Dynamic environment monitoring method and system for integrated barrier

Abstract

The invention provides a dynamic environment monitoring method and system for an integrated barrier, and relates to the technical field of environment monitoring, and the method comprises the steps: constructing a confidence matrix between monitoring points, carrying out the dependence association of environment monitoring data of each monitoring point according to the confidence matrix, and obtaining a monitoring dependence relation between all monitoring points; determining the difference gradient of the environment state between the monitoring points in each monitoring area, and performing clustering projection on the environment monitoring data of each monitoring area through each difference gradient and the monitoring dependency relationship to obtain the local state quantity of the environment of each monitoring area; determining a mutual interference coefficient of the sensor elements between the monitoring points according to the extracted environmental state characteristics and the monitoring dependency relationship; and carrying out spatial hierarchical coupling on the environment state of the closed space in the control cabinet through the mutual interference coefficient and all the local state quantities to obtain a global state quantity. According to the invention, coupling estimation of the working environment state of the integrated barrier can be realized, so that the accuracy of environment monitoring is improved.

Description

Technical Field

[0001] The present application relates to the field of environmental monitoring technology, and more specifically, to a dynamic environmental monitoring method and system for an integrated barrier machine. Background Art

[0002] With the continuous development of modern urban construction and traffic management, environmental monitoring technology is being used more and more widely in smart cities, especially in the management and maintenance of traffic facilities. As an important part of traffic control facilities, integrated barrier machines are often used to control the passage of vehicles and pedestrians. The working environment of integrated barrier machines is often affected by environmental factors such as temperature and humidity, resulting in unstable operation of the equipment or reduced accuracy. By combining advanced environmental monitoring technology with integrated barrier machines, the environmental conditions around the barrier machines can be monitored and evaluated in real time, and early warning can be issued.

[0003] In existing environmental monitoring technologies, environmental monitoring relies on decentralized sensors for data collection. This approach makes it difficult to ensure accurate monitoring of local and global environmental conditions. Due to the limited placement of sensors, a single sensor can only provide environmental information within a limited range and cannot reflect environmental changes in the entire space. In addition, the lack of effective correlation analysis between monitoring points results in isolated environmental information between each point, making it difficult to form a complete environmental status assessment. Moreover, the environment in a confined space is highly dynamic. For example, factors such as temperature and humidity may change significantly over time and with the operating status of the equipment. Traditional single-point monitoring methods are unable to capture these subtle changes in a timely manner. This lack of monitoring accuracy will directly affect the operating performance of the integrated barrier machine. Therefore, how to achieve coupled estimation of the working environment status of the integrated barrier machine and thus improve the accuracy of environmental monitoring has become a difficult problem facing the industry. Summary of the Invention

[0004] The present application provides a dynamic environment monitoring method and system for an integrated barrier machine, which can realize coupled estimation of the working environment state of the integrated barrier machine, thereby improving the accuracy of environmental monitoring.

[0005] In a first aspect, the present application provides a dynamic environment monitoring method for an integrated barrier machine, the dynamic environment monitoring method comprising the following steps: Collect environmental monitoring data from multiple monitoring points in the confined space of the integrated barrier machine's control cabinet through sensor elements; Constructing a confidence matrix between all monitoring points based on the distances between the monitoring points, and then performing dependency association on the environmental monitoring data of each monitoring point based on the confidence matrix to obtain a monitoring dependency relationship between all monitoring points; Dividing the confined space into a plurality of monitoring areas according to the location information of each monitoring point, and then determining the difference gradient of the environmental state between the monitoring points in each monitoring area, clustering and projecting the environmental monitoring data of each monitoring area according to each difference gradient and the monitoring dependency relationship, and obtaining the local state quantity of the environment in each monitoring area; Extracting environmental state characteristics at each monitoring point, and determining mutual interference coefficients of sensor elements between monitoring points based on all environmental state characteristics and the monitoring dependency relationship; The environmental state of the enclosed space in the control cabinet is spatially hierarchically coupled through the mutual interference coefficient and all local state quantities to obtain a global state quantity, which is then used as the state quantity of the working environment of the integrated barrier machine.

[0006] Preferably, constructing a confidence matrix between all monitoring points based on the distances between the monitoring points specifically includes: Collect historical environmental monitoring data from multiple monitoring points in the confined space of the control cabinet; Get the distance between each monitoring point; Determine the similarity of monitoring data between each monitoring point through all historical environmental monitoring data and the distance between each monitoring point; The confidence matrix between all monitoring points is constructed based on the similarity of the monitoring data between each monitoring point.

[0007] Preferably, performing dependency association on the environmental monitoring data of each monitoring point according to the confidence matrix to obtain the monitoring dependency relationship between all monitoring points specifically includes: Performing dependency correlation analysis on the environmental monitoring data of each monitoring point using the confidence matrix to obtain the dependency correlation between the data of each monitoring point; A dependency matrix between all monitoring points is constructed according to all dependency correlations, and the monitoring dependency relationship between all monitoring points is further described by the dependency matrix.

[0008] Preferably, determining the difference gradient of environmental conditions between monitoring points in each monitoring area specifically includes: For each monitoring area, obtain environmental monitoring data of all monitoring points in the monitoring area; Determine the environmental status of the corresponding monitoring point based on the environmental monitoring data of each monitoring point; The difference gradient of environmental conditions between monitoring points in the monitoring area is determined based on the distribution differences of all environmental conditions.

[0009] Preferably, clustering and projecting the environmental monitoring data of each monitoring area by each difference gradient and the monitoring dependency relationship to obtain the local state quantity of the environment of each monitoring area specifically includes: For each monitoring area, obtain environmental monitoring data of all monitoring points in the monitoring area; Correcting the environmental monitoring data of all monitoring points in the monitoring area according to the difference gradient corresponding to the monitoring area and the monitoring dependency relationship to obtain corrected environmental monitoring data; Cluster the corrected environmental monitoring data based on the K-means clustering algorithm, and project the clustering results into a low-dimensional state space; The local state quantity of the monitoring area environment is extracted from the low-dimensional state space.

[0010] Preferably, determining the mutual interference coefficient of sensor elements between monitoring points according to all environmental state characteristics and the monitoring dependency specifically includes: Determine the characteristic variance through all environmental state characteristics; Regression analysis is performed on all environmental state features based on the monitoring dependency and the feature variance to determine mutual interference coefficients of sensor elements between monitoring points.

[0011] Preferably, performing spatial hierarchical coupling on the environmental state of the enclosed space in the control cabinet by using the mutual interference coefficient and all local state quantities to obtain the global state quantity specifically includes: Compensating all local state quantities by using the mutual interference coefficient to obtain compensated local state quantities; The spatial hierarchical analysis method is used to spatially couple all compensated local state quantities to obtain a global state quantity that reflects the overall environmental state.

[0012] Preferably, the integrated automatic barrier machine is a new multifunctional and highly integrated automatic barrier machine.

[0013] Preferably, the sensor element includes a temperature sensor and a humidity sensor.

[0014] In a second aspect, the present application provides a dynamic environment monitoring system for an integrated barrier machine, for executing a dynamic environment monitoring method for an integrated barrier machine, the dynamic environment monitoring system comprising: A data acquisition module, used to collect environmental monitoring data of multiple monitoring points in the confined space of the control cabinet of the integrated barrier machine through sensor elements; A data processing module is used to construct a confidence matrix between all monitoring points based on the distance between each monitoring point, and then perform dependency association on the environmental monitoring data of each monitoring point based on the confidence matrix to obtain a monitoring dependency relationship between all monitoring points; A local state assessment module is used to divide the confined space into multiple monitoring areas based on the location information of each monitoring point, and then determine the difference gradient of the environmental state between the monitoring points in each monitoring area. The environmental monitoring data of each monitoring area is clustered and projected based on each difference gradient and the monitoring dependency relationship to obtain the local state quantity of the environment in each monitoring area; An interference assessment module is used to extract environmental state characteristics at each monitoring point and determine the mutual interference coefficient of sensor elements between monitoring points based on all environmental state characteristics and the monitoring dependency relationship; The global state evaluation module is used to perform spatial hierarchical coupling on the environmental state of the enclosed space in the control cabinet through the mutual interference coefficient and all local state quantities to obtain a global state quantity, and then use the global state quantity as the state quantity of the working environment of the integrated barrier machine.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: Environmental monitoring data of multiple monitoring points in the confined space of the control cabinet of the integrated barrier machine are collected through sensor elements; a confidence matrix between all monitoring points is constructed according to the distance between each monitoring point, and then the environmental monitoring data of each monitoring point are dependently associated according to the confidence matrix to obtain the monitoring dependency relationship between all monitoring points; the confined space is divided into multiple monitoring areas according to the position information of each monitoring point, and then the difference gradient of the environmental state between the monitoring points in each monitoring area is determined, and the environmental monitoring data of each monitoring area are clustered and projected according to each difference gradient and the monitoring dependency relationship to obtain the local state quantity of the environment of each monitoring area; the environmental state characteristics at each monitoring point are extracted, and the mutual interference coefficient of the sensor elements between the monitoring points is determined according to all the environmental state characteristics and the monitoring dependency relationship; the environmental state of the confined space in the control cabinet is spatially hierarchically coupled according to the mutual interference coefficient and all local state quantities to obtain a global state quantity, and then the global state quantity is used as the state quantity of the working environment of the integrated barrier machine.

[0016] It can be seen that the present application performs spatial hierarchical coupling on the environmental state of the confined space in the control cabinet through the mutual interference coefficient and all local state quantities to obtain a global state quantity, and then uses the global state quantity as the state quantity of the working environment of the integrated barrier machine; wherein, by analyzing the mutual interference coefficient of the sensor elements and combining the local state quantities of each monitoring area, the environmental state of the confined space in the control cabinet is spatially coupled and estimated, and finally the global state quantity is obtained, which effectively solves the measurement error problem caused by interference between sensors and improves the accuracy of the monitoring data; at the same time, the spatial hierarchical coupling analysis can integrate the local environmental states of each area into a unified global state, thereby comprehensively reflecting the environmental changes in the entire confined space. This global state quantity is used to monitor the working environment of the integrated barrier machine in real time, so that the system can grasp the operating environment of the equipment more accurately, improve the overall accuracy and reliability of monitoring, and effectively avoid the limitations of local monitoring; in summary, the present application scheme can realize the coupled estimation of the working environment state of the integrated barrier machine, thereby improving the accuracy of environmental monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0018] Figure 1 is a flow chart of a dynamic environment monitoring method for an integrated barrier machine provided in the present application; Figure 2 This is a module structure diagram of a dynamic environment monitoring system for an integrated barrier machine provided in this application. DETAILED DESCRIPTION

[0019] The following will be combined with the accompanying drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0020] The embodiment of the present application provides a dynamic environment monitoring method and system for an integrated barrier machine. The core of the method is to collect environmental monitoring data of multiple monitoring points in the control cabinet of the integrated barrier machine through sensor elements; construct a confidence matrix between all monitoring points based on the distance between each monitoring point, and then perform dependency association on the environmental monitoring data of each monitoring point based on the confidence matrix to obtain a monitoring dependency relationship between all monitoring points; divide the enclosed space into multiple monitoring areas based on the position information of each monitoring point, and then determine the difference gradient of the environmental state between the monitoring points in each monitoring area, cluster and project the environmental monitoring data of each monitoring area based on each difference gradient and the monitoring dependency relationship to obtain a local state quantity of the environment in each monitoring area; extract the environmental state characteristics at each monitoring point, and determine the mutual interference coefficient of the sensor elements between the monitoring points based on all the environmental state characteristics and the monitoring dependency relationship; perform spatial hierarchical coupling on the environmental state of the enclosed space in the control cabinet through the mutual interference coefficient and all local state quantities to obtain a global state quantity, and then use the global state quantity as the state quantity of the working environment of the integrated barrier machine. The above scheme can realize the coupled estimation of the working environment state of the integrated barrier machine, thereby improving the accuracy of environmental monitoring.

[0021] Example 1 In order to better understand the above technical solution, the following will be described in detail with reference to the accompanying drawings and specific implementation methods. Figure 1 As shown in FIG. 1 , this figure is an exemplary flow chart of a dynamic environment monitoring method for an integrated barrier machine according to this embodiment of the present application. The dynamic environment monitoring method includes the following steps: In step S1, environmental monitoring data of multiple monitoring points in the enclosed space of the control cabinet of the integrated barrier machine are collected through sensor elements.

[0022] It should be noted that the model of the integrated barrier machine in this application is MC710A-4F. The integrated automatic barrier machine is a new type of multifunctional highly integrated automatic barrier machine, which integrates lane control machine, high-speed barrier, fee display, license plate recognition, snapshot fill light, voice quotation, traffic light display, dynamic environment monitoring alarm, etc.

[0023] In specific implementation, multiple sensor elements are arranged on the top, bottom and both sides of the control cabinet, and the sensor elements include temperature sensors and humidity sensors. The sensor elements are used as monitoring points, and the environmental data of the enclosed space in the control cabinet is collected through the arranged sensor elements. The collected environmental data is used as environmental monitoring data, and the data is transmitted to the central controller through a wireless module, thereby completing the collection of environmental monitoring data. It should be noted that the environmental monitoring data in this application includes temperature data and humidity data, and may also include other data in other embodiments, which is not limited here.

[0024] It should be noted that by arranging sensors at multiple points in this application, the environmental information inside the control cabinet can be fully captured. For example, during a certain period of time, the temperature at the top increases and the humidity at the bottom increases. This helps to identify potential condensation risks or overheating problems, so as to adjust environmental control measures in a timely manner to avoid equipment damage.

[0025] In step S2, a confidence matrix between all monitoring points is constructed according to the distance between each monitoring point, and then dependency association is performed on the environmental monitoring data of each monitoring point according to the confidence matrix to obtain a monitoring dependency relationship between all monitoring points.

[0026] In this embodiment, the confidence matrix between all monitoring points is constructed based on the distance between each monitoring point, which can be achieved by the following steps: Collect historical environmental monitoring data from multiple monitoring points in the confined space of the control cabinet; Get the distance between each monitoring point; Determine the similarity of monitoring data between each monitoring point through all historical environmental monitoring data and the distance between each monitoring point; The confidence matrix between all monitoring points is constructed based on the similarity of the monitoring data between each monitoring point.

[0027] In the specific implementation, first, the environmental monitoring data of multiple monitoring points in the enclosed space of the control cabinet in the historical time period are collected through sensor elements, and all the collected environmental monitoring data are used as historical environmental monitoring data; secondly, the distance between each monitoring point is measured; then, the Euclidean distance can be used to evaluate the correlation of the historical environmental monitoring data between the two monitoring points, and then the product of the distance and the correlation between the two monitoring points is used as the similarity of the monitoring data between the two monitoring points, and then the similarity of the monitoring data between each monitoring point is obtained; finally, a monitoring point is selected as the selected monitoring point, and the average value of the similarity of the monitoring data between the selected monitoring point and other monitoring points is used as the confidence of the selected monitoring point, and then the confidence of all monitoring points is obtained, and then the confidence is arranged into a matrix according to the position of the corresponding monitoring point, and the matrix is ​​used as the confidence matrix between all monitoring points.

[0028] In this embodiment, the environmental monitoring data of each monitoring point is subjected to dependency association based on the confidence matrix to obtain the monitoring dependency relationship between all monitoring points. The following steps can be used to achieve this: Performing dependency correlation analysis on the environmental monitoring data of each monitoring point using the confidence matrix to obtain the dependency correlation between the data of each monitoring point; A dependency matrix between all monitoring points is constructed according to all dependency correlations, and the monitoring dependency relationship between all monitoring points is further described by the dependency matrix.

[0029] It should be noted that dependency correlation refers to the degree of relationship between data changes at two monitoring points, which is usually measured through correlation analysis or dependency analysis algorithms. A high dependency correlation means that data changes at one monitoring point can well predict changes at another monitoring point.

[0030] In the specific implementation, first, the confidence matrix contains the similarity information between each monitoring point, which can be used as basic data to judge the potential dependency relationship between the monitoring points, and then the correlation between the environmental monitoring data of each monitoring point is quantified by the Pearson correlation coefficient, and then the product of the potential dependency and the correlation is used as the dependency correlation of the data between the corresponding monitoring points; then, a monitoring point is selected as the selected monitoring point, and the average value of the dependency correlation of the data between the selected monitoring point and other monitoring points is used as the average dependency correlation of the selected monitoring point, and then the average dependency correlation of all monitoring points is obtained, and then the average dependency correlation is arranged into a matrix according to the position of the corresponding monitoring point, and the matrix is ​​used as the dependency matrix between all monitoring points, and then the monitoring dependency relationship between all monitoring points is described by the dependency matrix.

[0031] It should be noted that in this application, by combining the similarity information in the confidence matrix, the environmental monitoring data of each monitoring point is subjected to dependency correlation analysis, the data dependency relationship between the monitoring points is quantified and the dependency correlation is obtained. This process provides an important basis for subsequent data integration and anomaly detection.

[0032] In step S3, the confined space is divided into multiple monitoring areas according to the location information of each monitoring point, and then the difference gradient of the environmental state between the monitoring points in each monitoring area is determined. The environmental monitoring data of each monitoring area are clustered and projected through each difference gradient and the monitoring dependency relationship to obtain the local state quantity of the environment in each monitoring area.

[0033] It should be noted that in this application, dividing the confined space into multiple monitoring areas according to the location information of each monitoring point means dividing the entire confined space into several smaller areas by analyzing the actual positions of the monitoring points in the space, so as to carry out environmental monitoring and analysis more accurately, obtain the environmental monitoring data of all monitoring points in the monitoring area, and decompose them into different areas. The monitoring points in each area are responsible for monitoring the environmental conditions of the area.

[0034] In this embodiment, determining the difference gradient of the environmental conditions between monitoring points in each monitoring area can be achieved by using the following steps: For each monitoring area, obtain environmental monitoring data of all monitoring points in the monitoring area; Determine the environmental status of the corresponding monitoring point based on the environmental monitoring data of each monitoring point; The difference gradient of environmental conditions between monitoring points in the monitoring area is determined based on the distribution differences of all environmental conditions.

[0035] In specific implementation, for each monitoring area, first, the environmental monitoring data of all monitoring points in the monitoring area are obtained by the above-mentioned implementation method; then, the average value of the environmental monitoring data of each monitoring point can be used as the environmental state of the corresponding monitoring point, wherein the environmental state is the environmental characterization quantity of the monitoring point, such as temperature and humidity; finally, the difference in environmental state between adjacent monitoring points can be used as the environmental state neighborhood difference, and then the environmental state neighborhood difference of all adjacent monitoring points can be obtained, and then the average value of all environmental state neighborhood differences can be used as the difference gradient of environmental state between monitoring points in the monitoring area; and then the difference gradient of environmental state between monitoring points in each monitoring area can be obtained.

[0036] In this embodiment, clustering and projecting the environmental monitoring data of each monitoring area using each difference gradient and the monitoring dependency relationship to obtain the local state quantity of the environment of each monitoring area can be achieved by the following steps: For each monitoring area, obtain environmental monitoring data of all monitoring points in the monitoring area; Correcting the environmental monitoring data of all monitoring points in the monitoring area according to the difference gradient corresponding to the monitoring area and the monitoring dependency relationship to obtain corrected environmental monitoring data; Cluster the corrected environmental monitoring data based on the K-means clustering algorithm, and project the clustering results into a low-dimensional state space; The local state quantity of the monitoring area environment is extracted from the low-dimensional state space.

[0037] In a specific implementation, for each monitoring area, first, environmental monitoring data of all monitoring points in the monitoring area are obtained using the method of the above embodiment; second, a correction model is initialized, the difference gradient corresponding to the monitoring area and the monitoring dependency relationship are used as constraint parameters of the correction model, and the environmental monitoring data of all monitoring points in the monitoring area are used as initialization parameters of the correction model. Corrected environmental monitoring data are output through the correction model; then, the corrected environmental monitoring data are clustered using the K-means clustering algorithm in the prior art to divide the corrected environmental monitoring data into monitoring feature clusters and non-monitoring feature clusters, and the environmental monitoring data in the monitoring feature clusters are further projected into a low-dimensional state space, wherein the monitoring feature clusters refer to relatively important data in the environmental monitoring data, and the non-monitoring feature clusters refer to relatively unimportant data in the environmental monitoring data. Clustering can greatly reduce data redundancy, thereby improving subsequent computing speed; finally, state data of the monitoring area environment (i.e., valid environmental monitoring data) is extracted from the low-dimensional state space, and the average value of the state data is used as the local state quantity of the monitoring area environment; thereby, the local state quantities of the environments of all monitoring areas are obtained.

[0038] In step S4, the environmental state characteristics at each monitoring point are extracted, and the mutual interference coefficients of the sensor elements between the monitoring points are determined based on all the environmental state characteristics and the monitoring dependency relationship.

[0039] It should be noted that the environmental state characteristics in this application refer to the key variables used to describe the environmental state in a specific space. In this application, the key variables are the average values ​​of the environmental monitoring data, which can reflect the overall state of the monitoring point environment, such as the average humidity. In other embodiments, the environmental state characteristics can also be other state quantities, which are not limited here.

[0040] In this embodiment, the mutual interference coefficient of sensor elements between monitoring points is determined based on all environmental state characteristics and the monitoring dependency relationship, which can be achieved by the following steps: Determine the characteristic variance through all environmental state characteristics; Regression analysis is performed on all environmental state features based on the monitoring dependency and the feature variance to determine mutual interference coefficients of sensor elements between monitoring points.

[0041] In specific implementation, first, the characteristic variance is a statistical indicator to measure the degree of fluctuation of environmental state characteristics, which is used to express the degree of deviation between the characteristic value and its average value. All environmental state characteristics can be substituted into the variance calculation formula, and the calculation result can be used as the characteristic variance; then, the environmental state characteristics of multiple monitoring points can be used as independent variables, the monitoring dependency relationship as the dependent variable, and the characteristic variance as the error term to establish a multivariate linear regression model. The environmental state characteristics of all monitoring points are used as input variables. According to the previously constructed monitoring dependency matrix, the dependent variable data as the dependency is input, and then the regression coefficient corresponding to each monitoring point is solved through regression analysis methods (such as the least squares method). The size of the regression coefficient directly reflects the strength of the characteristic correlation and dependence between different monitoring points. Furthermore, the average value of the corresponding regression coefficients of all monitoring points is used as the mutual interference coefficient of the sensor elements between the monitoring points.

[0042] It should be noted that determining the mutual interference coefficient helps identify potential problems between sensors. For example, some sensors may generate false alarms when the environment changes or sensor data may affect each other, thereby reducing the monitoring accuracy of the system. Through the mutual interference coefficient, sensor layout and data processing can be optimized to reduce error propagation between sensors.

[0043] In step S5, the environmental state of the enclosed space in the control cabinet is spatially hierarchically coupled through the mutual interference coefficient and all local state quantities to obtain a global state quantity, which is then used as the state quantity of the working environment of the integrated barrier machine.

[0044] In this embodiment, the environmental state of the enclosed space in the control cabinet is spatially coupled by the mutual interference coefficient and all local state quantities to obtain the global state quantity, which can be achieved by the following steps: Compensating all local state quantities by using the mutual interference coefficient to obtain compensated local state quantities; The spatial hierarchical analysis method is used to spatially couple all compensated local state quantities to obtain a global state quantity that reflects the overall environmental state.

[0045] In specific implementation, first, the mutual interference coefficient can be used as the compensation coefficient of the linear compensator, and then all local state quantities are compensated by the linear compensator to obtain the compensated local state quantities; then, a model of the global state quantity is constructed based on the compensated local state quantities, and a spatial hierarchical analysis method such as AHP is used to weight the local state quantities according to a certain weight to obtain the global state quantity, wherein the weight can be set based on the mutual interference coefficient and the importance of each monitoring point to ensure that the influence of important monitoring points on the global state quantity is more significant. It should be noted that the hierarchical analysis method is used in this application to calculate the weight of each local state quantity, and the contribution of each monitoring point to the global state quantity is adjusted in combination with the mutual interference coefficient.

[0046] It should be noted that this application can effectively obtain a global state quantity that reflects the overall environment by performing interference compensation on local state quantities and then using a spatial hierarchical analysis method to couple the compensated local state quantities. This process not only improves the accuracy of environmental monitoring, but also provides a reliable data basis for subsequent management and optimization.

[0047] It can be seen that in the present application, the environmental state of the enclosed space in the control cabinet can be spatially coupled by the mutual interference coefficient and all local state quantities to obtain a global state quantity, and then the global state quantity is used as the state quantity of the working environment of the integrated barrier machine; wherein, by analyzing the mutual interference coefficient of the sensor elements and combining the local state quantities of each monitoring area, the environmental state of the enclosed space in the control cabinet is spatially coupled and estimated, and finally the global state quantity is obtained, which effectively solves the measurement error problem caused by interference between sensors and improves the accuracy of the monitoring data; at the same time, the spatial-level coupling analysis can integrate the local environmental states of each area into a unified global state, thereby comprehensively reflecting the environmental changes in the entire enclosed space. This global state quantity is used to monitor the working environment of the integrated barrier machine in real time, so that the system can grasp the operating environment of the equipment more accurately, improve the overall accuracy and reliability of monitoring, and effectively avoid the limitations of local monitoring; in summary, the present application scheme can realize the coupled estimation of the working environment state of the integrated barrier machine, thereby improving the accuracy of environmental monitoring.

[0048] Example 2 This application provides a dynamic environment monitoring system for an integrated barrier machine, referring to Figure 2 As shown, this figure is a schematic diagram of a dynamic environment monitoring system for an integrated barrier machine according to this embodiment of the present application. The dynamic environment monitoring system for an integrated barrier machine includes: The data acquisition module 100 is used to collect environmental monitoring data of multiple monitoring points in the confined space of the control cabinet of the integrated barrier machine through sensor elements; The data processing module 200 is used to construct a confidence matrix between all monitoring points based on the distance between each monitoring point, and then perform dependency association on the environmental monitoring data of each monitoring point based on the confidence matrix to obtain a monitoring dependency relationship between all monitoring points; A local state assessment module 300 is configured to divide the confined space into a plurality of monitoring areas based on the location information of each monitoring point, thereby determining a difference gradient of the environmental state between the monitoring points in each monitoring area, and clustering and projecting the environmental monitoring data of each monitoring area using each difference gradient and the monitoring dependency relationship to obtain a local state quantity of the environment in each monitoring area; Interference assessment module 400, for extracting environmental state characteristics at each monitoring point, and determining mutual interference coefficients of sensor elements between monitoring points based on all environmental state characteristics and the monitoring dependency relationship; The global state evaluation module 500 is used to perform spatial hierarchical coupling on the environmental state of the enclosed space in the control cabinet through the mutual interference coefficient and all local state quantities to obtain a global state quantity, and then use the global state quantity as the state quantity of the working environment of the integrated barrier machine.

[0049] In addition, the present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0050] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program. The program can be stored in a computer-readable storage medium, including a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM), or other optical disc storage, magnetic disk storage, or magnetic tape storage, or any other computer-readable medium capable of carrying or storing data.

[0051] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. A dynamic environment monitoring method for an integrated barrier machine, characterized in that: The dynamic environment monitoring method comprises the following steps: Collect environmental monitoring data from multiple monitoring points in the confined space of the integrated barrier machine's control cabinet through sensor elements; Constructing a confidence matrix between all monitoring points based on the distances between the monitoring points, and then performing dependency association on the environmental monitoring data of each monitoring point based on the confidence matrix to obtain a monitoring dependency relationship between all monitoring points; Dividing the confined space into a plurality of monitoring areas according to the location information of each monitoring point, and then determining the difference gradient of the environmental state between the monitoring points in each monitoring area, clustering and projecting the environmental monitoring data of each monitoring area according to each difference gradient and the monitoring dependency relationship, and obtaining the local state quantity of the environment in each monitoring area; Extracting environmental state characteristics at each monitoring point, and determining mutual interference coefficients of sensor elements between monitoring points based on all environmental state characteristics and the monitoring dependency relationship; The environmental state of the enclosed space in the control cabinet is spatially hierarchically coupled through the mutual interference coefficient and all local state quantities to obtain a global state quantity, which is then used as the state quantity of the working environment of the integrated barrier machine.

2. A dynamic environment monitoring method for an integrated barrier machine according to claim 1, characterized in that: The confidence matrix between all monitoring points is constructed based on the distance between each monitoring point, specifically including: Collect historical environmental monitoring data from multiple monitoring points in the confined space of the control cabinet; Get the distance between each monitoring point; Determine the similarity of monitoring data between each monitoring point through all historical environmental monitoring data and the distance between each monitoring point; The confidence matrix between all monitoring points is constructed based on the similarity of the monitoring data between each monitoring point.

3. A dynamic environment monitoring method for an integrated barrier machine according to claim 1, characterized in that: The environmental monitoring data of each monitoring point are subjected to dependency association according to the confidence matrix to obtain the monitoring dependency relationship between all monitoring points, which specifically includes: Performing dependency correlation analysis on the environmental monitoring data of each monitoring point using the confidence matrix to obtain the dependency correlation between the data of each monitoring point; A dependency matrix between all monitoring points is constructed according to all dependency correlations, and the monitoring dependency relationship between all monitoring points is further described by the dependency matrix.

4. A dynamic environment monitoring method for an integrated barrier machine according to claim 1, characterized in that: Determining the difference gradient of environmental conditions between monitoring points in each monitoring area specifically includes: For each monitoring area, obtain environmental monitoring data of all monitoring points in the monitoring area; Determine the environmental status of the corresponding monitoring point based on the environmental monitoring data of each monitoring point; The difference gradient of environmental conditions between monitoring points in the monitoring area is determined based on the distribution differences of all environmental conditions.

5. A dynamic environment monitoring method for an integrated barrier machine according to claim 1, characterized in that: The environmental monitoring data of each monitoring area is clustered and projected using each difference gradient and the monitoring dependency relationship to obtain the local state quantity of the environment of each monitoring area, specifically including: For each monitoring area, obtain environmental monitoring data of all monitoring points in the monitoring area; Correcting the environmental monitoring data of all monitoring points in the monitoring area according to the difference gradient corresponding to the monitoring area and the monitoring dependency relationship to obtain corrected environmental monitoring data; Cluster the corrected environmental monitoring data based on the K-means clustering algorithm, and project the clustering results into a low-dimensional state space; The local state quantity of the monitoring area environment is extracted from the low-dimensional state space.

6. A dynamic environment monitoring method for an integrated barrier machine according to claim 1, characterized in that: Determining the mutual interference coefficient of sensor elements between monitoring points based on all environmental state characteristics and the monitoring dependency specifically includes: Determine the characteristic variance through all environmental state characteristics; Regression analysis is performed on all environmental state features based on the monitoring dependency and the feature variance to determine mutual interference coefficients of sensor elements between monitoring points.

7. A dynamic environment monitoring method for an integrated barrier machine according to claim 1, characterized in that: The environmental state of the enclosed space in the control cabinet is spatially coupled by the mutual interference coefficient and all local state quantities to obtain the global state quantity, which specifically includes: Compensating all local state quantities by using the mutual interference coefficient to obtain compensated local state quantities; The spatial hierarchical analysis method is used to spatially couple all compensated local state quantities to obtain a global state quantity that reflects the overall environmental state.

8. A dynamic environment monitoring method for an integrated barrier machine according to claim 1, characterized in that: The integrated automatic barrier machine is a new type of multifunctional and highly integrated automatic barrier machine.

9. A dynamic environment monitoring method for an integrated barrier machine according to claim 1, characterized in that: The sensor elements include a temperature sensor and a humidity sensor.

10. A dynamic environment monitoring system for an integrated barrier machine, used to execute a dynamic environment monitoring method for an integrated barrier machine according to any one of claims 1 to 9, characterized in that: The dynamic environment monitoring system comprises: A data acquisition module, used to collect environmental monitoring data of multiple monitoring points in the confined space of the control cabinet of the integrated barrier machine through sensor elements; A data processing module is used to construct a confidence matrix between all monitoring points based on the distance between each monitoring point, and then perform dependency association on the environmental monitoring data of each monitoring point based on the confidence matrix to obtain a monitoring dependency relationship between all monitoring points; A local state assessment module is used to divide the confined space into multiple monitoring areas based on the location information of each monitoring point, and then determine the difference gradient of the environmental state between the monitoring points in each monitoring area. The environmental monitoring data of each monitoring area is clustered and projected based on each difference gradient and the monitoring dependency relationship to obtain the local state quantity of the environment in each monitoring area; An interference assessment module is used to extract environmental state characteristics at each monitoring point and determine the mutual interference coefficient of sensor elements between monitoring points based on all environmental state characteristics and the monitoring dependency relationship; The global state evaluation module is used to perform spatial hierarchical coupling on the environmental state of the enclosed space in the control cabinet through the mutual interference coefficient and all local state quantities to obtain a global state quantity, and then use the global state quantity as the state quantity of the working environment of the integrated barrier machine.

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