Multi-mechanism cooperative monitoring and filtering method and system

Abstract

The invention provides a multi-mechanism cooperative monitoring filtering method and system, and the method comprises the steps: obtaining a plurality of result data records corresponding to weather from a weather prediction mechanism, so as to construct a first equivalent partition; acquiring a plurality of process data records corresponding to weather from a data acquisition base station to construct a second equivalent partition; combining the second equivalent partitions of different data acquisition base stations into a data acquisition base station partition set, and obtaining a first correct decision value in combination with the first equivalent partition; judging whether the data acquisition base station is an important base station or an initial redundant base station based on the first correct decision value, and combining the important base stations into an important base station set; and obtaining a third correct decision value of the important base station set to determine whether the decision capability of the important base station set meets the requirement or not, thereby completing screening and elimination of the final redundant base stations. Based on the above mode, on the premise of ensuring stable decision ability, the number of data acquisition base stations is reduced, and the accuracy and efficiency of prediction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data prediction, and in particular to a multi-agency collaborative monitoring filtering method and system. Background Art

[0002] In the current information age, the construction and operation of meteorological forecasting systems are facing increasingly complex and changing challenges. As a key forecasting model, collaborative monitoring of multiple data collection base stations has gradually become the focus of attention because it can effectively integrate the resources and advantages of different data collection base stations to improve forecasting efficiency.

[0003] However, the traditional multi-agency collaborative monitoring method has exposed many shortcomings in actual operations. Specifically, under the traditional model, with the complexity of meteorological forecasting scenarios, the number of data collection base stations has gradually increased, which has led to a continuous increase in the total amount of data obtained. The massive amount of data will lead to reduced data processing efficiency, and the data quality between different data collection base stations is inconsistent. The data provided by data collection base stations with low data quality will not only fail to improve the quality of meteorological forecasts, but will also further affect the efficiency of data processing, thereby reducing the accuracy and effectiveness of meteorological forecasts. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a multi-institution collaborative monitoring and filtering method and system, aiming to solve the technical problem in the prior art that the accuracy and efficiency of weather forecasts are reduced as the number of data collection base stations with different data quality gradually increases.

[0005] In order to achieve the above-mentioned purpose, in a first aspect, an embodiment of the present application provides a multi-institution collaborative monitoring filtering method for eliminating final redundant base stations from a plurality of data collection base stations, and the multi-institution collaborative monitoring filtering method comprises the following steps: Obtaining a number of result data records corresponding to weather from a meteorological forecasting agency, extracting a number of decision attribute values ​​from the number of result data records based on decision attributes, and assigning a first unique identifier to each of the decision attribute values ​​to construct a first equivalence partition, wherein the first equivalence partition includes a number of first equivalence classes; Acquire a number of process data records corresponding to weather from the data collection base station, the number of the process data records corresponding to the number of the result data records, extract a collection attribute value group from the process data record based on a number of collection attributes, assign a second unique identifier to each of the collection attribute value groups to construct a second equivalence partition, the second equivalence partition including a number of second equivalence classes; Combining the second equivalent partitions of different data collection base stations into a data collection base station partition set including a plurality of third equivalent classes, and determining a first correct decision value of the data collection base station partition set in combination with the first equivalent partition; Based on the first correct decision value, the data collection base station is judged to be an important base station or an initial redundant base station, and the important base stations are merged into an important base station set; Obtain the third correct decision value of the important base station set to determine whether the decision-making ability of the important base station set meets the requirements based on the first correct decision value and the third correct decision value; if the decision-making ability of the important base station set meets the requirements, select all the initial redundant base stations as the final redundant base stations, and eliminate the final redundant base stations.

[0006] Compared with the prior art, the beneficial effects of the present invention are: by constructing the first equivalence partition with the first unique identifier and constructing the second equivalence partition with the second unique identifier, the result data record and the process data record themselves are not directly transmitted and processed, thereby ensuring the privacy and security of the data; by analyzing the impact of different data acquisition base stations on the decision-making ability changes of the overall meteorological forecasting system through the first correct decision value, and then determining the importance of different data acquisition base stations, and then, on the premise of ensuring the stability of the decision-making ability of the meteorological forecasting system, the number of data acquisition base stations is simplified to the greatest extent, that is, while reducing the overall data volume, data acquisition base stations with lower data quality are filtered out, which solves the problems of data redundancy, computational complexity and inconsistent data quality to a certain extent, and improves the accuracy and efficiency of the prediction.

[0007] Furthermore, the step of constructing a first equivalence partition, wherein the first equivalence partition includes a plurality of first equivalence classes, includes: comparing whether different decision attribute values ​​are the same; If the same decision attribute value exists, the first unique identifiers corresponding to the same decision attribute value are respectively combined into a plurality of first sets, and the first unique identifiers corresponding to different decision attribute values ​​are respectively divided into a plurality of second sets; The first set and the second set are both defined as first equivalence classes, and a number of the first equivalence classes are combined into a first equivalence partition.

[0008] Furthermore, the step of extracting a plurality of acquisition attribute value groups from the process data record based on a plurality of acquisition attributes is specifically as follows: Acquire a plurality of acquisition attributes corresponding to the data acquisition base station, match the acquisition attributes with the process data record to extract acquisition attribute values ​​corresponding to the acquisition attributes from the process data record, and combine a plurality of the acquisition attribute values ​​into an acquisition attribute value group.

[0009] Furthermore, the step of constructing a second equivalence partition, wherein the second equivalence partition includes a plurality of second equivalence classes, includes: comparing whether different groups of collected attribute values ​​are the same; If the same collection attribute value group exists, the second unique identifiers corresponding to the same collection attribute value group are respectively combined into a plurality of third sets, and the second unique identifiers corresponding to different collection attribute value groups are respectively divided into a plurality of fourth sets; The third set and the fourth set are both defined as second equivalence classes, and a number of the second equivalence classes are combined into a second equivalence partition.

[0010] Furthermore, the step of merging the second equivalent partitions of different data collection base stations into a data collection base station partition set includes: Selecting one of the second equivalent partitions as a benchmark equivalent partition, and selecting the remaining second equivalent partitions as equivalent partitions to be checked; Perform intersection judgment on each of the second equivalence classes in the benchmark equivalence partition and each of the second equivalence classes in the to-be-checked equivalence partition; If there is an intersection part, the intersection part is extracted to form a fifth set, the fifth set is defined as a third equivalence class, and a number of the third equivalence classes are combined into a data collection base station partition set.

[0011] Furthermore, the step of determining the first correct decision value of the data collection base station partition set in combination with the first equivalent partition includes: Determine whether each of the third equivalence classes in the data acquisition base station partition set is completely included in a first equivalence class in the first equivalence partition; A first correct decision value of the data collection base station partition set is determined based on the number of the third equivalence classes that are completely included in a certain first equivalence class in the first equivalence partition.

[0012] Furthermore, the step of judging the data collection base station as an important base station or an initial redundant base station based on the first correct decision value includes: Eliminate a data collection base station from the plurality of data collection base stations to obtain a remaining base station partition set of the remaining data collection base stations, and obtain a second correct decision value of the remaining base station partition set; Obtaining a degree of change in decision-making ability based on the first correct decision value and the second correct decision value; The degree of change in the decision-making ability is compared with a first threshold. If the degree of change in the decision-making ability is greater than the first threshold, the eliminated data collection base station is an important base station. If the degree of change in the decision-making ability is less than the first threshold, the eliminated data collection base station is an initial redundant base station.

[0013] Furthermore, the calculation formula for the degree of change in decision-making ability is: , in, Indicates that the data collection base station is removed The degree of change in decision-making ability after Indicates that the data collection base station is removed The second correct decision value after represents the first correct decision value.

[0014] Furthermore, in the step of obtaining the third correct decision value of the important base station set to determine whether the decision-making ability of the important base station set meets the requirements according to the first correct decision value and the third correct decision value, if the decision-making ability of the important base station set meets the requirements, all the initial redundant base stations are selected as the final redundant base stations, and the step of eliminating the final redundant base stations also includes: If the decision-making ability of the important base station set does not meet the requirement, selecting a base station to be added from a plurality of the initial redundant base stations, and adding the base station to be added to the important base station set; Obtaining a fourth correct decision value of the important base station set after the base station to be added is added, so as to determine whether the decision-making capability of the important base station set after the base station to be added meets the requirements according to the first correct decision value and the fourth correct decision value; If the decision-making capability of the important base station set after adding the base station to be added meets the requirement, the remaining initial redundant base stations are selected as the final redundant base stations, and the final redundant base stations are eliminated.

[0015] In a second aspect, an embodiment of the present application provides a multi-institution collaborative monitoring and filtering system, which is applied to the multi-institution collaborative monitoring and filtering method as described in the first aspect above, and the system includes: A first acquisition module is used to obtain a plurality of result data records corresponding to weather from a meteorological forecasting agency, extract a plurality of decision attribute values ​​from the plurality of result data records based on decision attributes, and assign a first unique identifier to each of the decision attribute values ​​to construct a first equivalence partition, wherein the first equivalence partition includes a plurality of first equivalence classes; A second acquisition module is used to acquire a number of process data records corresponding to the weather from the data acquisition base station, the number of the process data records corresponding to the number of the result data records, extract a collection attribute value group from the process data record based on a number of collection attributes, and assign a second unique identifier to each of the collection attribute value groups to construct a second equivalence partition, wherein the second equivalence partition includes a number of second equivalence classes; A combining module, used for combining the second equivalent partitions of different data collection base stations into a data collection base station partition set including a plurality of third equivalent classes, and determining a first correct decision value of the data collection base station partition set in combination with the first equivalent partition; a classification module, configured to determine, based on the first correct decision value, that the data collection base station is an important base station or an initial redundant base station, and merge the important base stations into an important base station set; An execution module is used to obtain a third correct decision value of the important base station set, so as to determine whether the decision-making ability of the important base station set meets the requirements according to the first correct decision value and the third correct decision value; if the decision-making ability of the important base station set meets the requirements, all the initial redundant base stations are selected as the final redundant base stations, and the final redundant base stations are eliminated.

[0016] In a third aspect, an embodiment of the present application provides a computer comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein when the processor executes the computer program, the multi-institution collaborative monitoring and filtering method as described in the first aspect above is implemented.

[0017] In a fourth aspect, an embodiment of the present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the multi-institutional collaborative monitoring and filtering method as described in the first aspect above is implemented. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of the multi-organization collaborative monitoring and filtering method in the first embodiment of the present invention; Figure 2 It is a structural block diagram of a multi-organization collaborative monitoring and filtering system in a second embodiment of the present invention; The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0019] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0020] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0022] See also Figure 1 The first embodiment of the present invention provides a multi-institution collaborative monitoring filtering method for eliminating final redundant base stations from a plurality of data collection base stations. The multi-institution collaborative monitoring filtering method comprises the following steps: S10: obtaining a plurality of result data records corresponding to the weather from a meteorological forecasting agency, extracting a plurality of decision attribute values ​​from the plurality of result data records based on a decision attribute, assigning a first unique identifier to each of the decision attribute values, so as to construct a first equivalence partition, wherein the first equivalence partition includes a plurality of first equivalence classes; Assume that some of the result data records are weather records for 3 days. The decision attribute is weather, that is, by taking weather as a keyword, extracting specific weather conditions from the weather records, it can be understood that the decision attribute value is sunny, cloudy, rainy and / or snowy.

[0023] Specifically, the step S10 includes: S110: comparing whether different decision attribute values ​​are the same; S120: If the same decision attribute value exists, the first unique identifiers corresponding to the same decision attribute value are respectively combined into a plurality of first sets, and the first unique identifiers corresponding to different decision attribute values ​​are respectively divided into a plurality of second sets; Taking 3 days of weather records as an example, if the decision attribute values ​​are sunny, cloudy, and sunny, then after the first unique identifier is assigned, they are 1, sunny; 2, cloudy; 3, sunny. At this time, there are identical decision attribute values. Then the first set is (1, 3), the second set is (2), and the first equivalence partition is {(1, 3), (2)}.

[0024] It can be understood that if there is no identical decision attribute value, the first unique identifiers corresponding to different decision attribute values ​​are respectively divided into a plurality of sixth sets; Taking the weather records of three days as an example, if the decision attribute values ​​are sunny, cloudy, and rainy, then after assigning the first unique identifier, they are 1. Sunny; 2. Cloudy; 3. Rainy. At this time, there is no identical decision attribute value, so the three sixth sets are (1), (2), (3), and the first equivalent partition is {(1), (2), (3)}.

[0025] It should be noted that the first set, the second set, and the sixth set have the same meaning, and only the number of records included, that is, the number of first unique identifiers may be different.

[0026] S130: defining the first set and the second set as a first equivalence class, and combining a plurality of the first equivalence classes into a first equivalence partition; The first equivalence partition is the set of the first equivalence classes.

[0027] S20: obtaining a plurality of process data records corresponding to the weather from the data collection base station, the process data records corresponding to the number of the result data records, extracting a collection attribute value group from the process data record based on a plurality of collection attributes, assigning a second unique identifier to each of the collection attribute value groups, so as to construct a second equivalence partition, wherein the second equivalence partition includes a plurality of second equivalence classes; The data collected by different data collection base stations may be the same or different. If there are three data collection base stations, the first data collection base station may collect air pressure, wind speed, and temperature, the second data collection base station may collect humidity and wind speed, and the third data collection base station may collect wind speed and temperature. It can be understood that the process data record is the data collection record of the data collection base station. If several result data records are 3-day weather records, the process data record is 3-day data collection record. Different data collection base stations have process data records corresponding to the number of result data records.

[0028] The step S20 comprises: S210: Acquire a plurality of acquisition attributes corresponding to the data acquisition base station, match the acquisition attributes with the process data record to extract acquisition attribute values ​​corresponding to the acquisition attributes from the process data record, and combine the plurality of acquisition attribute values ​​into an acquisition attribute value group; The collection attribute is the collection target of the data collection base station, namely wind speed, temperature, humidity, air pressure, etc. When wind speed is the collection attribute, the collection attribute value is high, medium, and low. Taking the mechanism 1 in the above example as an example, the collection attribute values ​​extracted from the first process data record are: high, large, and high, which respectively represent high air pressure, strong wind, and high temperature. The three collection attribute values ​​form the collection attribute value group. The number of the collection attribute value groups corresponds to the number of the process data records.

[0029] S220: comparing whether different collection attribute value groups are the same; S230: If the same collection attribute value group exists, merging the second unique identifiers corresponding to the same collection attribute value group into a plurality of third sets, and dividing the second unique identifiers corresponding to different collection attribute value groups into a plurality of fourth sets; S240: defining the third set and the fourth set as second equivalence classes, and combining a plurality of the second equivalence classes into a second equivalence partition; The second equivalence partition is obtained in a manner substantially consistent with the first equivalence partition. Still taking the three data collection base stations as an example, after obtaining the collection attribute value groups of the data collection base stations and assigning the second unique identifier, the results are shown in Table 1, Table 2, and Table 3: Table 1 , Table 2 , Table 3 , For data collection base station 1, the second equivalent partition is: {(1), (2), (3)}, for data collection base station 2, the second equivalent partition is: {(1, 3), (2)}, for data collection base station 3, the second equivalent partition is: {(1, 3), (2)}. It can be understood that the number of the second equivalent partitions corresponds to the number of the data collection base stations.

[0030] S30: merging the second equivalent partitions of different data collection base stations into a data collection base station partition set including a plurality of third equivalent classes, and determining a first correct decision value of the data collection base station partition set in combination with the first equivalent partition; Specifically, the step S30 includes: S310: selecting one of the second equivalent partitions as a benchmark equivalent partition, and selecting the remaining second equivalent partitions as equivalent partitions to be checked; Extending the above example, the second equivalent partition of data collection base station 1 is: {(1), (2), (3)}, the second equivalent partition of data collection base station 2 is: {(1, 3), (2)}, and the second equivalent partition of data collection base station 3 is: {(1, 3), (2)}. Data collection base station 1 is selected as the benchmark equivalent partition, and data collection base station 2 and data collection base station 3 are selected as the equivalent partitions to be checked.

[0031] S320: Performing intersection judgment on each of the second equivalence classes in the benchmark equivalence partition and each of the second equivalence classes in the to-be-checked equivalence partition; S330: If there is an intersection part, extract the intersection part and form a fifth set, define the fifth set as a third equivalence class, and combine several third equivalence classes into a data collection base station partition set; The second equivalence classes of data collection base station 1 are 3, namely (1), (2), and (3), and the second equivalence classes of data collection base station 2 are 2, namely (1, 3), and (2). After the intersection judgment of (1) in data collection base station 1 and (1, 3), (2) in data collection base station 1, (1) and (1, 3) have an intersection, and the intersection part is (1). The rest of the intersection judgment is similar, and the data collection base station partition set can be obtained as {(1), (2), (3)}.

[0032] S340: Determine whether each of the third equivalence classes in the data collection base station partition set is completely included in a first equivalence class in the first equivalence partition; S350: Determine a first correct decision value of the data collection base station partition set based on the number of the third equivalence classes that are completely included in a certain first equivalence class in the first equivalence partition; Assume that the first equivalence partition is {(1, 3), (2)}, the data acquisition base station partition set is {(1), (2), (3)}, the first equivalence classes are 2, namely (1, 3), (2), and the third equivalence classes are 3, namely (1), (2), (3). Then (1) and (3) of the data acquisition base station partition set are completely included in (1, 3) of the first equivalence partition, and (2) of the data acquisition base station partition set is completely included in (2) of the first equivalence partition. Then the first correct decision value is 3. If the first equivalent partition is {(1, 3), (2)}, and the data acquisition base station partition set is {(1, 2), (3)}, then only (3) of the data acquisition base station partition set is completely included in (1, 3) of the first equivalent partition, and the first correct decision value is 1.

[0033] S40: judging the data collection base station as an important base station or an initial redundant base station based on the first correct decision value, and merging the important base stations into an important base station set; The step S40 comprises: S410: Eliminate a data collection base station from the plurality of data collection base stations to obtain a remaining base station partition set of the remaining data collection base stations, and obtain a second correct decision value of the remaining base station partition set; That is, if there are three data collection base stations, after eliminating one of the data collection base stations, the remaining base station partitioning set is obtained based on the remaining two data collection base stations. The remaining base station partitioning set is obtained in the same way as the data collection base station partitioning set, and the second correct decision value is obtained in the same way as the first correct decision value, which will not be repeated here.

[0034] S420: Obtaining a degree of change in decision-making ability based on the first correct decision value and the second correct decision value; The calculation formula for the degree of change in decision-making ability is: , in, Indicates that the data collection base station is removed The degree of change in decision-making ability after Indicates that the data collection base station is removed The second correct decision value after represents the first correct decision value.

[0035] S430: Compare the degree of change in the decision-making ability with a first threshold. If the degree of change in the decision-making ability is greater than the first threshold, the eliminated data collection base station is an important base station. If the degree of change in the decision-making ability is less than the first threshold, the eliminated data collection base station is an initial redundant base station.

[0036] It can be understood that by eliminating the data collection base stations one by one, the influence of all the data collection base stations on the decision-making ability after being eliminated can be obtained separately, and then the importance of the data collection base stations can be determined.

[0037] S50: Obtaining a third correct decision value of the important base station set, so as to determine whether the decision-making ability of the important base station set meets the requirements according to the first correct decision value and the third correct decision value; if the decision-making ability of the important base station set meets the requirements, selecting all the initial redundant base stations as the final redundant base stations, and eliminating the final redundant base stations; The third correct decision value is obtained in the same manner as the first correct decision value, which will not be described in detail here. The judgment formula for whether the decision-making ability of the important base station set meets the requirements is: , in, represents the third correct decision value, represents the first correct decision value, represents a first threshold. That is, when the difference between the ratio of the third correct decision value to the first correct decision value and 1 is less than the first threshold, it is determined that the decision-making ability of the important base station set meets the requirements; conversely, when the difference between the ratio of the third correct decision value to the first correct decision value and 1 is greater than the first threshold, the decision-making ability of the important base station set does not meet the requirements.

[0038] If the decision-making ability of the important base station set does not meet the requirement, selecting a base station to be added from a plurality of the initial redundant base stations, and adding the base station to be added to the important base station set; In this embodiment, the decision-making capability change impact degree of several initial redundant base stations is ranked, and the decision-making capability change impact degree can be obtained based on step S40, and the initial redundant base station with the highest decision-making capability change impact degree is selected as the base station to be added, thereby forming a new important base station set.

[0039] Obtaining a fourth correct decision value of the important base station set after the base station to be added is added, so as to determine whether the decision-making capability of the important base station set after the base station to be added meets the requirements according to the first correct decision value and the fourth correct decision value; The fourth correct decision value is obtained in the same steps as the first correct decision value, and the judgment of whether the decision-making ability meets the requirements is consistent with the above judgment, which will not be repeated here.

[0040] If the decision-making ability of the important base station set after adding the base station to be added meets the requirement, the remaining initial redundant base stations are selected as the final redundant base stations, and the final redundant base stations are eliminated; It should be noted that if the decision-making capability still does not meet the requirements, the next base station to be added will continue to be selected until the decision-making capability meets the requirements. It should be noted that the first equivalent division can be completed in the meteorological forecasting agency, and the second equivalent division can be completed in the data collection base station, and then the first equivalent division and the second equivalent division are encrypted and uploaded to the central cloud server for subsequent processing to further improve the security of the data. After proposing the final redundant base station, the meteorological forecasting agency can make weather forecasts based on the data collected by the remaining data collection base stations.

[0041] By constructing the first equivalence partition with the first unique identifier and constructing the second equivalence partition with the second unique identifier, the result data record and the process data record themselves are not directly transmitted and processed, thereby ensuring the privacy and security of the data; the first correct decision value is used to analyze the impact of changes in the decision-making ability of the meteorological forecasting system by different data acquisition base stations, and then the importance of different data acquisition base stations is determined, and then, under the premise of ensuring the stability of the decision-making ability of the meteorological forecasting system, the number of data acquisition base stations is simplified to the greatest extent, that is, while reducing the overall data volume, data acquisition base stations with lower data quality are filtered out, which solves the problems of data redundancy, computational complexity and inconsistent data quality to a certain extent, and improves the accuracy and efficiency of the prediction.

[0042] See also Figure 2 The second embodiment of the present invention provides a multi-institution collaborative monitoring and filtering system, which is applied to the multi-institution collaborative monitoring and filtering method described in the above embodiment, and will not be repeated hereafter. As used below, the terms "module", "unit", "sub-unit", etc. may be a combination of software and / or hardware that implements predetermined functions. Although the apparatus described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and conceivable.

[0043] The system comprises: A first acquisition module 10 is used to obtain a plurality of result data records corresponding to weather from a meteorological forecasting agency, extract a plurality of decision attribute values ​​from the plurality of result data records based on decision attributes, and assign a first unique identifier to each of the decision attribute values ​​to construct a first equivalence partition, wherein the first equivalence partition includes a plurality of first equivalence classes; The first acquisition module 10 includes: The first unit is used to compare whether different decision attribute values ​​are the same; A second unit is used for merging the first unique identifiers corresponding to the same decision attribute values ​​into a plurality of first sets if there are the same decision attribute values, and dividing the first unique identifiers corresponding to different decision attribute values ​​into a plurality of second sets; A third unit is used to define the first set and the second set as a first equivalence class, and combine a plurality of the first equivalence classes into a first equivalence partition; A second acquisition module 20 is used to acquire a number of process data records corresponding to the weather from the data acquisition base station, the number of the process data records corresponding to the number of the result data records, extract a collection attribute value group from the process data record based on a number of collection attributes, and assign a second unique identifier to each of the collection attribute value groups to construct a second equivalence partition, wherein the second equivalence partition includes a number of second equivalence classes; The second acquisition module 20 includes: A fourth unit is used to obtain a plurality of collection attributes corresponding to the data collection base station, match the collection attributes with the process data record to extract a collection attribute value corresponding to the collection attribute from the process data record, and combine a plurality of the collection attribute values ​​into a collection attribute value group; A fifth unit is used to compare whether different acquisition attribute value groups are the same; a sixth unit, configured to merge the second unique identifiers corresponding to the same collection attribute value group into a plurality of third sets if the same collection attribute value group exists, and to divide the second unique identifiers corresponding to different collection attribute value groups into a plurality of fourth sets; A seventh unit is used to define the third set and the fourth set as second equivalence classes, and combine a plurality of the second equivalence classes into a second equivalence partition; A combining module 30 is used to combine the second equivalent partitions of different data collection base stations into a data collection base station partition set including a plurality of third equivalent classes, and determine a first correct decision value of the data collection base station partition set in combination with the first equivalent partition; The combined module 30 comprises: An eighth unit is used to select one of the second equivalent partitions as a reference equivalent partition, and select the remaining second equivalent partitions as equivalent partitions to be checked; A ninth unit, configured to perform intersection judgment on each of the second equivalence classes in the benchmark equivalence partition and each of the second equivalence classes in the to-be-checked equivalence partition; A tenth unit is used for extracting the intersection part if there is an intersection part, and forming a fifth set, defining the fifth set as a third equivalence class, and combining a plurality of the third equivalence classes into a data collection base station partition set; An eleventh unit is used to determine whether each of the third equivalence classes in the data collection base station partition set is completely included in a certain first equivalence class in the first equivalence partition; A twelfth unit is used to determine a first correct decision value of the data collection base station partition set based on the number of the third equivalence classes in a certain first equivalence class that is completely included in the first equivalence partition; A classification module 40, configured to determine, based on the first correct decision value, that the data collection base station is an important base station or an initial redundant base station, and merge the important base stations into an important base station set; The classification module 40 includes: A thirteenth unit is used to eliminate a data collection base station from the plurality of data collection base stations to obtain a remaining base station partition set of the remaining data collection base stations, and obtain a second correct decision value of the remaining base station partition set; A fourteenth unit, configured to obtain a degree of change in decision-making ability based on the first correct decision value and the second correct decision value; A fifteenth unit is used to compare the decision-making ability change degree with a first threshold value. If the decision-making ability change degree is greater than the first threshold value, the eliminated data collection base station is an important base station. If the decision-making ability change degree is less than the first threshold value, the eliminated data collection base station is an initial redundant base station. An execution module 50 is used to obtain a third correct decision value of the important base station set, so as to determine whether the decision-making ability of the important base station set meets the requirements according to the first correct decision value and the third correct decision value; if the decision-making ability of the important base station set meets the requirements, all the initial redundant base stations are selected as the final redundant base stations, and the final redundant base stations are eliminated; The execution module 50 is also used to select a base station to be added from a number of the initial redundant base stations and add the base station to the important base station set if the decision-making ability of the important base station set does not meet the requirements; obtain a fourth correct decision value of the important base station set after the base station to be added is added, so as to determine whether the decision-making ability of the important base station set after the base station to be added is added meets the requirements according to the first correct decision value and the fourth correct decision value; if the decision-making ability of the important base station set after the base station to be added is added meets the requirements, the remaining initial redundant base stations are selected as the final redundant base stations, and the final redundant base stations are eliminated.

[0044] The present invention also provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the multi-agency collaborative monitoring and filtering method as described in the above technical solution is implemented.

[0045] The present invention also provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the multi-institutional collaborative monitoring and filtering method described in the above technical solution is implemented.

[0046] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" 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 may be combined in any one or more embodiments or examples in a suitable manner.

[0047] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A multi-organization collaborative monitoring filtering method for eliminating final redundant base stations from a number of data collection base stations, characterized in that: The multi-agency collaborative monitoring and filtering method comprises the following steps: Obtaining a number of result data records corresponding to weather from a meteorological forecasting agency, extracting a number of decision attribute values ​​from the number of result data records based on decision attributes, and assigning a first unique identifier to each of the decision attribute values ​​to construct a first equivalence partition, wherein the first equivalence partition includes a number of first equivalence classes; Acquire a number of process data records corresponding to weather from the data collection base station, the number of the process data records corresponding to the number of the result data records, extract a collection attribute value group from the process data record based on a number of collection attributes, assign a second unique identifier to each of the collection attribute value groups to construct a second equivalence partition, the second equivalence partition including a number of second equivalence classes; Combining the second equivalent partitions of different data collection base stations into a data collection base station partition set including a plurality of third equivalent classes, and determining a first correct decision value of the data collection base station partition set in combination with the first equivalent partition; Based on the first correct decision value, the data collection base station is judged to be an important base station or an initial redundant base station, and the important base stations are merged into an important base station set; Obtain the third correct decision value of the important base station set to determine whether the decision-making ability of the important base station set meets the requirements based on the first correct decision value and the third correct decision value; if the decision-making ability of the important base station set meets the requirements, select all the initial redundant base stations as the final redundant base stations, and eliminate the final redundant base stations.

2. The multi-institutional collaborative monitoring and filtering method according to claim 1 is characterized in that: The step of constructing a first equivalence partition, wherein the first equivalence partition includes a plurality of first equivalence classes, comprises: comparing whether different decision attribute values ​​are the same; If the same decision attribute value exists, the first unique identifiers corresponding to the same decision attribute value are respectively combined into a plurality of first sets, and the first unique identifiers corresponding to different decision attribute values ​​are respectively divided into a plurality of second sets; The first set and the second set are both defined as first equivalence classes, and a number of the first equivalence classes are combined into a first equivalence partition.

3. The multi-institutional collaborative monitoring and filtering method according to claim 1 is characterized in that: The step of extracting a plurality of acquisition attribute value groups from the process data record based on a plurality of acquisition attributes is specifically as follows: Acquire a plurality of acquisition attributes corresponding to the data acquisition base station, match the acquisition attributes with the process data record to extract acquisition attribute values ​​corresponding to the acquisition attributes from the process data record, and combine a plurality of the acquisition attribute values ​​into an acquisition attribute value group.

4. The multi-institutional collaborative monitoring and filtering method according to claim 1 is characterized in that: The step of constructing a second equivalence partition, wherein the second equivalence partition includes a plurality of second equivalence classes, comprises: comparing whether different groups of collected attribute values ​​are the same; If the same collection attribute value group exists, the second unique identifiers corresponding to the same collection attribute value group are respectively combined into a plurality of third sets, and the second unique identifiers corresponding to different collection attribute value groups are respectively divided into a plurality of fourth sets; The third set and the fourth set are both defined as second equivalence classes, and a number of the second equivalence classes are combined into a second equivalence partition.

5. The multi-institutional collaborative monitoring and filtering method according to claim 1 is characterized in that: The step of merging the second equivalent partitions of different data collection base stations into a data collection base station partition set comprises: Selecting one of the second equivalent partitions as a benchmark equivalent partition, and selecting the remaining second equivalent partitions as equivalent partitions to be checked; Perform intersection judgment on each of the second equivalence classes in the benchmark equivalence partition and each of the second equivalence classes in the to-be-checked equivalence partition; If there is an intersection part, the intersection part is extracted to form a fifth set, the fifth set is defined as a third equivalence class, and a number of the third equivalence classes are combined into a data collection base station partition set.

6. The multi-institutional collaborative monitoring and filtering method according to claim 1 is characterized in that: The step of determining the first correct decision value of the data acquisition base station partition set in combination with the first equivalent partition comprises: Determine whether each of the third equivalence classes in the data acquisition base station partition set is completely included in a first equivalence class in the first equivalence partition; A first correct decision value of the data collection base station partition set is determined based on the number of the third equivalence classes that are completely included in a certain first equivalence class in the first equivalence partition.

7. The multi-organization collaborative monitoring and filtering method according to claim 1 is characterized in that: The step of judging whether the data acquisition base station is an important base station or an initial redundant base station based on the first correct decision value comprises: Eliminate a data collection base station from the plurality of data collection base stations to obtain a remaining base station partition set of the remaining data collection base stations, and obtain a second correct decision value of the remaining base station partition set; Obtaining a degree of change in decision-making ability based on the first correct decision value and the second correct decision value; The degree of change in the decision-making ability is compared with a first threshold. If the degree of change in the decision-making ability is greater than the first threshold, the eliminated data collection base station is an important base station. If the degree of change in the decision-making ability is less than the first threshold, the eliminated data collection base station is an initial redundant base station.

8. The multi-organization collaborative monitoring and filtering method according to claim 7 is characterized in that: The calculation formula for the degree of change in decision-making ability is: ， in, Indicates that the data collection base station is removed The degree of change in decision-making ability after Indicates that the data collection base station is removed The second correct decision value after represents the first correct decision value.

9. The multi-organization collaborative monitoring and filtering method according to claim 1, characterized in that: In the step of obtaining the third correct decision value of the important base station set, determining whether the decision-making ability of the important base station set meets the requirements according to the first correct decision value and the third correct decision value, if the decision-making ability of the important base station set meets the requirements, selecting all the initial redundant base stations as the final redundant base stations, and eliminating the final redundant base stations, further includes: If the decision-making ability of the important base station set does not meet the requirement, selecting a base station to be added from a plurality of the initial redundant base stations, and adding the base station to be added to the important base station set; Obtaining a fourth correct decision value of the important base station set after the base station to be added is added, so as to determine whether the decision-making capability of the important base station set after the base station to be added meets the requirements according to the first correct decision value and the fourth correct decision value; If the decision-making capability of the important base station set after adding the base station to be added meets the requirement, the remaining initial redundant base stations are selected as the final redundant base stations, and the final redundant base stations are eliminated.

10. A multi-institution collaborative monitoring and filtering system, applied to the multi-institution collaborative monitoring and filtering method as claimed in any one of claims 1 to 9, characterized in that: The system comprises: A first acquisition module is used to obtain a plurality of result data records corresponding to weather from a meteorological forecasting agency, extract a plurality of decision attribute values ​​from the plurality of result data records based on decision attributes, and assign a first unique identifier to each of the decision attribute values ​​to construct a first equivalence partition, wherein the first equivalence partition includes a plurality of first equivalence classes; A second acquisition module is used to acquire a number of process data records corresponding to the weather from the data acquisition base station, the number of the process data records corresponding to the number of the result data records, extract a collection attribute value group from the process data record based on a number of collection attributes, and assign a second unique identifier to each of the collection attribute value groups to construct a second equivalence partition, wherein the second equivalence partition includes a number of second equivalence classes; A combining module, used for combining the second equivalent partitions of different data collection base stations into a data collection base station partition set including a plurality of third equivalent classes, and determining a first correct decision value of the data collection base station partition set in combination with the first equivalent partition; a classification module, configured to determine, based on the first correct decision value, that the data collection base station is an important base station or an initial redundant base station, and merge the important base stations into an important base station set; An execution module is used to obtain a third correct decision value of the important base station set, so as to determine whether the decision-making ability of the important base station set meets the requirements according to the first correct decision value and the third correct decision value; if the decision-making ability of the important base station set meets the requirements, all the initial redundant base stations are selected as the final redundant base stations, and the final redundant base stations are eliminated.

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