Environmental meteorological intelligent monitoring system based on multi-source data collection and analysis

Abstract

The present invention belongs to the field of environmental meteorological monitoring and relates to data analysis technology, which is used to solve the problem that the prior art cannot combine multi-source data for comprehensive collection and analysis, resulting in the inability to accurately evaluate the overall state of environmental meteorology. Specifically, it is an environmental meteorological intelligent monitoring system based on multi-source data collection and analysis, including a data collection module, a data analysis module, a monitoring and analysis module, a necessary analysis module, an optimization analysis module and a database; the data collection module is used to collect environmental meteorological data; the data analysis module is used to perform weight analysis on environmental meteorological data; the present invention can monitor and analyze environmental meteorological data, combine the monitored values ​​of environmental meteorological parameters and weight coefficients for comprehensive analysis and calculation to obtain an evaluation coefficient, evaluate the overall environmental meteorological state within the monitoring period through the evaluation coefficient, and provide timely feedback and early warning when the overall environmental meteorological state is abnormal.

Description

Technical Field

[0001] The invention belongs to the field of environmental meteorological monitoring and relates to data analysis technology, and specifically to an environmental meteorological intelligent monitoring system based on multi-source data collection and analysis. Background Art

[0002] The environmental meteorological intelligent monitoring system is a system that uses modern information technology to conduct real-time, continuous and high-precision monitoring of environmental meteorology. It collects and analyzes various meteorological parameters such as temperature, humidity, wind speed, wind direction, air pressure, precipitation, solar radiation, etc. through technologies such as the Internet of Things, big data, cloud computing and artificial intelligence, providing solid data support for meteorological services.

[0003] The invention patent with announcement number CN104181615B discloses a universal platform for real-time monitoring of micro-meteorological environment and physical information. The platform can quickly respond to, measure in real time, and frame and report various environmental information such as meteorological environment and physical information of the platform itself; however, the platform cannot combine multi-source data for comprehensive collection and analysis, resulting in the inability to accurately evaluate the overall state of the environmental meteorology, and at the same time, it is impossible to perform optimized decision-making analysis when the overall state is abnormal.

[0004] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent environmental meteorological monitoring system based on multi-source data collection and analysis, which is used to solve the problem that the prior art cannot combine multi-source data for comprehensive collection and analysis, resulting in the inability to accurately evaluate the overall state of environmental meteorology; The technical problem to be solved by the present invention is: how to provide an environmental meteorological intelligent monitoring system based on multi-source data collection and analysis that can combine multi-source data to accurately evaluate the overall state.

[0006] The purpose of the present invention can be achieved through the following technical solutions: The environmental meteorological intelligent monitoring system based on multi-source data collection and analysis includes a data collection module, a data analysis module, a monitoring and analysis module, a necessary analysis module, an optimization analysis module and a database, wherein the data collection module, the monitoring and analysis module, the necessary analysis module, the optimization analysis module and the database are sequentially connected in communication, the data analysis module is also connected in communication with the monitoring and analysis module, and the database is also connected in communication with the monitoring and analysis module and the necessary analysis module; The data acquisition module is used to collect environmental meteorological data: the environmental meteorological parameters to be monitored are marked as parameters CSi, i=1, 2, ..., n, n is a positive integer, the values ​​of the parameters CSi are collected in real time through the corresponding monitoring equipment of the parameters CSi and marked as the monitoring values ​​JCi of the parameters CSi, and all the monitoring values ​​JCi of the parameters CSi are sent to the monitoring and analysis module; The data analysis module is used to perform weight analysis on the environmental meteorological data and obtain the weight coefficient Ui of the parameter CSi, and send the weight coefficient Ui of all parameters CSi to the monitoring and analysis module; The monitoring and analysis module is used to monitor and analyze environmental meteorological data: generate a monitoring cycle and divide the monitoring cycle into several monitoring periods, obtain the evaluation coefficient PG of the monitoring period by numerically calculating the monitoring values ​​JCi and the analysis values ​​FXi of all parameters CSi, and mark the monitoring period as a normal period or an abnormal period by the evaluation coefficient PG; The necessity analysis module is used to analyze the necessity of active optimization of the environmental meteorological state: at the end of the monitoring period, the ratio of the number of markings of the abnormal period to the total number of monitoring periods is marked as the abnormal coefficient of the monitoring period, and whether the monitoring period has the active optimization feature is determined by the abnormal coefficient; The optimization analysis module is used to optimize and analyze the environmental meteorological conditions.

[0007] Furthermore, the process of obtaining the weight coefficient Ui of the parameter CSi includes: calling the normal value range of the parameter CSi, marking the difference between the maximum value and the minimum value of the normal value range as the range value FWi of the parameter CSi, marking the average value of the maximum value and the minimum value of the normal value range as the standard value BZi of the parameter CSi, and calculating the weight coefficient Ui of the parameter CSi by the formula Get the weight coefficient Ui of parameter CSi.

[0008] Furthermore, the acquisition process of the evaluation coefficient PG during the monitoring period includes: marking the absolute value of the difference between the monitoring value JCi of the parameter CSi and the standard value BZi as the abnormal value YCi of the parameter CSi, marking the maximum value of the abnormal value YCi during the monitoring period as the analysis value FXi of the parameter CSi during the monitoring period, and The evaluation coefficient PG of the monitoring period is obtained.

[0009] Furthermore, the specific process of marking the monitoring period as a normal period or an abnormal period includes: obtaining the evaluation threshold PGmax through the database, and comparing the evaluation coefficient PG of the monitoring period with the evaluation threshold PGmax: if the evaluation coefficient PG is less than the evaluation threshold PGmax, it is determined that the environmental meteorological conditions within the monitoring period meet the requirements, and the corresponding monitoring period is marked as a normal period; if the evaluation coefficient PG is greater than or equal to the evaluation threshold PGmax, it is determined that the environmental meteorological conditions within the monitoring period do not meet the requirements, and the corresponding monitoring period is marked as an abnormal period, a meteorological anomaly signal is generated and the meteorological anomaly signal is sent to the mobile phone terminal of the management personnel.

[0010] Furthermore, the specific process of determining whether the monitoring cycle has active optimization characteristics includes: obtaining an abnormality threshold through a database, and comparing the abnormality coefficient with the abnormality threshold: if the abnormality coefficient is less than the abnormality threshold, it is determined that the monitoring cycle does not have active optimization characteristics; if the abnormality coefficient is greater than or equal to the abnormality threshold, it is determined that the monitoring cycle has active optimization characteristics, generating an optimization analysis signal and sending the optimization analysis signal to the optimization analysis module.

[0011] Furthermore, the specific process of the optimization analysis module for optimizing the environmental meteorological state includes: marking Ui×FXi in each abnormal time period as the optimized value YHi of the parameter CSi, arranging the parameters CSi in the abnormal time period in descending order according to the optimized value YHi to obtain the optimized sequence YHi of the abnormal time period, intercepting the first L1 parameters CSi in the optimized sequence YHi of all abnormal time periods and marking them as optimization objects, marking the number of times the parameter CSi is marked as the optimization object as the selected value XZi of the parameter CSi, calculating the variance of the selected values ​​CXi of all the parameters CSi to obtain the distribution coefficient of the monitoring period, and marking the abnormal characteristics of the environmental meteorological state within the monitoring period by the distribution coefficient.

[0012] Furthermore, the specific process of marking the abnormal characteristics of the environmental meteorological state during the monitoring period includes: retrieving the distribution threshold through the database, and comparing the distribution coefficient with the distribution threshold: if the distribution coefficient is less than the distribution threshold, the abnormal characteristics of the environmental meteorological state during the monitoring period are marked as overall abnormalities, an overall optimization signal is generated and the overall optimization signal is sent to the mobile phone terminal of the manager; if the distribution coefficient is greater than or equal to the distribution threshold, the abnormal characteristics of the environmental meteorological state during the monitoring period are marked as local abnormalities, the parameters CSi are arranged in descending order according to the selected value XZi value to obtain a selected sequence, the top L2 parameters CSi in the selected sequence are intercepted and marked as active optimization parameters, and all active optimization parameters are sent to the mobile phone terminal of the manager.

[0013] Furthermore, the working method of the environmental meteorological intelligent monitoring system based on multi-source data collection and analysis includes the following steps: Step 1: Collect environmental meteorological data: mark the environmental meteorological parameters to be monitored as parameters CSi, collect the values ​​of the parameters CSi in real time through the corresponding monitoring equipment of the parameters CSi and mark them as the monitoring values ​​JCi of the parameters CSi; Step 2: Perform weight analysis on environmental meteorological data: retrieve the normal value range of parameter CSi, mark the difference between the maximum value and the minimum value of the normal value range as the range value FWi of parameter CSi, and perform numerical calculation on the range values ​​FWi of all parameters CSi to obtain the weight coefficient Ui; Step 3: Monitor and analyze environmental meteorological data: Generate a monitoring cycle and divide the monitoring cycle into several monitoring periods. Perform numerical calculations on the monitoring values ​​JCi of all parameters CSi and the weight coefficient Ui to obtain the evaluation coefficient PG of the monitoring period. The monitoring period is marked as a normal period or an abnormal period by the evaluation coefficient PG. Step 4: Analyze the necessity of active optimization of environmental meteorological conditions: At the end of the monitoring period, mark the ratio of the number of marks of the abnormal period to the total number of monitoring periods as the abnormal coefficient of the monitoring period, and judge whether the monitoring period has the active optimization feature by the abnormal coefficient. If it has, execute step 5; Step 5: Optimize and analyze the environmental meteorological conditions and obtain the distribution coefficient, and use the distribution coefficient to mark the abnormal characteristics of the environmental meteorological conditions during the monitoring period.

[0014] The present invention has the following beneficial effects: The data acquisition module can be used to collect environmental meteorological data, mark the environmental meteorological parameters that need to be monitored, and then collect values ​​through the corresponding monitoring equipment. Combined with the data analysis module, the corresponding weight coefficients are assigned to the environmental meteorological parameters to provide data support for the monitoring and analysis process; The monitoring and analysis module can monitor and analyze environmental meteorological data, and conduct comprehensive analysis and calculation to obtain the evaluation coefficient by combining the monitoring values ​​of environmental meteorological parameters and weight coefficients. The overall environmental meteorological status during the monitoring period can be evaluated by the evaluation coefficient, and timely feedback and early warning can be given when abnormal overall environmental meteorological status is detected. The necessity analysis module can be used to analyze the necessity of active optimization of the environmental meteorological state, and the abnormal coefficient can be obtained by combining the marking results of all monitoring periods within the monitoring period. The abnormal coefficient can be used to determine whether the environmental meteorological state within the monitoring period needs to be actively optimized, so as to trigger the optimization analysis process for optimization decision analysis when necessary; 4. The optimization analysis module can be used to optimize the environmental meteorological status, analyze the numerical distribution status of environmental meteorological parameters in all abnormal time periods to obtain the distribution coefficient, and then generate the corresponding optimization processing decision through the distribution coefficient to improve the processing efficiency when the environmental meteorological status is abnormal. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flow chart of the method of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0017] The technical scheme of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: Figure 1 As shown, the environmental meteorological intelligent monitoring system based on multi-source data acquisition and analysis includes a data acquisition module, a data analysis module, a monitoring and analysis module, a necessary analysis module, an optimization analysis module and a database. The data acquisition module, the monitoring and analysis module, the necessary analysis module, the optimization analysis module and the database are communicatively connected in sequence, the data analysis module is also communicatively connected with the monitoring and analysis module, and the database is also communicatively connected with the monitoring and analysis module and the necessary analysis module.

[0019] The data acquisition module is used to collect environmental meteorological data: the environmental meteorological parameters that need to be monitored are marked as parameters CSi, i=1, 2,…, n, n is a positive integer. Generally speaking, parameter CSi includes temperature, humidity, wind speed, atmospheric pressure, etc.; the value of parameter CSi is collected in real time through the corresponding monitoring equipment of parameter CSi and marked as the monitoring value JCi of parameter CSi, and all the monitoring values ​​JCi of parameter CSi are sent to the monitoring and analysis module.

[0020] The data analysis module is used to perform weight analysis on environmental meteorological data: retrieve the normal value range of parameter CSi, mark the difference between the maximum and minimum values ​​of the normal value range as the range value FWi of parameter CSi, mark the average value of the maximum and minimum values ​​of the normal value range as the standard value BZi of parameter CSi, and calculate the standard value BZi of parameter CSi by formula Obtain the weight coefficient Ui of the parameter CSi, and send the weight coefficients Ui of all parameters CSi to the monitoring and analysis module; mark the environmental meteorological parameters that need to be monitored, and then collect numerical values ​​through the corresponding monitoring equipment, and combine the data analysis module to assign corresponding weight coefficients to the environmental meteorological parameters to provide data support for the monitoring and analysis process.

[0021] The monitoring and analysis module is used to monitor and analyze environmental meteorological data: generate a monitoring cycle and divide the monitoring cycle into several monitoring periods, mark the absolute value of the difference between the monitoring value JCi of the parameter CSi and the standard value BZi as the abnormal value YCi of the parameter CSi, and mark the maximum value of the abnormal value YCi in the monitoring period as the analysis value FXi of the parameter CSi in the monitoring period. The evaluation coefficient PG of the monitoring period is obtained, and the evaluation threshold PGmax is obtained through the database. The evaluation coefficient PG of the monitoring period is compared with the evaluation threshold PGmax: if the evaluation coefficient PG is less than the evaluation threshold PGmax, it is determined that the environmental meteorological state during the monitoring period meets the requirements, and the corresponding monitoring period is marked as a normal period; if the evaluation coefficient PG is greater than or equal to the evaluation threshold PGmax, it is determined that the environmental meteorological state during the monitoring period does not meet the requirements, and the corresponding monitoring period is marked as an abnormal period, and a meteorological anomaly signal is generated and sent to the mobile phone terminal of the manager; the evaluation coefficient is obtained by comprehensive analysis and calculation based on the monitoring values ​​of the environmental meteorological parameters and the weight coefficient, and the overall environmental meteorological state during the monitoring period is evaluated by the evaluation coefficient, and timely feedback and early warning are given when the overall environmental meteorological state is abnormal.

[0022] The necessary analysis module is used to analyze the necessity of active optimization of environmental meteorological conditions: at the end of the monitoring period, the ratio of the number of markings of the abnormal period to the total number of monitoring periods is marked as the abnormal coefficient of the monitoring period, the abnormal threshold is obtained through the database, and the abnormal coefficient is compared with the abnormal threshold: if the abnormal coefficient is less than the abnormal threshold, it is determined that the monitoring period does not have the active optimization feature; if the abnormal coefficient is greater than or equal to the abnormal threshold, it is determined that the monitoring period has the active optimization feature, and an optimization analysis signal is generated and sent to the optimization analysis module; the marking results of all monitoring periods in the monitoring period are analyzed to obtain the abnormal coefficient, and the abnormal coefficient is used to determine whether the environmental meteorological conditions in the monitoring period need to be actively optimized, so as to trigger the optimization analysis process for optimization decision analysis when necessary.

[0023] The optimization analysis module is used to optimize the environmental meteorological status: mark Ui×FXi in each abnormal period as the optimized value YHi of parameter CSi, arrange the parameters CSi in the abnormal period in descending order according to the optimized value YHi to obtain the optimized sequence YHi of the abnormal period, intercept the first L1 parameters CSi in the optimized sequence YHi of all abnormal periods and mark them as optimization objects, L1 is a numerical constant, and the specific value of L1 is set by the management personnel, and the number of times the parameter CSi is marked as the optimization object is marked as the selected value XZi of the parameter CSi, and the variance of the selected values ​​CXi of all parameters CSi is calculated to obtain the distribution coefficient of the monitoring period, and the distribution threshold is retrieved through the database, and the distribution coefficient is compared with the distribution threshold: if the distribution coefficient is less than the distribution threshold, the monitoring The abnormal characteristics of the environmental meteorological state within the period are marked as overall abnormalities, and an overall optimization signal is generated and sent to the mobile phone terminal of the manager; if the distribution coefficient is greater than or equal to the distribution threshold, the abnormal characteristics of the environmental meteorological state within the monitoring period are marked as local abnormalities, and the parameters CSi are arranged in descending order according to the selected value XZi to obtain a selected sequence, and the top L2 parameters CSi in the selected sequence are intercepted and marked as active optimization parameters, L2 is a numerical constant, and the specific value of L2 is set by the manager; all active optimization parameters are sent to the mobile phone terminal of the manager; the numerical distribution state of the environmental meteorological parameters in all abnormal time periods is analyzed to obtain the distribution coefficient, and then the corresponding optimization processing decision is generated through the distribution coefficient to improve the processing efficiency when the environmental meteorological state is abnormal.

[0024] Embodiment 2: Figure 2 As shown, the environmental meteorological intelligent monitoring method based on multi-source data collection and analysis includes the following steps: Step 1: Collect environmental meteorological data: mark the environmental meteorological parameters to be monitored as parameters CSi, collect the values ​​of the parameters CSi in real time through the corresponding monitoring equipment of the parameters CSi and mark them as the monitoring values ​​JCi of the parameters CSi; Step 2: Perform weight analysis on environmental meteorological data: retrieve the normal value range of parameter CSi, mark the difference between the maximum value and the minimum value of the normal value range as the range value FWi of parameter CSi, and perform numerical calculation on the range values ​​FWi of all parameters CSi to obtain the weight coefficient Ui; Step 3: Monitor and analyze environmental meteorological data: Generate a monitoring cycle and divide the monitoring cycle into several monitoring periods. Perform numerical calculations on the monitoring values ​​JCi of all parameters CSi and the weight coefficient Ui to obtain the evaluation coefficient PG of the monitoring period. The monitoring period is marked as a normal period or an abnormal period by the evaluation coefficient PG. Step 4: Analyze the necessity of active optimization of environmental meteorological conditions: At the end of the monitoring period, mark the ratio of the number of marks of the abnormal period to the total number of monitoring periods as the abnormal coefficient of the monitoring period, and judge whether the monitoring period has the active optimization feature by the abnormal coefficient. If it has, execute step 5; Step 5: Optimize and analyze the environmental meteorological conditions and obtain the distribution coefficient, and use the distribution coefficient to mark the abnormal characteristics of the environmental meteorological conditions during the monitoring period.

[0025] The environmental meteorological intelligent monitoring system based on multi-source data collection and analysis, when working, marks the environmental meteorological parameters that need to be monitored as parameters CSi, collects the values ​​of the parameters CSi in real time through the corresponding monitoring equipment of the parameters CSi and marks them as the monitoring values ​​JCi of the parameters CSi; retrieves the normal value range of the parameters CSi, marks the difference between the maximum value and the minimum value of the normal value range as the range value FWi of the parameters CSi, and numerically calculates the range values ​​FWi of all the parameters CSi to obtain the weight coefficient Ui; generates a monitoring cycle and divides the monitoring cycle into several monitoring time periods, numerically calculates the monitoring values ​​JCi of all the parameters CSi and the weight coefficient Ui to obtain the evaluation coefficient PG of the monitoring time period, and marks the monitoring time period as a normal time period or an abnormal time period through the evaluation coefficient PG; at the end of the monitoring cycle, marks the ratio of the number of marking times of the abnormal time period to the total number of monitoring time periods as the abnormal coefficient of the monitoring cycle, judges whether the monitoring cycle has active optimization characteristics through the abnormal coefficient, optimizes and analyzes the environmental meteorological state when it has such characteristics and obtains the distribution coefficient, and marks the abnormal characteristics of the environmental meteorological state in the monitoring cycle through the distribution coefficient.

[0026] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the claims, they should all fall within the protection scope of the present invention.

[0027] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0028] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The environmental meteorological intelligent monitoring system based on multi-source data collection and analysis is characterized by: It includes data collection module, data analysis module, monitoring analysis module, necessary analysis module, optimization analysis module and database; The data acquisition module is used to collect environmental meteorological data: the environmental meteorological parameters to be monitored are marked as parameters CSi, i=1, 2, ..., n, n is a positive integer, the values ​​of the parameters CSi are collected in real time through the corresponding monitoring equipment of the parameters CSi and marked as the monitoring values ​​JCi of the parameters CSi, and all the monitoring values ​​JCi of the parameters CSi are sent to the monitoring and analysis module; The data analysis module is used to perform weight analysis on the environmental meteorological data and obtain the weight coefficient Ui of the parameter CSi, and send the weight coefficient Ui of all parameters CSi to the monitoring and analysis module; The monitoring and analysis module is used to monitor and analyze environmental meteorological data: generate a monitoring cycle and divide the monitoring cycle into several monitoring periods, obtain the evaluation coefficient PG of the monitoring period by numerically calculating the monitoring values ​​JCi and the analysis values ​​FXi of all parameters CSi, and mark the monitoring period as a normal period or an abnormal period by the evaluation coefficient PG; The necessity analysis module is used to analyze the necessity of active optimization of the environmental meteorological state: at the end of the monitoring period, the ratio of the number of markings of the abnormal period to the total number of monitoring periods is marked as the abnormal coefficient of the monitoring period, and whether the monitoring period has the active optimization feature is determined by the abnormal coefficient; The optimization analysis module is used to optimize and analyze the environmental meteorological conditions.

2. The environmental meteorological intelligent monitoring system based on multi-source data collection and analysis according to claim 1 is characterized in that: The process of obtaining the weight coefficient Ui of the parameter CSi includes: calling the normal value range of the parameter CSi, marking the difference between the maximum value and the minimum value of the normal value range as the range value FWi of the parameter CSi, marking the average value of the maximum value and the minimum value of the normal value range as the standard value BZi of the parameter CSi, and calculating the weight coefficient Ui of the parameter CSi by the formula Get the weight coefficient Ui of parameter CSi.

3. The environmental meteorological intelligent monitoring system based on multi-source data collection and analysis according to claim 2 is characterized in that: The acquisition process of the evaluation coefficient PG during the monitoring period includes: marking the absolute value of the difference between the monitoring value JCi of the parameter CSi and the standard value BZi as the abnormal value YCi of the parameter CSi, marking the maximum value of the abnormal value YCi during the monitoring period as the analysis value FXi of the parameter CSi during the monitoring period, and obtaining the evaluation coefficient PG during the monitoring period by the formula The evaluation coefficient PG of the monitoring period is obtained.

4. The environmental meteorological intelligent monitoring system based on multi-source data collection and analysis according to claim 3 is characterized in that: The specific process of marking the monitoring period as a normal period or an abnormal period includes: obtaining the evaluation threshold PGmax through the database, and comparing the evaluation coefficient PG of the monitoring period with the evaluation threshold PGmax: if the evaluation coefficient PG is less than the evaluation threshold PGmax, it is determined that the environmental meteorological conditions within the monitoring period meet the requirements, and the corresponding monitoring period is marked as a normal period; if the evaluation coefficient PG is greater than or equal to the evaluation threshold PGmax, it is determined that the environmental meteorological conditions within the monitoring period do not meet the requirements, and the corresponding monitoring period is marked as an abnormal period, a meteorological anomaly signal is generated and the meteorological anomaly signal is sent to the mobile phone terminal of the management personnel.

5. The environmental meteorological intelligent monitoring system based on multi-source data collection and analysis according to claim 4 is characterized in that: The specific process of determining whether the monitoring cycle has active optimization features includes: obtaining an abnormality threshold through a database, and comparing the abnormality coefficient with the abnormality threshold: if the abnormality coefficient is less than the abnormality threshold, it is determined that the monitoring cycle does not have active optimization features; if the abnormality coefficient is greater than or equal to the abnormality threshold, it is determined that the monitoring cycle has active optimization features, generating an optimization analysis signal and sending the optimization analysis signal to the optimization analysis module.

6. The environmental meteorological intelligent monitoring system based on multi-source data collection and analysis according to claim 5 is characterized in that: The specific process of the optimization analysis module for optimizing the environmental meteorological state includes: marking Ui×FXi in each abnormal period as the optimized value YHi of the parameter CSi, arranging the parameters CSi in the abnormal period in descending order according to the optimized value YHi to obtain the optimized sequence YHi of the abnormal period, intercepting the first L1 parameters CSi in the optimized sequence YHi of all abnormal periods and marking them as optimization objects, marking the number of times the parameter CSi is marked as the optimization object as the selected value XZi of the parameter CSi, calculating the variance of the selected values ​​CXi of all parameters CSi to obtain the distribution coefficient of the monitoring period, and marking the abnormal characteristics of the environmental meteorological state within the monitoring period by the distribution coefficient.

7. The environmental meteorological intelligent monitoring system based on multi-source data collection and analysis according to claim 6 is characterized in that: The specific process of marking the abnormal characteristics of the environmental meteorological state during the monitoring period includes: retrieving the distribution threshold through the database, and comparing the distribution coefficient with the distribution threshold: if the distribution coefficient is less than the distribution threshold, the abnormal characteristics of the environmental meteorological state during the monitoring period are marked as overall abnormalities, an overall optimization signal is generated and the overall optimization signal is sent to the mobile phone terminal of the manager; if the distribution coefficient is greater than or equal to the distribution threshold, the abnormal characteristics of the environmental meteorological state during the monitoring period are marked as local abnormalities, the parameters CSi are arranged in descending order according to the selected value XZi value to obtain a selected sequence, the top L2 parameters CSi in the selected sequence are intercepted and marked as active optimization parameters, and all active optimization parameters are sent to the mobile phone terminal of the manager.

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