processor, a memory, a computer program stored in the memory and executable by the processor, and a data bus for realizing connection and communication between the processor and the memory, wherein the computer program is executable by the processor to realize steps of any one of the indoor environment data analysis methods provided in the specification of the present application.

[0017] In a fourth aspect, an embodiment of the present application further provides a storage medium for computer readable storage, characterized in that the storage medium stores one or more programs, and the one or more programs are executable by one or more processors to realize steps of any one of the indoor environment data analysis methods provided in the specification of the present application.

[0018] Embodiments of the present invention provide a method and related apparatus for analyzing indoor environmental data. The method includes obtaining first monitoring data corresponding to a first moment and second monitoring data corresponding to a second moment of a target object monitored by multiple LoRa sensors. The method then analyzes the first monitoring data to determine a first correlation between any two LoRa sensors, and analyzes the second monitoring data to determine a second correlation between any two LoRa sensors. This helps reveal the relationships and functions between different sensors and helps understand the influence and dependencies between sensors in a monitoring network. The method then determines a first analysis result for the target object based on the first and second correlations. This helps detect significant changes or anomalies in the target object. Furthermore, by analyzing the first and second monitoring data, the data change of the target object can be calculated, and a second analysis result for the target object can be determined based on a data threshold. This helps promptly detect data changes and identify potential problems or risks. The first and second analysis results are then integrated to determine a target analysis result for the target object, providing a more comprehensive understanding of the target object's status and supporting subsequent decision-makers in making accurate judgments and decisions. The method also enhances the monitoring and analysis capabilities of target object status changes, facilitating timely warnings of potential problems and the implementation of targeted measures, thereby improving the safety of the target object. It also solves the problem that the related technology lacks the connection between Lora sensors, so the synergistic effect of Lora sensors cannot be exerted, resulting in poor analysis results, further increasing the safety risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 A schematic diagram of a flow chart of an indoor environment data analysis method provided by an embodiment of the present invention;

[0021] Figure 2 A schematic diagram of the module structure of an indoor environment data analysis device provided by an embodiment of the present invention;

[0022] Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0024] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0025] It should be understood that the terms used in this specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the specification and appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0026] Embodiments of the present invention provide a method and related apparatus for analyzing indoor environmental data. The method can be applied to a terminal device, such as a tablet computer, laptop computer, desktop computer, personal digital assistant, or wearable device. The terminal device can also be a server or a server cluster.

[0027] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0028] Please refer to Figure 1 , Figure 1 A flowchart of an indoor environment data analysis method provided by an embodiment of the present invention.

[0029] like Figure 1 As shown, the indoor environment data analysis method includes steps S101 to S105.

[0030] Step S101: Obtain first monitoring data corresponding to a first moment and second monitoring data corresponding to a second moment of a target object under monitoring by multiple Lora sensors.

[0031] For example, Lora sensors typically use low-power wide area network (LPWAN) technology for data transmission, ensuring stable data transmission to centralized storage or processing units. The target object is the indoor environment to be monitored, which can be a scene room for cultural relics protection or an ecological environment for ecological protection, etc.

[0032] Exemplarily, a plurality of LoRa sensors are used to monitor the target object, and the plurality of LoRa sensors include but are not limited to temperature sensors, humidity sensors, light intensity sensors and other LoRa sensors.

[0033] Exemplarily, the second moment can be the current moment, and the first moment can be the previous moment adjacent to the current moment, and then the target object is monitored using multiple Lora sensors to obtain first monitoring data corresponding to the first moment, and the target object is monitored using multiple Lora sensors to obtain second monitoring data corresponding to the second moment.

[0034] Step S102: obtaining a first association relationship between any two Lora sensors among the plurality of Lora sensors according to the first monitoring data, and obtaining a second association relationship between any two Lora sensors among the plurality of Lora sensors according to the second monitoring data.

[0035] Exemplarily, the multiple Lora sensors include a temperature sensor, a humidity sensor, and a light intensity sensor, and then obtain first temperature data corresponding to the temperature sensor, first humidity data corresponding to the humidity sensor, and first light data corresponding to the light intensity sensor from the first monitoring data.

[0036] Exemplarily, the first temperature data and the first humidity data are used to evaluate the first temperature-humidity correlation between the temperature sensor and the humidity sensor using the Pearson correlation coefficient, or the covariance between the first temperature data and the first humidity data is used to measure the overall directional correlation between the temperature sensor and the humidity sensor. For example, a positive covariance indicates that the correlation between the two sensors is positively correlated, and a negative covariance indicates that the correlation between the two sensors is negatively correlated. Similarly, the first temperature data and the first light data are used to determine the first temperature-light correlation between the temperature sensor and the light intensity sensor, and the first light data and the first light data are used to determine the first humidity-light correlation between the humidity sensor and the light intensity sensor, thereby obtaining a first correlation based on the first temperature-humidity correlation, the first temperature-light correlation, and the first humidity-light correlation.

[0037] Exemplarily, second temperature data corresponding to the temperature sensor, second humidity data corresponding to the humidity sensor, and second illumination data corresponding to the illumination intensity sensor are obtained from the second monitoring data.

[0038] Exemplarily, the second temperature-humidity correlation between the temperature sensor and the humidity sensor is evaluated using the second temperature data and the second humidity data using a Pearson correlation coefficient, or the degree of correlation of the overall directionality between the temperature sensor and the humidity sensor is measured using the covariance between the second temperature data and the second humidity data. For example, a positive covariance indicates that the correlation between the two sensors is positive, and a negative covariance indicates that the correlation between the two sensors is negative. Similarly, the second temperature-illumination correlation between the temperature sensor and the illumination intensity sensor is determined using the second temperature data and the second illumination data, and the second humidity-illumination correlation between the humidity sensor and the illumination intensity sensor is determined using the second humidity data and the second illumination data, so as to obtain the second correlation relationship according to the second temperature-humidity correlation, the second temperature-illumination correlation, and the second humidity-illumination correlation.

[0039] In some embodiments, the first monitoring data at least includes first sensing data corresponding to the first Lora sensor and second sensing data corresponding to the second Lora sensor, and the first correlation relationship between any two Lora sensors in the plurality of Lora sensors is obtained according to the first monitoring data, including: performing data clustering on the first sensing data and the second sensing data to obtain a first multi-dimensional data set; performing data correlation analysis according to the first multi-dimensional data set to obtain a correlation matrix between the first Lora sensor and the second Lora sensor; and determining the first correlation relationship between the first Lora sensor and the second Lora sensor according to the correlation matrix.

[0040] Exemplarily, the first monitoring data at least includes first sensing data corresponding to the first Lora sensor and second sensing data corresponding to the second Lora sensor, such as the first Lora sensor being a temperature sensor and the second Lora sensor being a humidity sensor.

[0041] Exemplarily, the first sensing data and the second sensing data are clustered into different clusters using a clustering algorithm (such as K-means clustering, hierarchical clustering, etc.). The goal of clustering is to classify data points between the first sensing data and the second sensing data with similar features into the same cluster. The data points obtained by clustering are grouped into a first multi-dimensional data set. Each data point can be regarded as a vector representing the characteristics in different dimensions.

[0042] Exemplarily, an association analysis method (such as association rule mining, collaborative filtering, etc.) is used to explore the relationship between the first Lora sensor and the second Lora sensor in the first multi-dimensional data set. Association analysis can help discover frequent co-occurrence patterns or rules between sensors, thereby identifying the correlation between them.

[0043] Exemplarily, a correlation matrix is constructed according to the result of the correlation analysis, each element of the matrix representing the correlation degree or relationship strength between two Lora sensors, so that relationship classification is performed based on the correlation matrix to obtain the corresponding first correlation relationship between the first Lora sensor and the second Lora sensor at the first time.

[0044] Specifically, the clustering process helps to discover the potential patterns and clusters in the data among different sensors. These patterns can reveal common features or change patterns between different sensor data at the same time, providing a basis for subsequent correlation analysis. Further correlation analysis based on the first multidimensional data set can further explore the correlation patterns between sensors, and further discover which sensors are close to each other in the multidimensional space, or have a common change tendency in some specific features, thereby providing good support for subsequent data analysis.

[0045] In some embodiments, the data correlation analysis according to the first multidimensional data set to obtain the correlation matrix between the first Lora sensor and the second Lora sensor comprises: obtaining the first data corresponding to the first Lora sensor and the second data corresponding to the second Lora sensor from the first multidimensional data set; calculating the data distance between the first data and the second data at the same time, and establishing an adjacency matrix according to the data distance; obtaining combined data by combining the first data and the second data in any combination, and obtaining the first probability corresponding to the combined data; obtaining the second probability corresponding to the first data in the combined data and the third probability corresponding to the second data in the combined data; determining the correlation matrix between the first Lora sensor and the second Lora sensor according to the adjacency matrix, the first probability, the second probability and the third probability; wherein the correlation matrix is obtained according to the following formula:

[0046] ;

[0047] represents the matrix value corresponding to the correlation matrix in the ith row and the jth column, represents the first probability of the combined data obtained by combining the ith data in the first data and the jth data in the second data; represents the second probability corresponding to the ith data in the first data; represents the third probability corresponding to the jth data in the second data; represents the matrix value corresponding to the adjacency matrix in the ith row and the jth column; represents the weight information between the ith data in the first data and the jth data in the second data.

[0048] Exemplarily, the first data corresponding to the first Lora sensor and the second data corresponding to the second Lora sensor are extracted from the first multi-dimensional data set. The data can be features determined in the clustering process or other data representations. Then, for the first data and the second data at the same time, the data distance between them is calculated. The distance measure can select a suitable method according to the data type, such as Euclidean distance, Manhattan distance, etc.

[0049] Exemplarily, based on the calculated data distance, a neighborhood matrix is established. The neighborhood matrix is used to represent the proximity or distance between two sensors. Generally, the smaller the distance, the closer the sensor pair or the higher the correlation. Thus, the first data and the second data are combined in any way to form combined data.

[0050] Exemplarily, based on statistical analysis, model output or other prior knowledge, a first probability corresponding to each combined data is obtained. The first frequency of the first data in the first multi-dimensional data set corresponding to the combined data is obtained, and then the second probability is obtained according to the first frequency, and the second frequency of the second data in the first multi-dimensional data set corresponding to the combined data is obtained, and then the third probability is obtained according to the second frequency.

[0051] Exemplarily, the correlation matrix between the first Lora sensor and the second Lora sensor is determined according to the neighborhood matrix, the first probability, the second probability and the third probability, the correlation between the data of the first Lora sensor and the second Lora sensor is determined through the correlation matrix, and the correlation matrix is obtained according to the following formula:

[0052] ;

[0053] represents the matrix value corresponding to the i-th row and the j-th column of the correlation matrix, represents the first probability of the combined data obtained by combining the i-th data in the first data and the j-th data in the second data; represents the second probability corresponding to the i-th data in the first data; represents the third probability corresponding to the j-th data in the second data; represents the matrix value corresponding to the i-th row and the j-th column of the neighborhood matrix; represents the weight information between the i-th data in the first data and the j-th data in the second data.

[0054] Specifically, by calculating the data distance between two Lora sensors at the same moment and establishing an adjacency matrix, the relationship between these sensors can be identified and modeled. The adjacency matrix can help understand which sensors are close to each other in space or data, allowing for more refined analysis and optimization. By then arbitrarily combining the first and second data and calculating the corresponding probabilities of the combined data, a more comprehensive understanding of the relationships between the data and their impact can be achieved. This approach helps discover potential patterns and regularities in data combinations. By constructing an association matrix, it provides support for a better understanding of the associations between sensors and a more accurate understanding of the interactions and dependencies between data, thereby improving the accuracy of data processing and analysis.

[0055] Similarly, according to the above steps, the second monitoring data is used to obtain a second association relationship between any two Lora sensors in the plurality of Lora sensors, which will not be described in detail here.

[0056] Step S103: Determine a first analysis result corresponding to the target object according to the first association relationship and the second association relationship.

[0057] Exemplarily, the first association relationship represents the association or similarity between any two Lora sensors at a first moment, and the second association relationship represents the association or similarity between any two Lora sensors at a second moment.

[0058] Exemplarily, when the first correlation between the first Lora sensor and the second Lora sensor is positively correlated at the first moment, and the second correlation between the first Lora sensor and the second Lora sensor is also positively correlated at the second moment, the first analysis result is that the test result of the target object between the first Lora sensor and the second Lora sensor is normal; when the first correlation between the first Lora sensor and the second Lora sensor is positively correlated at the first moment, and the second correlation between the first Lora sensor and the second Lora sensor is negatively correlated at the second moment, the first analysis result is that the test result of the target object between the first Lora sensor and the second Lora sensor is abnormal.

[0059] In some embodiments, determining the first analysis result corresponding to the target object based on the first association relationship and the second association relationship includes: determining a mapping table, obtaining a first numerical value corresponding to the first association relationship based on the mapping table, and obtaining a second numerical value corresponding to the second association relationship based on the mapping table; determining a second data change between the first association relationship and the second association relationship based on the first numerical value and the second numerical value; and determining the first analysis result corresponding to the target object based on the second data change.

[0060] Exemplarily, a mapping table is created to map the first association and the second association to specific numerical values. These mappings can be based on pre-set rules or data analysis results.

[0061] Exemplarily, using the mapping table, the first numerical value and the second numerical value corresponding to the first association and the second association are obtained respectively. These numerical values can be any measure to describe the strength of the relationship or similarity. Thus, based on the obtained first numerical value and the second numerical value, the second data change between them is calculated or inferred. This can be a difference value, a ratio, or other related measure, to represent the change or trend of the relationship.

[0062] Exemplarily, the first analysis result corresponding to the target object is determined using statistical analysis or machine learning methods according to the second data change.

[0063] Specifically, by determining the mapping table and obtaining the numerical values corresponding to the first association and the second association according to the mapping table, complex relationship data can be quantified into standardized numerical values that can be compared and analyzed. This helps to eliminate subjectivity and uncertainty, making subsequent analysis more objective and reliable. Further, based on the first numerical value and the second numerical value, the data change between the association relationships is calculated. This analysis can reveal the evolution trend of the relationship, such as whether it tends to strengthen, weaken or remain stable. Thus, by determining the first analysis result of the target object through the second data change, the impact of the relationship change on the target object can be better understood.

[0064] Step S104, determining the first data change corresponding to the target object according to the first monitoring data and the second monitoring data, and determining the second analysis result corresponding to the target object according to the first data change and a data threshold.

[0065] Exemplarily, the difference (such as difference value, proportional change, etc.) between the first monitoring data and the second monitoring data is calculated to obtain the first data change. According to business requirements or historical data, one or more data thresholds are set. These thresholds are used to determine whether the data change has reached a level that needs attention, which may include a safety range, a performance decline warning line, a fault warning line, etc.

[0066] Exemplarily, the calculated first data change is compared with the set data threshold. According to the comparison result, the second analysis result corresponding to the target object is determined. For example, if the data change exceeds a certain data threshold, it may indicate that the state of the target object has changed significantly, and appropriate measures need to be taken.

[0067] In some embodiments, the first monitoring data includes third sensor data corresponding to a third Lora sensor, and the second monitoring data includes fourth sensor data corresponding to the third Lora sensor, and determining the first data change corresponding to the target object based on the first monitoring data and the second monitoring data includes: splicing the third sensor data and the fourth sensor data to obtain spliced ​​sensor data; determining a sliding window, and obtaining window sensor data corresponding to the spliced ​​sensor data based on the sliding window; calculating a first mean value and a first variance corresponding to the window sensor data; determining a window representation value corresponding to the target object based on the first mean value and the first variance, and determining the first data change based on the window representation value.

[0068] Exemplarily, the first monitoring data includes at least third sensor data corresponding to the third Lora sensor, and the second monitoring data includes at least fourth sensor data corresponding to the third Lora sensor, and then the third sensor data and the fourth sensor data are spliced ​​in the order of collection to form spliced ​​sensor data.

[0069] For example, a sliding window is set to extract window sensor data from the spliced ​​sensor data. The size and step size of the sliding window can be set based on specific requirements, such as the window's time span or the number of data points. Statistical analysis is then performed on the window sensor data within each sliding window to calculate the first mean and first variance of the window sensor data. These statistics help understand the central tendency and distribution range of the data within the window.

[0070] For example, data fusion is performed based on the calculated first mean and first variance to determine a window representation value, which is then used for subsequent analysis and comparison. The window representation value is then used to calculate the first data change corresponding to the target object. This may involve comparing with previous window representation values ​​or using other mathematical models or rules to analyze the change trend between windows.

[0071] Specifically, setting a sliding window and extracting windowed sensor data can make analysis more refined and dynamic. By moving the window over time or a data series, both transient changes and long-term trends in the data can be captured, providing a more accurate basis for analysis. Calculating the mean and variance of the windowed sensor data helps extract statistical features of the window. These features not only summarize the central tendency and distribution range of the data within the window but also provide a quantitative basis for subsequent data change analysis. Based on the windowed representation, data changes of the target object can be calculated. This analysis helps identify and understand temporal or spatial patterns of change in the target object, providing deeper insights and basis for prediction, optimization, and decision-making.

[0072] In some embodiments, determining the window representation value corresponding to the target object based on the first mean and the first variance includes: obtaining the second mean and the second variance corresponding to the previous sliding window; determining the sliding factor corresponding to the sliding window, and fusing the first mean and the second mean according to the sliding factor to determine the target mean; fusing the first variance and the second variance according to the sliding factor to determine the target variance; and using an adjustment factor to fuse the target mean and the target variance to determine the window representation value corresponding to the target object.

[0073] For example, during the window sliding process, the second mean and second variance of the previous sliding window are saved and recorded, and a sliding factor is set, which can be a fixed value or dynamically adjusted according to specific circumstances. The sliding factor determines how the statistical features of the previous window are integrated with the statistical features of the current window.

[0074] For example, a sliding factor is used to linearly or nonlinearly fuse the first average value of the previous window and the first average value of the current window to determine the target average value. This fusion can be performed using a suitable method, such as weighted average or other mathematical models, based on business requirements and data characteristics.

[0075] Similarly, the sliding factor is used to fuse the first variance of the previous window and the first variance of the current window to determine the target variance. This step is similar to the fusion of the average value, and the appropriate fusion method can be selected as needed.

[0076] For example, a tuning factor is used to combine the fused target mean and target variance to form the final window representation value. This tuning factor can be adjusted according to the specific application scenario to achieve the optimal balance or accuracy requirements.

[0077] Specifically, this method enables comprehensive analysis and feature extraction of the window sensor data corresponding to the sliding window, thereby understanding the dynamic characteristics and changing trends of the target object over time. This method not only improves the accuracy and practicality of data analysis, but also provides an important reference for subsequent decision-making and optimization.

[0078] Step S105: Fusing the first analysis result and the second analysis result to determine a target analysis result corresponding to the target object.

[0079] Exemplarily, a logical operation (such as logical AND, logical OR) or a decision tree fusion algorithm is used to merge or operate the first analysis result and the second analysis result according to the selected fusion method to obtain a comprehensive target analysis result.

[0080] In some embodiments, the fusing the first analysis result and the second analysis result to determine the target analysis result corresponding to the target object comprises: obtaining a first analysis probability corresponding to the first analysis result and a second analysis probability corresponding to the second analysis result; fusing the first analysis probability and the second analysis probability to obtain a target analysis probability; and determining the target analysis result corresponding to the target object according to the target analysis probability.

[0081] For example, for the first analysis result, a first analysis probability corresponding to the result is obtained or calculated according to the analysis method or model used. This probability may represent the likelihood or accuracy of the result. Similarly, a second analysis probability corresponding to the second analysis result is calculated.

[0082] For example, a suitable probability fusion method is selected. Common methods include weighted average, product rule, maximum rule, or more complex fusion algorithms such as Dempster-Shafer theory, etc. Then the first analysis probability and the second analysis probability are fused according to the probability fusion method to obtain the target analysis probability.

[0083] For example, a decision threshold is set, which is used to determine whether the target analysis probability meets the standard for taking certain actions or drawing conclusions. The target analysis probability is compared with the decision threshold. If the target analysis probability exceeds the threshold, the target analysis result of the target object is determined according to the pre-set rules or logic. The target analysis result can be a classification label, a prediction value, a decision suggestion, etc., depending on the application scenario and the analysis purpose.

[0084] Specifically, fusing the first analysis probability and the second analysis probability can reduce the possibility of misjudgment of a single analysis result. By considering information from different sources, the target analysis probability obtained is more reliable and robust, which helps to make more reliable decisions or inferences. Thus, determining the target analysis result of the target object according to the fused target analysis probability can provide an important reference for subsequent decision making. This method makes the decision more data and probability-based, reduces the influence of subjective judgment, and helps to improve the accuracy and effectiveness of the decision.

[0085] Embodiments of the present invention provide a method and related apparatus for analyzing indoor environmental data. The method includes obtaining first monitoring data corresponding to a first moment and second monitoring data corresponding to a second moment of a target object monitored by multiple LoRa sensors. The method then analyzes the first monitoring data to determine a first correlation between any two LoRa sensors, and analyzes the second monitoring data to determine a second correlation between any two LoRa sensors. This helps reveal the relationships and functions between different sensors and helps understand the influence and dependencies between sensors in a monitoring network. The method then determines a first analysis result for the target object based on the first and second correlations. This helps detect significant changes or anomalies in the target object. Furthermore, by analyzing the first and second monitoring data, the data change of the target object can be calculated, and a second analysis result for the target object can be determined based on a data threshold. This helps promptly detect data changes and identify potential problems or risks. The first and second analysis results are then integrated to determine a target analysis result for the target object, providing a more comprehensive understanding of the target object's status and supporting subsequent decision-makers in making accurate judgments and decisions. The method also enhances the monitoring and analysis capabilities of target object status changes, facilitating timely warnings of potential problems and the implementation of targeted measures, thereby improving the safety of the target object. It also solves the problem that the related technology lacks the connection between Lora sensors, so the synergistic effect of Lora sensors cannot be exerted, resulting in poor analysis results, further increasing the safety risk.

[0086] See also Figure 2 , Figure 2An indoor environment data analysis device 200 is provided in the embodiments of the present application, and the indoor environment data analysis device 200 comprises a data collection module 201, an association analysis module 202, a first analysis module 203, a second analysis module 204, and a target analysis module 205. The data collection module 201 is configured to obtain first monitoring data corresponding to a first time and second monitoring data corresponding to a second time of a target object under monitoring of a plurality of Lora sensors. The association analysis module 202 is configured to obtain a first association relationship between any two Lora sensors in the plurality of Lora sensors according to the first monitoring data, and obtain a second association relationship between any two Lora sensors in the plurality of Lora sensors according to the second monitoring data. The first analysis module 203 is configured to determine a first analysis result corresponding to the target object according to the first association relationship and the second association relationship. The second analysis module 204 is configured to determine a first data change corresponding to the target object according to the first monitoring data and the second monitoring data, and determine a second analysis result corresponding to the target object according to the first data change and a data threshold. The target analysis module 205 is configured to determine a target analysis result corresponding to the target object by fusing the first analysis result and the second analysis result.

[0087] In some embodiments, the first monitoring data at least comprises first sensing data corresponding to a first Lora sensor and second sensing data corresponding to a second Lora sensor, and the association analysis module 202 performs the following in the process of obtaining the first association relationship between any two Lora sensors in the plurality of Lora sensors according to the first monitoring data:

[0088] performing data clustering on the first sensing data and the second sensing data to obtain a first multi-dimensional data set;

[0089] performing data association analysis according to the first multi-dimensional data set to obtain an association matrix between the first Lora sensor and the second Lora sensor;

[0090] determining the first association relationship between the first Lora sensor and the second Lora sensor according to the association matrix.

[0091] In some embodiments, the association analysis module 202 performs the following in the process of performing data association analysis according to the first multi-dimensional data set to obtain the association matrix between the first Lora sensor and the second Lora sensor:

[0092] obtaining first data corresponding to the first Lora sensor and second data corresponding to the second Lora sensor from the first multi-dimensional data set;

[0093] Calculating a data distance between the first data and the second data at the same time, and establishing an adjacency matrix according to the data distance;

[0094] Arbitrarily combining the first data and the second data to obtain combined data, and obtaining a first probability corresponding to the combined data;

[0095] Obtaining a second probability corresponding to the first data in the combined data and a third probability corresponding to the second data in the combined data;

[0096] determining the association matrix between the first LoRa sensor and the second LoRa sensor based on the adjacency matrix, the first probability, the second probability, and the third probability;

[0097] The correlation matrix is ​​obtained according to the following formula:

[0098] ;

[0099] represents the matrix value corresponding to the incidence matrix in row i and column j, represents the first probability of the combined data obtained by combining the i-th data in the first data and the j-th data in the second data; represents the second probability corresponding to the i-th data in the first data; represents the third probability corresponding to the j-th data in the second data; Represents the matrix value corresponding to the adjacent matrix in the i-th row and j-th column; Represents the weight information between the i-th data in the first data and the j-th data in the second data.

[0100] In some implementations, during the process of determining the first analysis result corresponding to the target object according to the first association relationship and the second association relationship, the first analysis module 203 performs:

[0101] Determine a mapping table, obtain a first value corresponding to the first association relationship according to the mapping table, and obtain a second value corresponding to the second association relationship according to the mapping table;

[0102] determining a second data change between the first association relationship and the second association relationship according to the first value and the second value;

[0103] The first analysis result corresponding to the target object is determined according to the second data change.

[0104] In some embodiments, the first monitoring data includes third sensor data corresponding to a third LoRa sensor, and the second monitoring data includes fourth sensor data corresponding to the third LoRa sensor. During the process of determining the first data change corresponding to the target object based on the first monitoring data and the second monitoring data, the second analysis module 204 performs:

[0105] performing data splicing on the third sensor data and the fourth sensor data to obtain spliced ​​sensor data;

[0106] Determine a sliding window, and obtain window sensor data corresponding to the spliced ​​sensor data according to the sliding window;

[0107] Calculating a first mean value and a first variance corresponding to the window sensing data;

[0108] A window representation value corresponding to the target object is determined according to the first mean value and the first variance, and the first data change is determined according to the window representation value.

[0109] In some implementations, during the process of determining the window representation value corresponding to the target object according to the first mean value and the first variance, the second analysis module 204 performs:

[0110] Obtain a second mean value and a second variance corresponding to the previous sliding window;

[0111] Determining a sliding factor corresponding to the sliding window, and fusing the first average value and the second average value according to the sliding factor to determine a target average value;

[0112] Determine a target variance by fusing the first variance and the second variance according to the sliding factor;

[0113] The target mean value and the target variance are fused using an adjustment factor to determine the window representation value corresponding to the target object.

[0114] In some implementations, during the process of fusing the first analysis result and the second analysis result to determine the target analysis result corresponding to the target object, the target analysis module 205 performs:

[0115] Obtaining a first analysis probability corresponding to the first analysis result and a second analysis probability corresponding to the second analysis result;

[0116] fusing the first analysis probability and the second analysis probability to obtain a target analysis probability;

[0117] The target analysis result corresponding to the target object is determined according to the target analysis probability.

[0118] In some implementations, the indoor environment data analysis device 200 may be applied to a terminal device.

[0119] It should be noted that, those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the indoor environment data analysis device 200 described above can refer to the corresponding process in the aforementioned indoor environment data analysis method embodiment, and will not be repeated here.

[0120] See also Figure 3 , Figure 3 A schematic block diagram of the structure of a terminal device provided in an embodiment of the present invention.

[0121] like Figure 3 As shown, the terminal device 300 includes a processor 301 and a memory 302 , and the processor 301 and the memory 302 are connected via a bus 303 , such as an I 2 C (Inter-Integrated Circuit) bus.

[0122] Specifically, processor 301 is used to provide computing and control capabilities to support the operation of the entire terminal device. Processor 301 can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0123] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0124] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the embodiment of the present invention, and does not constitute a limitation on the terminal device to which the embodiment of the present invention is applied. The specific server may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0125] The processor is configured to run a computer program stored in the memory and implement the indoor environment data analysis method provided by any one of the embodiments of the application when the computer program is executed.

[0126] In an embodiment, the processor is configured to run a computer program stored in the memory and implement the following steps when the computer program is executed:

[0127] obtain first monitoring data corresponding to a first time and second monitoring data corresponding to a second time of the target object under monitoring of the plurality of Lora sensors;

[0128] obtain a first correlation relationship between any two Lora sensors of the plurality of Lora sensors according to the first monitoring data, and obtain a second correlation relationship between any two Lora sensors of the plurality of Lora sensors according to the second monitoring data;

[0129] determine a first analysis result corresponding to the target object according to the first correlation relationship and the second correlation relationship;

[0130] determine a first data change corresponding to the target object according to the first monitoring data and the second monitoring data, and determine a second analysis result corresponding to the target object according to the first data change and a data threshold;

[0131] determine a target analysis result corresponding to the target object by fusing the first analysis result and the second analysis result.

[0132] In some embodiments, the first monitoring data at least includes first sensing data corresponding to a first Lora sensor and second sensing data corresponding to a second Lora sensor, and the processor 301, in the process of obtaining a first correlation relationship between any two Lora sensors of the plurality of Lora sensors according to the first monitoring data, performs:

[0133] performing data clustering on the first sensing data and the second sensing data to obtain a first multi-dimensional data set;

[0134] performing data correlation analysis according to the first multi-dimensional data set to obtain a correlation matrix between the first Lora sensor and the second Lora sensor;

[0135] determining a first correlation relationship between the first Lora sensor and the second Lora sensor according to the correlation matrix.

[0136] In some embodiments, the processor 301, in the process of performing data correlation analysis according to the first multi-dimensional data set to obtain a correlation matrix between the first Lora sensor and the second Lora sensor, performs:

[0137] Obtaining first data corresponding to the first LoRa sensor and second data corresponding to the second LoRa sensor from the first multidimensional dataset;

[0138] Calculating a data distance between the first data and the second data at the same time, and establishing an adjacency matrix according to the data distance;

[0139] Arbitrarily combining the first data and the second data to obtain combined data, and obtaining a first probability corresponding to the combined data;

[0140] Obtaining a second probability corresponding to the first data in the combined data and a third probability corresponding to the second data in the combined data;

[0141] determining the association matrix between the first LoRa sensor and the second LoRa sensor based on the adjacency matrix, the first probability, the second probability, and the third probability;

[0142] The correlation matrix is ​​obtained according to the following formula:

[0143] ;

[0144] represents the matrix value corresponding to the incidence matrix in row i and column j, represents the first probability of the combined data obtained by combining the i-th data in the first data and the j-th data in the second data; represents the second probability corresponding to the i-th data in the first data; represents the third probability corresponding to the j-th data in the second data; Represents the matrix value corresponding to the adjacent matrix in the i-th row and j-th column; Represents the weight information between the i-th data in the first data and the j-th data in the second data.

[0145] In some implementations, during the process of determining the first analysis result corresponding to the target object according to the first association relationship and the second association relationship, the processor 301 executes:

[0146] Determine a mapping table, obtain a first value corresponding to the first association relationship according to the mapping table, and obtain a second value corresponding to the second association relationship according to the mapping table;

[0147] determining a second data change between the first association relationship and the second association relationship according to the first value and the second value;

[0148] The first analysis result corresponding to the target object is determined according to the second data change.

[0149] In some embodiments, the first monitoring data includes third sensor data corresponding to a third Lora sensor, and the second monitoring data includes fourth sensor data corresponding to the third Lora sensor. During the process of determining a first data change corresponding to the target object based on the first monitoring data and the second monitoring data, the processor 301 executes:

[0150] performing data splicing on the third sensor data and the fourth sensor data to obtain spliced ​​sensor data;

[0151] Determine a sliding window, and obtain window sensor data corresponding to the spliced ​​sensor data according to the sliding window;

[0152] Calculating a first mean value and a first variance corresponding to the window sensing data;

[0153] A window representation value corresponding to the target object is determined according to the first mean value and the first variance, and the first data change is determined according to the window representation value.

[0154] In some implementations, during the process of determining the window representation value corresponding to the target object according to the first mean value and the first variance, the processor 301 executes:

[0155] Obtain a second mean value and a second variance corresponding to the previous sliding window;

[0156] Determining a sliding factor corresponding to the sliding window, and fusing the first average value and the second average value according to the sliding factor to determine a target average value;

[0157] Determine a target variance by fusing the first variance and the second variance according to the sliding factor;

[0158] The target mean value and the target variance are fused using an adjustment factor to determine the window representation value corresponding to the target object.

[0159] In some implementations, during the process of fusing the first analysis result and the second analysis result to determine the target analysis result corresponding to the target object, the processor 301 executes:

[0160] Obtaining a first analysis probability corresponding to the first analysis result and a second analysis probability corresponding to the second analysis result;

[0161] fusing the first analysis probability and the second analysis probability to obtain a target analysis probability;

[0162] The target analysis result corresponding to the target object is determined according to the target analysis probability.

[0163] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the terminal device described above can refer to the corresponding process in the aforementioned indoor environment data analysis method embodiment, and will not be repeated here.

[0164] An embodiment of the present invention also provides a storage medium for computer-readable storage, wherein the storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of any indoor environment data analysis method provided in the description of the embodiment of the present invention.

[0165] The storage medium may be an internal storage unit of the terminal device in the aforementioned embodiment, such as a hard disk or memory of the terminal device. The storage medium may also be an external storage device of the terminal device, such as a plug-in hard disk equipped on the terminal device, a smart memory card (SMC), a secure digital (SD) card, a flash card, etc.

[0166] Those skilled in the art will appreciate that all or some of the steps, systems, and functional modules / units in the methods, systems, and devices disclosed above may be implemented as software, firmware, hardware, or any combination thereof. In hardware embodiments, the division between functional modules / units described above does not necessarily correspond to the division between physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media encompasses both volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, as is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0167] It should be understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system that includes a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements that are inherent to such process, method, article or system. In the absence of further limitations, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system that includes the element.

[0168] The serial numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. The above are only specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be based on the scope of protection of the claims.

Claims

1. A method for analyzing indoor environment data, characterized in that: The method comprises: Obtaining first monitoring data corresponding to a first moment and second monitoring data corresponding to a second moment of a target object under monitoring by multiple Lora sensors; Obtaining a first association relationship between any two Lora sensors among the plurality of Lora sensors based on the first monitoring data, and obtaining a second association relationship between any two Lora sensors among the plurality of Lora sensors based on the second monitoring data; Determine a first analysis result corresponding to the target object according to the first association relationship and the second association relationship; Determining a first data change corresponding to the target object according to the first monitoring data and the second monitoring data, and determining a second analysis result corresponding to the target object according to the first data change and a data threshold; fusing the first analysis result and the second analysis result to determine a target analysis result corresponding to the target object; The first monitoring data includes third sensor data corresponding to a third Lora sensor, the second monitoring data includes fourth sensor data corresponding to the third Lora sensor, and determining a first data change corresponding to the target object based on the first monitoring data and the second monitoring data includes: performing data splicing on the third sensor data and the fourth sensor data to obtain spliced ​​sensor data; Determine a sliding window, and obtain window sensor data corresponding to the spliced ​​sensor data according to the sliding window; Calculating a first mean value and a first variance corresponding to the window sensing data; determining a window representation value corresponding to the target object according to the first mean value and the first variance, and determining the first data change according to the window representation value; The determining of the window representation value corresponding to the target object according to the first mean value and the first variance includes: Obtain a second mean value and a second variance corresponding to the previous sliding window; Determining a sliding factor corresponding to the sliding window, and fusing the first average value and the second average value according to the sliding factor to determine a target average value; Determine a target variance by fusing the first variance and the second variance according to the sliding factor; The target mean value and the target variance are fused using an adjustment factor to determine the window representation value corresponding to the target object.

2. The method according to claim 1, characterized in that The first monitoring data includes at least first sensor data corresponding to the first Lora sensor and second sensor data corresponding to the second Lora sensor, and obtaining a first association relationship between any two Lora sensors from the plurality of Lora sensors based on the first monitoring data includes: performing data clustering on the first sensor data and the second sensor data to obtain a first multidimensional data set; Performing data association analysis on the first multidimensional data set to obtain an association matrix between the first Lora sensor and the second Lora sensor; A first association relationship between the first LoRa sensor and the second LoRa sensor is determined according to the association matrix.

3. The method according to claim 2, characterized in that The performing data association analysis according to the first multidimensional data set to obtain an association matrix between the first LoRa sensor and the second LoRa sensor includes: Obtaining first data corresponding to the first LoRa sensor and second data corresponding to the second LoRa sensor from the first multidimensional dataset; Calculating a data distance between the first data and the second data at the same time, and establishing an adjacency matrix according to the data distance; Arbitrarily combining the first data and the second data to obtain combined data, and obtaining a first probability corresponding to the combined data; Obtaining a second probability corresponding to the first data in the combined data and a third probability corresponding to the second data in the combined data; determining the association matrix between the first LoRa sensor and the second LoRa sensor based on the adjacency matrix, the first probability, the second probability, and the third probability; The correlation matrix is ​​obtained according to the following formula: ; represents the matrix value corresponding to the incidence matrix in row i and column j, represents the first probability of the combined data obtained by combining the i-th data in the first data and the j-th data in the second data; represents the second probability corresponding to the i-th data in the first data; represents the third probability corresponding to the j-th data in the second data; Represents the matrix value corresponding to the adjacent matrix in the i-th row and j-th column; Represents the weight information between the i-th data in the first data and the j-th data in the second data.

4. The method according to claim 1, wherein The determining a first analysis result corresponding to the target object according to the first association relationship and the second association relationship includes: Determine a mapping table, obtain a first value corresponding to the first association relationship according to the mapping table, and obtain a second value corresponding to the second association relationship according to the mapping table; determining a second data change between the first association relationship and the second association relationship according to the first value and the second value; The first analysis result corresponding to the target object is determined according to the second data change.

5. The method according to claim 1, wherein The fusing the first analysis result and the second analysis result to determine the target analysis result corresponding to the target object includes: Obtaining a first analysis probability corresponding to the first analysis result and a second analysis probability corresponding to the second analysis result; fusing the first analysis probability and the second analysis probability to obtain a target analysis probability; The target analysis result corresponding to the target object is determined according to the target analysis probability.

6. An indoor environment data analysis device, characterized in that: include: A data acquisition module is used to obtain first monitoring data corresponding to a first moment and second monitoring data corresponding to a second moment of a target object under monitoring by multiple Lora sensors; an association analysis module, configured to obtain a first association relationship between any two Lora sensors among the plurality of Lora sensors based on the first monitoring data, and to obtain a second association relationship between any two Lora sensors among the plurality of Lora sensors based on the second monitoring data; A first analysis module, configured to determine a first analysis result corresponding to the target object according to the first association relationship and the second association relationship; The second analysis module is configured to determine a first data change corresponding to the target object based on the first monitoring data and the second monitoring data, and determine a second analysis result corresponding to the target object based on the first data change and a data threshold; wherein the first monitoring data includes third sensor data corresponding to a third Lora sensor, and the second monitoring data includes fourth sensor data corresponding to the third Lora sensor, and the determining of the first data change corresponding to the target object based on the first monitoring data and the second monitoring data includes: splicing the third sensor data and the fourth sensor data to obtain spliced ​​sensor data; determining a sliding window, and obtaining window sensor data corresponding to the spliced ​​sensor data according to the sliding window; and calculating the window a first mean value and a first variance corresponding to the oral sensor data; determining a window representation value corresponding to the target object based on the first mean value and the first variance, and determining the first data change based on the window representation value; wherein, determining the window representation value corresponding to the target object based on the first mean value and the first variance comprises: obtaining a second mean value and a second variance corresponding to the previous sliding window; determining a sliding factor corresponding to the sliding window, and fusing the first mean value and the second mean value to determine a target mean value based on the sliding factor; fusing the first variance and the second variance to determine a target variance based on the sliding factor; and fusing the target mean value and the target variance using an adjustment factor to determine the window representation value corresponding to the target object; The target analysis module is used to fuse the first analysis result and the second analysis result to determine the target analysis result corresponding to the target object.

7. A terminal device, characterized in that: The terminal device includes a processor and a memory; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the indoor environment data analysis method according to any one of claims 1 to 5 when executing the computer program.

8. A computer storage medium for computer storage, characterized in that: The computer storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps of the indoor environment data analysis method according to any one of claims 1 to 5.

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