A cloud data processing system for measuring stations and Beidou base stations

Abstract

The invention discloses a cloud data processing system for a measuring station and a Beidou base station, and relates to the technical field of data processing. The system discloses a data acquisition module, a record deletion module, a base station acquisition module, a data reference module, and an analysis and adjustment module. By setting the record deletion module, the measuring station can be classified into a registered measuring station and an initial measuring station, and the data acquisition records of the registered measuring station can be audited and processed according to the classification result. By constructing a data prediction model and a deviation analysis model, the data acquisition records that affect subsequent references can be intelligently judged and deleted to avoid affecting subsequent references and ensure high-precision measurement of the measuring station. The data reference module can be set to set a default ratio to analyze the original data in the data acquisition record, analyze the spatial position information of the measuring station according to the reference data obtained after analysis, and reasonably set the data analysis amount of the system on the premise of ensuring accurate measurement of the spatial position information of the measuring station.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and more specifically, to a cloud data processing system for a measuring station and a Beidou base station. Background Art

[0002] The high-precision Beidou data processing system can estimate the location of the measuring station. Each measuring station needs to use multiple Beidou base stations for data monitoring. The processing system performs corresponding analysis based on the monitoring data and performs high-precision measurement of the spatial position of the measuring station through the analyzed data.

[0003] The current Beidou data processing system usually needs to parse a large amount of Beidou monitoring data when estimating the position of the measuring station, which causes a lot of data analysis pressure on the data processing system. At the same time, a large amount of Beidou monitoring data is prone to have monitoring data with large deviations. Such monitoring data is meaningless in subsequent analysis, so it will cause invalid analysis of the system.

[0004] Therefore, it is particularly important to provide a data processing system that can not only ensure high-precision measurement at the measuring station, but also reasonably analyze the monitoring data of the measuring station. Summary of the invention

[0005] In view of the shortcomings of the prior art, the object of the present invention is to provide a cloud data processing system for a measurement station and a Beidou base station.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A cloud data processing system for a measuring station and a Beidou base station, comprising a data acquisition module, a record deletion and protection module, a base station acquisition module, a data reference module, and an analysis and adjustment module;

[0008] The data acquisition module is used to control a preset number of Beidou base stations to collect raw data from the measurement station based on a preset cycle, and each Beidou base station generates a data acquisition record after each acquisition;

[0009] The record deletion module is used to delete some data collection records and store the remaining data collection records in the cloud server, specifically:

[0010] Get the measurement history duration of the measurement station, set the measurement boundary duration, when the measurement history duration of the measurement station ≥ the measurement boundary duration, mark the measurement station as a registered measurement station, when the measurement history duration of the measurement station < the measurement boundary duration, mark the measurement station as an initial measurement station;

[0011] When the measuring station is a registered measuring station, obtain the reference data of the registered measuring station in the previous preset period, use the reference data as the data of the data prediction model of the registered measuring station, obtain the prediction data of the registered measuring station in the current preset period, obtain all data collection records of the registered measuring station in the current preset period, use the original data of each data collection record as the input data of the deviation analysis model to which the registered measuring station belongs, obtain the output data of the deviation analysis model, mark the training label of the output data of the deviation analysis model as the deviation analysis value, obtain the standard reference value Ht of the Beidou base station for the registered measuring station, set the standard reference threshold, when the standard reference value Ht≥the standard reference threshold, delete the data collection record of the Beidou base station for the registered measuring station in the current preset period, and mark the current time as the record deletion moment, when the standard reference value Ht<the standard reference threshold, retain the data collection record of the Beidou base station for the registered measuring station in the current preset period;

[0012] When the measuring station is an initial measuring station, all data collection records of the initial measuring station within the current preset period are retained;

[0013] Store the retained data collection records in a cloud server;

[0014] The base station acquisition module is used to cancel the subsequent raw data acquisition of the measurement station by the Beidou base station, specifically: obtain the reference deletion value Rw of the Beidou base station, set the reference deletion threshold, when the reference deletion value Rw of the Beidou base station ≥ the reference deletion threshold, cancel the subsequent raw data acquisition of the measurement station by the Beidou base station, when the reference deletion value Rw of the Beidou base station < the reference deletion threshold, no processing is performed;

[0015] The data reference module is used to parse the original data according to the default ratio, specifically: obtain all data collection records of the registered measurement station in the current preset period, extract the original data in the data collection records according to the default ratio for parsing, obtain reference data, and analyze the spatial location information of the measurement station through the reference data;

[0016] The analysis and adjustment module is used to adjust the default ratio of the raw data extracted by the registered measurement station, specifically:

[0017] The data analysis value Wh of the registered measurement station is obtained, and the data analysis high value and the data analysis low value are set, wherein the data analysis high value > the data analysis low value. When the data analysis value Wh of the registered measurement station ≥ the data analysis high value, the default ratio of the original data extracted by the registered measurement station is adjusted upward; when the data analysis low value ≤ the data analysis value Wh of the registered measurement station < the data analysis high value, the default ratio of the original data extracted by the registered measurement station is maintained; when the data analysis value Wh of the registered measurement station < the data analysis low value, the default ratio of the original data extracted by the registered measurement station is adjusted downward.

[0018] Furthermore, the measurement history duration of the measurement station is obtained by the following steps: obtaining the initial measurement time of the measurement station, calculating the time difference between the current time of the system and the initial measurement time of the measurement station, and obtaining the measurement history duration of the measurement station.

[0019] Furthermore, the data collection records include the measurement station number, Beidou base station number, original data, and data collection time.

[0020] Furthermore, the data prediction model of the registered measurement station is obtained through the following steps: obtaining reference data of the registered measurement station before the current time of the system, marking the reference data as training data, assigning training labels to the training data, dividing the training data into a training set and a validation set according to a set ratio, constructing a neural network model, and iteratively training the neural network model through the training set and the validation set. When the number of iterative training is greater than the iteration number threshold, it is determined that the neural network model has completed training, and the trained neural network model is marked as a data prediction model. The data prediction model is used to predict the original data of the registered measurement station in the next preset period, and the predicted original data is marked as predicted data.

[0021] Furthermore, the deviation analysis model of the registered measurement station is obtained through the following steps: obtaining the predicted data and reference data of the registered measurement station before the current time of the system, marking the predicted data and reference data as training data, assigning training labels to the training data, dividing the training data into training sets and validation sets according to a set ratio, constructing a neural network model, and iteratively training the neural network model with the training set and the validation set. When the number of iterative training times is greater than the iteration number threshold, it is determined that the neural network model has completed training, and the trained neural network model is marked as a deviation analysis model. The larger the training label value of the output data of the deviation analysis model, the greater the deviation between the predicted data and the original data within the current preset period.

[0022] Furthermore, the standard reference value Ht of the Beidou base station for the registered measurement station is obtained by the following steps: obtaining n data collection records of the same Beidou base station for the same registered measurement station before the current time of the system, obtaining the deviation analysis value corresponding to each data collection record, setting the deviation analysis threshold, and when the deviation analysis value ≥ the deviation analysis threshold, marking the corresponding data collection record as a deviation collection record, sorting the data collection moments of the deviation collection record in chronological order, calculating the time difference between the two adjacent data collection moments after sorting, obtaining the deviation collection interval, summing up all the deviation collection intervals and taking the average to obtain the deviation collection average interval, and marking it as Es, summing up the deviation analysis values ​​of all the deviation collection records and taking the average to obtain the deviation analysis mean, and marking it as Pe, obtaining the total number of deviation collection records, and marking it as Nd, using the formula The standard reference value Ht of the Beidou base station for the registered measurement station is obtained, where a1 is the deviation collection interval, a2 is the deviation analysis mean, and a3 is the deviation collection quantity coefficient.

[0023] Furthermore, the data analysis value Wh of the registered measurement station is obtained by the following steps: all deviation analysis values ​​of the registered measurement station in the current preset period are summed and averaged to obtain the average deviation value, all average deviation values ​​in m preset periods before the current time of the system are obtained, all average deviation values ​​are sorted in the order of the periods, the difference between the next average deviation value and the previous average deviation value after sorting is calculated to obtain the average deviation value, and the analysis average deviation threshold is set. When the average deviation value ≥ the analysis average deviation threshold, the average deviation value is marked as the average excess deviation value, and all the average excess deviation values ​​are summed to obtain the average excess total deviation value, and marked as Mk, obtain the total number of mean deviation values ​​marked as mean excess deviation values, and mark it as Yg. When the mean deviation value is less than the analysis mean deviation threshold, mark the mean deviation value as mean low deviation value, sum up all mean low deviation values ​​to obtain the mean low total deviation value, and mark it as Fb. Obtain the total number of mean deviation values ​​marked as mean low deviation values, and mark it as Zc. Use the formula Wh=Mk×b1+Yg×b2-Fb×b3-Zc×b4 to obtain the data analysis value Wh of the registered measurement station, where b1 is the mean excess total deviation value coefficient, b2 is the mean excess deviation quantity coefficient, b3 is the mean low total deviation value coefficient, and b4 is the mean low deviation quantity coefficient.

[0024] Furthermore, the reference deletion value Rw of the Beidou base station is obtained by the following steps: obtaining all the record deletion times of the Beidou base station for the measuring station before the current time of the system, sorting all the record deletion times in chronological order, calculating the time difference between the two adjacent record deletion times after sorting, obtaining the record deletion interval, summing up all the record deletion intervals and taking the average, obtaining the record deletion average interval, and marking it as Js, obtaining the total number of record deletion times, and marking it as Ed, using the formula The reference deletion value Rw of the Beidou base station is obtained, where c1 is the record deletion interval coefficient and c2 is the record deletion quantity coefficient.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] 1. Set up the record deletion and protection module, which can classify the measurement stations into registered measurement stations and initial measurement stations, and review and process the data collection records of the registered measurement stations according to the classification results. By building a data prediction model and a deviation analysis model, the data collection records that affect subsequent references can be intelligently judged and deleted to avoid affecting subsequent references and ensure high-precision measurement of the measurement stations. Set up the data reference module, which can set the default ratio to parse the original data in the data collection records, and analyze the spatial location information of the measurement station according to the reference data obtained after parsing. Under the premise of ensuring the accurate measurement of the spatial location information of the measurement station, the data parsing amount of the system can be reasonably set to ensure that the system can efficiently parse the original data of the measurement station;

[0027] 2. Setting up the base station sampling module can intelligently analyze the Beidou base station and promptly cancel the subsequent measurement work of the Beidou base station that affects the reference, thereby reducing the measurement pressure of the Beidou base station while ensuring subsequent high-precision measurement;

[0028] 3. Set up the analysis ratio adjustment module to adjust the default ratio of the original data extracted from the registered measurement station according to the data analysis value. On the basis of ensuring high-precision measurement of the measurement station, the subsequent data analysis volume of different registered measurement stations can be rationally adjusted. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is a principle block diagram of the record deletion module of the present invention;

[0030] Figure 2 It is a principle block diagram of the data reference module of the present invention. DETAILED DESCRIPTION

[0031] Example 1

[0032] Reference Figure 1-Figure 2, a cloud data processing system for a measuring station and a Beidou base station, including a data acquisition module, a record deletion module, and a data reference module.

[0033] The data acquisition module is used to control a preset number of Beidou base stations to collect the raw data of the measuring station based on a preset cycle. Each Beidou base station generates a data acquisition record after each acquisition; the data acquisition record includes the measuring station, the Beidou base station, the raw data, and the data acquisition time. Each measuring station corresponds to multiple Beidou base stations, and multiple Beidou base stations are required to monitor and obtain the location data of the measuring station, and analyze the measuring station accordingly through the parsed data. The preset cycle range and the preset number can be set as needed. For example, if the preset cycle is 3 hours and the preset number is 24, when monitoring measurement station No. 1, 24 Beidou base stations are controlled every 3 hours to collect the original data of measurement station No. 1, and 24 data collection records are generated in each cycle, namely data collection record 1, data collection record 2, ..., data collection record 24. The measurement station to which data collection record 1 belongs is measurement station No. 1, the Beidou base station to which it belongs is Beidou base station No. 1, and the data collection record is XXXX-XX-XX-DD XX:XX:XX. The measurement station to which data collection record 2 belongs is measurement station No. 1, the Beidou base station to which it belongs is Beidou base station No. 2, and the data collection record is XXXX-XX-XX-DD XX:XX:XX, ..., the measurement station to which data collection record 24 belongs is measurement station No. 1, the Beidou base station to which it belongs is Beidou base station No. 24, and the data collection record is XXXX-XX-XX-DD XX:XX:XX.

[0034] The record deletion module is used to delete some data collection records and store the remaining data collection records in the cloud server. Specifically:

[0035] Get the measurement history duration of the measurement station, set the measurement demarcation duration, when the measurement history duration of the measurement station ≥ the measurement demarcation duration, mark the measurement station as a registered measurement station, when the measurement history duration of the measurement station < the measurement demarcation duration, mark the measurement station as an initial measurement station. The measurement history duration of the measurement station is obtained by the following steps: Get the initial measurement time of the measurement station (that is, the time when the measurement station first measures in the system), calculate the time difference between the current system time and the initial measurement time of the measurement station, and get the measurement history duration of the measurement station.

[0036] When the measurement station is a registered measurement station, obtain the reference data of the registered measurement station in the previous preset period, use the reference data as the data of the data prediction model of the registered measurement station, obtain the predicted data of the registered measurement station in the current preset period, and the data prediction model of the registered measurement station is obtained through the following steps: obtain the reference data of the registered measurement station before the current time of the system, mark the reference data as training data, assign training labels to the training data, and divide the training data into training sets and validation sets according to a set ratio. The set ratio of the training set to the validation set includes but is not limited to 1:2 and 1:3. Construct a neural network model, iteratively train the neural network model through the training set and the validation set. When the number of iterative training is greater than the iteration number threshold, it is determined that the neural network model has completed training, and the trained neural network model is marked as a data prediction model. The training label of the data output by the data prediction model is to predict the original data of the registered measurement station in the next preset period, and the predicted original data is marked as predicted data.

[0037] Obtain all data collection records of the registered measurement station within the current preset period, use the original data of each data collection record as the input data of the deviation analysis model to which the registered measurement station belongs (each registered measurement station corresponds to a deviation analysis model), obtain the output data of the deviation analysis model, and mark the training label of the output data of the deviation analysis model as a deviation analysis value. The deviation analysis model of the registered measurement station is obtained through the following steps: obtain the predicted data and reference data of the registered measurement station before the current time of the system, mark the predicted data and the reference data as training data, assign training labels to the training data, divide the training data into a training set and a validation set according to a set ratio, and the set ratio of the training set validation set includes but is not limited to 1:3 and 1:4, construct a neural network model, iteratively train the neural network model through the training set and the validation set, and when the number of iterative training is greater than the iteration number threshold, it is determined that the neural network model has completed training, and the trained neural network model is marked as a deviation analysis model. The larger the training label value of the output data of the deviation analysis model, the greater the deviation between the predicted data and the original data within the current preset period.

[0038] Obtain the standard reference value Ht of the Beidou base station for the registered measurement station. The standard reference value Ht of the Beidou base station for the registered measurement station is obtained by the following steps: obtain n data collection records of the same Beidou base station for the same registered measurement station before the current time of the system, obtain the deviation analysis value corresponding to each data collection record, set the deviation analysis threshold, when the deviation analysis value ≥ the deviation analysis threshold, mark the corresponding data collection record as a deviation collection record, sort the data collection moments of the deviation collection record in chronological order, calculate the time difference between the two adjacent data collection moments after sorting, obtain the deviation collection interval, sum up all the deviation collection intervals and take the average, obtain the deviation collection average interval, and mark it as Es, sum up all the deviation analysis values ​​of the deviation collection records and take the average, obtain the deviation analysis average, and mark it as Pe, obtain the total number of deviation collection records, and mark it as Nd, use the formula The standard reference value Ht of the Beidou base station for the registered measurement station is obtained, where a1 is the deviation collection interval, a2 is the deviation analysis mean, and a3 is the deviation collection quantity coefficient. The value of a1 is 0.83, the value of a2 is 0.47, and the value of a3 is 0.58. A standard reference threshold is set. When the standard reference value Ht ≥ the standard reference threshold, the data collection record of the Beidou base station for the registered measurement station in the current preset period is deleted, and the current time is marked as the record deletion time. When the standard reference value Ht < the standard reference threshold, the data collection record of the Beidou base station for the registered measurement station in the current preset period is retained.

[0039] When the measuring station is an initial measuring station, all data collection records of the initial measuring station within the current preset period are retained. The retained data collection records are stored in the cloud server. By setting up a record deletion module, the measuring stations can be classified into registered measuring stations and initial measuring stations, and the data collection records of the registered measuring stations can be reviewed and processed according to the classification results. By building a data prediction model and a deviation analysis model, the data collection records that affect subsequent references can be intelligently judged and deleted to avoid affecting subsequent references and ensure high-precision measurement of the measuring station.

[0040] The data reference module is used to parse the original data at the default ratio, specifically: obtain all data collection records of the registered measurement station in the current preset period (for example, if there are 24 data collection records in the current preset period, then obtain 24 data collection records), extract the original data in the data collection records at the default ratio for analysis (for example, if the default ratio is 50%, then extract the original data of 12 data collection records for analysis), obtain reference data, and analyze the spatial location information of the measurement station through the reference data. When setting the data reference module, you can set the default ratio to parse the original data in the data collection records, analyze the spatial location information of the measurement station based on the reference data obtained after analysis, and reasonably set the data analysis amount of the system on the premise of ensuring the accurate measurement of the spatial location information of the measurement station, so as to ensure that the system can efficiently parse the original data of the measurement station.

[0041] Example 2

[0042] On the basis of Example 1, it also includes a base station acquisition module, which is used to cancel the Beidou base station's subsequent raw data acquisition of the measuring station, specifically: obtaining the reference deletion value Rw of the Beidou base station, the reference deletion value Rw of the Beidou base station is obtained by the following steps: obtaining all record deletion times of the Beidou base station for the measuring station before the current time of the system, sorting all the record deletion times in chronological order, calculating the time difference between the two adjacent record deletion times after sorting, obtaining the record deletion interval, summing up all the record deletion intervals and taking the average, obtaining the record deletion average interval, and marking it as Js, obtaining the total number of record deletion times, and marking it as Ed, using the formula The reference deletion value Rw of the Beidou base station is obtained, where c1 is the record deletion interval coefficient, c2 is the record deletion quantity coefficient, the value of c1 is 0.72, and the value of c2 is 0.48. A reference deletion threshold is set. When the reference deletion value Rw of the Beidou base station is ≥ the reference deletion threshold, the Beidou base station cancels the subsequent raw data collection of the measurement station (for example, the measurement station originally collected raw data through 24 Beidou base stations, the Beidou base station is deleted, and the remaining 23 Beidou base stations collect raw data for the measurement station). When the reference deletion value Rw of the Beidou base station is < the reference deletion threshold, no processing is performed. Setting a base station acquisition module can intelligently analyze the Beidou base station and promptly cancel the subsequent measurement work of the Beidou base station that affects the reference, thereby reducing the measurement pressure of the Beidou base station while ensuring subsequent high-precision measurements.

[0043] Example 3

[0044] On the basis of Example 2, it also includes an analysis and adjustment module, which is used to adjust the default ratio of the raw data extracted by the registered measurement station, specifically:

[0045] The data analysis value Wh of the registered measurement station is obtained. The data analysis value Wh of the registered measurement station is obtained by the following steps: all deviation analysis values ​​of the registered measurement station in the current preset period are summed and averaged to obtain the average deviation value; all average deviation values ​​in m preset periods before the current system time are obtained; all average deviation values ​​are sorted in the order of the periods; the difference between the next average deviation value and the previous average deviation value after sorting is calculated to obtain the average deviation value; an analysis average deviation threshold is set; when the average deviation value ≥ the analysis average deviation threshold, the average deviation value is marked as an average excess deviation value; all average excess deviation values ​​are summed to obtain an average excess total deviation value, and marked as Mk; the average deviation value obtained is marked as an average excess deviation value. The total number of differences is marked as Yg. When the mean deviation value is less than the analysis mean difference threshold, the mean deviation value is marked as the mean low deviation value. All the mean low deviation values ​​are summed to obtain the mean low total deviation value and marked as Fb. The total number of mean deviation values ​​marked as mean low deviation values ​​is obtained and marked as Zc. The data analysis value Wh of the registered measurement station is obtained by using the formula Wh=Mk×b1+Yg×b2-Fb×b3-Zc×b4, where b1 is the mean excess total deviation value coefficient, b2 is the mean excess deviation quantity coefficient, b3 is the mean low total deviation value coefficient, and b4 is the mean low deviation quantity coefficient. The value of b1 is 0.69, the value of b2 is 0.56, the value of b3 is 0.68, and the value of b4 is 0.57. Set the data analysis high value and the data analysis low value, where the data analysis high value>data analysis low value. When the data analysis value Wh of the registered measurement station is ≥ the data analysis high value, the default ratio of the original data extracted by the registered measurement station is adjusted upward. If the default ratio is 50%, when the data analysis value Wh of the registered measurement station 1 ≥ the data analysis high value, the default ratio of the raw data extracted from the registered measurement station 1 is adjusted to above 50%. When the data analysis low value ≤ the data analysis value Wh of the registered measurement station < the data analysis high value, the default ratio of the raw data extracted from the registered measurement station is maintained. When the data analysis value Wh of the registered measurement station < the data analysis low value, the default ratio of the raw data extracted from the registered measurement station is adjusted downward. If the default ratio is 50%, when the data analysis value Wh of the registered measurement station 2 < the data analysis high value, the default ratio of the raw data extracted from the registered measurement station is adjusted to below 50%. The adjustment range of the default ratio can be adjusted according to the system settings. By setting the analysis ratio adjustment module, the default ratio of the raw data extracted from the registered measurement station can be adjusted according to the data analysis value. On the basis of ensuring the high-precision measurement of the measurement station, the subsequent data analysis volume of different registered measurement stations can be rationally adjusted.

[0046] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0047] 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. A cloud data processing system for a measurement station and a Beidou base station, characterized in that: Including data collection module, record deletion module, data reference module; The data acquisition module is used to control a preset number of Beidou base stations to collect raw data from the measurement station based on a preset cycle, and each Beidou base station generates a data acquisition record after each acquisition; The record deletion module is used to delete some data collection records and store the remaining data collection records in the cloud server, specifically: The measuring stations are classified into registered measuring stations or initial measuring stations according to the measuring history of the measuring stations; When the measuring station is a registered measuring station, the standard reference value Ht of the Beidou base station for the registered measuring station is obtained, and the data collection record of the registered measuring station is deleted or retained according to the standard reference value Ht; When the measuring station is an initial measuring station, all data collection records of the initial measuring station within the current preset period are retained; Store the retained data collection records in a cloud server; The data reference module is used to parse the original data according to the default ratio, specifically: obtain all data collection records of the registered measurement station in the current preset period, extract the original data in the data collection records according to the default ratio for parsing, obtain reference data, and analyze the spatial location information of the measurement station through the reference data; The data collection records of the registered measurement station are deleted or retained according to the standard reference value Ht, specifically: the reference data of the registered measurement station in the previous preset period is obtained, the reference data is used as the data of the data prediction model of the registered measurement station, the prediction data of the registered measurement station in the current preset period is obtained, all data collection records of the registered measurement station in the current preset period are obtained, the original data of each data collection record is used as the input data of the deviation analysis model to which the registered measurement station belongs, the output data of the deviation analysis model is obtained, the training label of the output data of the deviation analysis model is marked as the deviation analysis value, the standard reference value Ht of the Beidou base station for the registered measurement station is obtained, the standard reference threshold is set, when the standard reference value Ht≥the standard reference threshold, the data collection record of the Beidou base station for the registered measurement station in the current preset period is deleted, and the current time is marked as the record deletion moment, when the standard reference value Ht<the standard reference threshold, the data collection record of the Beidou base station for the registered measurement station in the current preset period is retained; The standard reference value Ht is obtained by the following steps: obtain n data collection records of the same Beidou base station for the same registered measurement station before the current time of the system, obtain the deviation analysis value corresponding to each data collection record, set the deviation analysis threshold, and when the deviation analysis value ≥ the deviation analysis threshold, mark the corresponding data collection record as a deviation collection record, sort the data collection moments of the deviation collection record in chronological order, calculate the time difference between the two adjacent data collection moments after sorting, and obtain the deviation collection interval, sum up all the deviation collection intervals and take the average to obtain the deviation collection average interval, and mark it as Es, sum up all the deviation analysis values ​​of the deviation collection records and take the average to obtain the deviation analysis mean, and mark it as Pe, obtain the total number of deviation collection records, and mark it as Nd, and use the formula The standard reference value Ht of the Beidou base station for the registered measurement station is obtained, where a1 is the deviation collection interval coefficient, a2 is the deviation analysis mean coefficient, and a3 is the deviation collection quantity coefficient.

2. The cloud data processing system of a measuring station and a Beidou base station according to claim 1, characterized in that: The measurement history duration of the measurement station is obtained by the following steps: obtaining the initial measurement time of the measurement station, calculating the time difference between the current time of the system and the initial measurement time of the measurement station, and obtaining the measurement history duration of the measurement station.

3. The cloud data processing system of a measuring station and a Beidou base station according to claim 2, characterized in that: Data collection records include measurement station number, Beidou base station number, original data, and data collection time.

4. The cloud data processing system of a measuring station and a Beidou base station according to claim 3, characterized in that: It also includes a base station sampling module, which is used to cancel the Beidou base station's subsequent original data collection for the measurement station. Specifically, the reference deletion value Rw of the Beidou base station is obtained, and a reference deletion threshold is set. When the reference deletion value Rw of the Beidou base station is ≥ the reference deletion threshold, the subsequent original data collection of the measurement station by the Beidou base station is canceled.

5. The cloud data processing system of a measuring station and a Beidou base station according to claim 4, characterized in that: The reference deletion value Rw of the Beidou base station is obtained by the following steps: obtain all the record deletion times of the Beidou base station for the measuring station before the current time of the system, sort all the record deletion times in chronological order, calculate the time difference between the two adjacent record deletion times after sorting, and obtain the record deletion interval, sum up all the record deletion intervals and take the average value to obtain the record deletion interval, and mark it as Js, obtain the total number of record deletion times, and mark it as Ed, and use the formula The reference deletion value Rw of the Beidou base station is obtained, where c1 is the record deletion interval coefficient and c2 is the record deletion quantity coefficient.

6. A cloud data processing system for a measurement station and a Beidou base station according to claim 5, characterized in that: It also includes an analysis and adjustment module, which is used to adjust the default ratio of the raw data extracted by the registered measurement station, specifically: The data analysis value Wh of the registered measurement station is obtained, and the data analysis high value and the data analysis low value are set, wherein the data analysis high value > the data analysis low value. When the data analysis value Wh of the registered measurement station ≥ the data analysis high value, the default proportion of the original data extracted by the registered measurement station is lowered; when the data analysis low value ≤ the data analysis value Wh of the registered measurement station < the data analysis high value, the default proportion of the original data extracted by the registered measurement station is maintained; when the data analysis value Wh of the registered measurement station < the data analysis low value, the default proportion of the original data extracted by the registered measurement station is increased.

7. A cloud data processing system for a measurement station and a Beidou base station according to claim 6, characterized in that: The data analysis value Wh of the registered measurement station is obtained by the following steps: summing up all deviation analysis values ​​of the registered measurement station in the current preset period and taking the average to obtain the average deviation value, obtaining all average deviation values ​​in the m preset periods before the current time of the system, sorting all average deviation values ​​in the order of the periods, calculating the difference between the next average deviation value and the previous average deviation value after sorting to obtain the average deviation value, setting the analysis average deviation threshold, when the average deviation value ≥ the analysis average deviation threshold, marking the average deviation value as the average excess deviation value, summing up all the average excess deviation values ​​to obtain the average excess total deviation value, and marking it as Mk , obtain the total number of average deviation values ​​marked as average excess deviation values, and mark it as Yg. When the average deviation value is less than the analysis average deviation threshold, mark the average deviation value as an average low deviation value. Sum all the average low deviation values ​​to obtain the average low total deviation value, and mark it as Fb. Obtain the total number of average deviation values ​​marked as average low deviation values, and mark it as Zc. Use the formula Wh=Mk×b1+Yg×b2-Fb×b3-Zc×b4 to obtain the data analysis value Wh of the registered measurement station, where b1 is the average excess total deviation value coefficient, b2 is the average excess deviation quantity coefficient, b3 is the average low total deviation value coefficient, and b4 is the average low deviation quantity coefficient.

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