Multi-source sounding data fusion processing method and related device

Abstract

The invention discloses a multi-source sounding data fusion processing method and a related device. The method comprises the steps of filtering a plurality of pieces of sounding second data received in real time based on a detection range and a geographic position range of sounding related equipment, then calculating a data quality score of each piece of sounding second data by using an established multi-dimensional sounding second data quality evaluation model, and calculating a data quality score of each piece of sounding second data according to the data quality score. And in the sounding second data from the same sonde, the sounding second data with the highest quality score in each second is spliced and fused into the complete sounding data of the sonde, and then the complete sounding data of each sonde is obtained through statistical arrangement. According to the data fusion method and device, it can be guaranteed that the data quality of each piece of sounding second data is high, then the data quality, integrity and reliability of the sounding second data obtained through splicing fusion can be guaranteed, and the data fusion mode is more efficient and reliable.

Description

Technical Field

[0001] This application relates to the field of meteorological radiosonde technology, specifically to the technical fields of radiosonde data quality assessment and radiosonde data filtering, and particularly to a method and related apparatus for fusion processing of multi-source radiosonde data. Background Technology

[0002] The third-generation meteorological radiosonde system is an intelligent drift radiosonde system based on the BeiDou navigation technology system, possessing "ascent-drift-descent" three-stage observation capabilities. The BeiDou radiosonde system employs a new air-to-ground IoT real-time linkage observation technology, forming a nationwide BeiDou radiosonde observation network. The data reception mode has shifted from single-station reception to multi-station intelligent network reception. This allows for multi-channel network reception of radiosonde data with matched frequencies, ensuring data integrity and real-time performance, provided the radiosonde and receiver frequencies are matched.

[0003] However, in this new radiosonde networking mode, there may be situations where multiple receivers receive radiosonde data from the same radiosonde, which could lead to overlap in the radiosonde data received by the data center. However, considering the individual differences of each receiver, there may also be differences in radiosonde data for the same radiosonde. Therefore, it is not feasible to simply delete or retain radiosonde data received by a particular receiver.

[0004] Therefore, there is an urgent need to find a fusion processing solution for multi-source radiosonde data to solve the problem that the current technology for processing multiple radiosonde data from the same radiosonde is simple and crude and cannot guarantee the data quality. Summary of the Invention

[0005] This application provides a method and related apparatus for fusion processing of multi-source radiosonde data, in order to solve the problem that the existing technology for processing multiple radiosonde data from the same radiosonde is simple and crude and cannot guarantee the data quality of the radiosonde data.

[0006] The technical solution is as follows: Firstly, a method for fusing and processing multi-source radiosonde data is provided, including: It receives several radiosonde seconds of data in real time from several radiosonde receivers; each radiosonde second carries at least a radiosonde identifier and a radiosonde receiver identifier; Based on the detection range of the radiosonde-related equipment and its geographical location, radiosonde second data that are outside the range are filtered out. Based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode, a multi-dimensional radiosonde second data quality assessment model is established. The multiple radiosonde second data points retained after filtering are grouped according to the radiosonde identifier; For a group containing at least two radionuclide data points: using the multi-dimensional radionuclide data quality assessment model, calculate and evaluate the data quality of each radionuclide data point in the group, and select the radionuclide data point with the highest quality score to obtain the radionuclide data point of the radiosonde corresponding to the group; The radiosonde second data of each radiosonde under the current radiosonde networking mode are statistically analyzed and merged according to the time series to obtain the complete radiosonde data of each radiosonde; among them, there are no radiosonde second data with overlapping time in the complete radiosonde data of each radiosonde.

[0007] In one possible implementation, a multi-dimensional radiosonde second data quality assessment model is established based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode. Specifically, this includes: Based on the radiosondes and receivers deployed in the current radiosonde networking mode, evaluation indicators are determined from attribute parameters used to characterize the quality of radiosonde second data in multiple dimensions. The evaluation indicators include: radiosonde positioning status, number of radiosonde satellites, radiosonde frequency drift deviation, radiosonde health, received signal strength of radiosonde receiver, and historical reliability. Based on the current sounding network mode, the entropy weight method is used to determine the weight of each evaluation indicator. The weight of each indicator is dynamically adjusted as the sounding network mode and sounding scenario change. Establish a sounding second data quality assessment model that combines multi-dimensional evaluation indicators with corresponding indicator weights.

[0008] In one possible implementation, each radiosonde second also carries the attribute parameter values ​​of the radiosonde and the radiosonde receiver; Using the aforementioned multi-dimensional radiosonde second data quality assessment model, the data quality of each radiosonde second data point in this group is calculated and evaluated, specifically including: For each sounding second data point in the aforementioned group, perform the following: The scores of each evaluation index are determined based on the attribute parameter values ​​of the radiosonde second data. Find the corresponding indicator weights for each evaluation indicator from the multi-dimensional sounding second data quality assessment model; The data quality score of the radiosonde data is obtained by summing the products of the scores of all evaluation indicators and their corresponding weights.

[0009] In one possible implementation, the scores of each evaluation index are determined based on the attribute parameter values ​​of the radiosonde second data, specifically including: The score is determined by whether the radiosonde is in a fixed position or not; Based on the number of radiosonde satellites, a normalized formula is used to calculate the score of the evaluation index for the number of radiosonde satellites. Based on the frequency drift deviation of the radiosonde, the score of the frequency drift deviation evaluation index is calculated by subtracting the deviation value from 1. The score is determined by whether the radiosonde's battery voltage is within the normal range, and either 0 or 1 point is assigned. The score of the received signal strength evaluation index of the radiosonde receiver is determined based on the ratio of the received signal strength of the radiosonde receiver to the maximum received signal strength. The ratio of the daily number of warnings from the radiosonde receiver to the daily maximum warning threshold is used as the historical reliability assessment score for the radiosonde receiver.

[0010] In one possible implementation, the radiosonde data with the highest quality score is selected from the radiosonde data, specifically including: The timestamps of radiosonde data appearing in this group are counted, and the hash value of the timestamp is calculated using a hash algorithm; Create a bit and use a hash value to label the address of that bit, setting that address to store the quality score; where the index of the address in the bit array is the same as the index of the hash value labeling that address, and each address is 4 bits long; The quality scores of the radiosonde second data in the group are sequentially stored into the address. Each time a data is stored, it is compared with the already stored quality scores. Only the quality scores with higher scores are retained, and the quality scores with lower scores are removed. This process is repeated until the quality scores of all radiosonde second data are compared. The radiosonde second data corresponding to the final retained quality score is taken as the radiosonde second data with the highest quality score under the timestamp corresponding to that address.

[0011] In one possible implementation, the method further includes: All complete radiosonde data obtained through statistical analysis will be categorized and stored according to the radiosonde identification; and / or, All complete radiosonde data obtained through statistical analysis will be classified and stored according to the radiosonde receiver identifier.

[0012] Secondly, a multi-source sounding data fusion processing device is provided, comprising: The receiving module is used to receive in real time a number of radiosonde second data points received by a number of radiosonde receivers; each radiosonde second data point carries at least a radiosonde identifier and a radiosonde receiver identifier. The filtering module is used to filter radiosonde second data that are outside the detection range and geographical location range of the radiosonde-related equipment. A module is established to build a multi-dimensional radiosonde second data quality assessment model based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode. The grouping module is used to group multiple radiosonde second data points that have been filtered and retained according to the radiosonde identifier; The fusion module is used to calculate and evaluate the data quality of each radionuclide data in a group containing at least two radionuclide data points using the multi-dimensional radionuclide data quality assessment model, and select the radionuclide data with the highest quality score to obtain the radionuclide data of the radiosonde corresponding to the group. The statistics module is used to collect and organize the radiosonde second data of each radiosonde in the current radiosonde networking mode, and to splice and fuse the radiosonde second data according to the time series to obtain the complete radiosonde data of each radiosonde; wherein, there are no radiosonde second data with overlapping time in the complete radiosonde data of each radiosonde.

[0013] Thirdly, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the methods described above and any possible implementations.

[0014] Fourthly, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement the aspects described above and any possible implementation thereof.

[0015] Fifthly, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the aspects and any possible implementations described above.

[0016] The beneficial effects of the technical solution provided in this application include at least the following: As can be seen from the above technical solution, this application's embodiments, based on the detection range and geographical location of the radiosonde-related equipment, filter several real-time received radiosonde data points. Then, using an established multi-dimensional radiosonde data quality assessment model, the data quality score of each radiosonde data point is calculated. The radiosonde data points with the highest quality score from the same radiosonde are then spliced ​​and fused into a complete radiosonde data point for that radiosonde. Furthermore, the complete radiosonde data for each radiosonde is obtained through statistical analysis. This application can initially filter radiosonde data based on detection range and location range, eliminating radiosonde data that does not meet objective environmental and transmission requirements. Then, a multi-dimensional radiosonde data quality assessment model is used to assess the quality of each radiosonde data point, selecting the radiosonde data points with the highest quality scores from different receivers from multiple radiosonde data points from the same radiosonde, and then splicing and fusing them. This ensures high data quality for each radiosonde data point, thus guaranteeing the data quality, integrity, and reliability of the spliced ​​and fused radiosonde data. Moreover, this data fusion method is more efficient and reliable.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a schematic diagram of the radiosonde network architecture to which the fusion processing method provided in one embodiment of this application is applicable.

[0020] Figure 2 This is a schematic diagram of the steps of the multi-source sounding data fusion processing method provided in the embodiments of this application.

[0021] Figure 3 This is a schematic diagram of the fusion and splicing of radiosonde second data provided in an embodiment of this application.

[0022] Figure 4 This is a structural block diagram of a multi-source sounding data fusion processing device provided in one embodiment of this application.

[0023] Figure 5 This is a block diagram of the electronic device provided in the embodiments of this application. Detailed Implementation

[0024] The following description, in conjunction with the accompanying drawings, illustrates exemplary embodiments of this application, including various details to aid understanding. These embodiments should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0025] Obviously, the described embodiments are only some, not all, of the embodiments in this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without inventive effort are within the scope of protection of this application.

[0026] It should be noted that the terminal devices involved in the embodiments of this application may include, but are not limited to, smart devices such as mobile phones, personal digital assistants (PDAs), wireless handheld devices, and tablet computers; the display devices may include, but are not limited to, personal computers, televisions, and other devices with display functions.

[0027] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0028] Given that existing technologies for processing multiple radiosonde data from the same radiosonde are simplistic and cannot guarantee data quality, this application proposes a multi-source radiosonde data fusion processing scheme. The main inventive concept is as follows: based on the detection range and geographical location of the radiosonde-related equipment, several real-time received radiosonde data points are filtered. Then, using an established multi-dimensional radiosonde data quality assessment model, a data quality score is calculated for each radiosonde data point. The radiosonde data point with the highest quality score from the same radiosonde data point is then spliced ​​and fused into a complete radiosonde data point for that radiosonde. Finally, the complete radiosonde data for each radiosonde is statistically analyzed and processed. This application can initially filter radiosonde data based on detection range and location range, eliminating data that does not meet objective environmental and transmission requirements. Then, a multi-dimensional radiosonde data quality assessment model is used to evaluate the quality of each radiosonde data point, selecting the radiosonde data point with the highest quality score from different receivers from multiple radiosonde data points from the same radiosonde, and then splicing and fusing them. This ensures high data quality for each second of radiosonde data, thereby guaranteeing the data quality, integrity, and reliability of the radiosonde data obtained through splicing and fusion. Furthermore, this data fusion method is more efficient and reliable.

[0029] Reference Figure 1 The diagram shown is a schematic of the radiosonde network architecture to which the fusion processing method provided in this application embodiment applies.

[0030] like Figure 1 In this radiosonde network architecture, multiple radiosondes 101, multiple radiosonde receivers 102, multiple integrated processors 103, cloud integrated processors 104, radiosonde data hub equipment 105, and data center equipment 106 can be deployed, and business servers 107 can also be included.

[0031] Among them, multiple radiosondes 101 can be launched from different launch stations and perform corresponding radiosonde tasks in different radiosonde areas through different flight stages such as ascent, drift, and descent.

[0032] Multiple radiosonde receivers 102 can be distributed at different receiving stations to receive radiosonde data while matching the frequency of the radiosonde 101. For example... Figure 1 As shown, one radiosonde receiver 102 can receive radiosonde data from one radiosonde 101, multiple radiosonde receivers 102 can simultaneously receive radiosonde data from one radiosonde 101, and one radiosonde receiver 102 can also simultaneously receive radiosonde data from two radiosondes 101.

[0033] Subsequently, the radiosonde data can be processed by the integrated processor 103 or the cloud integrated processor 104, sent to the radiosonde data hub device 105 for central management, and then forwarded to the data center device 106 for data fusion processing. The data fusion scheme of this application mainly occurs in the data center device 106; other parts are not the core improvements of this application, so they will not be described in detail.

[0034] Considering the radiosonde networking mode in the aforementioned radiosonde network architecture, there may be situations where multiple radiosonde receivers 102 receive radiosonde data from the same radiosonde 101. Therefore, the data center device 106 is highly likely to receive identical or slightly different radiosonde data, all originating from the same radiosonde 101, which constitutes duplicate data for the data center device 106. Therefore, a multi-source radiosonde data fusion scheme is proposed (here, the source refers to the receiving source: the radiosonde receiver, not the sending radiosonde). By filtering and quality evaluating the radiosonde data, the optimal second-by-second data is selected and fused to obtain high-quality radiosonde data, ensuring that each radiosonde has only one record of radiosonde data. This avoids data duplication while efficiently and reliably fusing into complete, high-quality radiosonde data. It should be noted that since the radiosonde receiver receives and transmits radiosonde data transmitted one second at a time, this application, when receiving radiosonde data transmitted by the radiosonde receiver, is actually receiving radiosonde data transmitted one second at a time. The fusion processing device of this application performs a series of processing on each radiosonde second data and then splices and fuses them to obtain radiosonde data for a period of time.

[0035] The solutions involved in this application will be described in detail below through specific embodiments.

[0036] Reference Figure 2 The diagram illustrates the steps of a multi-source radiosonde data fusion processing method provided in this embodiment. It should be understood that the executing entity of this fusion processing method can be a multi-source radiosonde data fusion processing device. This device can be a hardware device with computer functions such as data calculation, processing, and storage, or a software module or component integrated into such hardware device. These hardware devices can be, for example, smart electronic devices such as computers, tablets, smartphones, and smart wearable devices, or various servers such as cloud servers and distributed servers. Preferably, the executing entity of the fusion processing method involved in this application can be a server, which, by deploying corresponding functions, provides radiosonde data fusion processing services in the data center device 106.

[0037] like Figure 2 As shown, the method for fusion processing of multi-source radiosonde data may include the following steps: Step 202: Receive several radiosonde second data points received by several radiosonde receivers in real time; each radiosonde second data point carries at least the radiosonde identifier and the radiosonde receiver identifier.

[0038] In this application, the multi-source radiosonde data fusion processing scheme can be implemented by fusing radiosonde data received every second. This radiosonde data can be radiosonde data received from multiple radiosondes by different radiosonde receivers. Taking the initial moment as an example, 10 radiosonde data are received from 10 radiosonde receivers (Receiver 1-Receiver 10), that is, each radiosonde data is received by one radiosonde receiver. These 10 radiosonde data points can come from the same or different radiosondes. For example, there are 4 radiosondes (radiosonde 1 to radiosonde 4). Receiver 1 and Receiver 2 receive radiosonde data from radiosonde 1 (radiosonde data 1 and radiosonde data 2), respectively. Receiver 3 receives radiosonde data from radiosonde 2 (radiosonde data 3). Receivers 4, 5, and 6 receive radiosonde data from radiosonde 3 (radiosonde data 4, radiosonde data 5, and radiosonde data 6), respectively. The remaining receivers receive radiosonde data from radiosonde 4 (radiosonde data 7, radiosonde data 8, radiosonde data 9, and radiosonde data 10), respectively.

[0039] Since the receiver and radiosonde for each radiosonde second data point may be different, each radiosonde second data point carries both a radiosonde receiver identifier and a radiosonde identifier to facilitate subsequent identification of the radiosonde second data point.

[0040] Step 204: Based on the detection range of the radiosonde-related equipment and its geographical location, filter the radiosonde second data that are outside the range.

[0041] Considering the possibility of errors in wireless data transmission, and taking into account the device's detection range and geographical location, data outside the specified range is filtered out: values ​​such as latitude and longitude, temperature, humidity, air pressure, and altitude that exceed the range are initially filtered out. Latitude range: [-90°, +90°]; Longitude: [-180°, +180°]; Temperature range: [-90℃, +50℃]; Humidity range: [0, 110%]; Air pressure range: [0hPa~1060hPa]; Altitude range: [0, 60km].

[0042] It should be understood that the above detection range and address location range only show some parameter value ranges. In fact, there are other parameters used to limit the validity of the radiosonde data, which are not shown here. The parameters shown will not limit this application.

[0043] Step 206: Based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode, establish a multi-dimensional radiosonde second data quality assessment model.

[0044] Optionally, step 206, when establishing a multi-dimensional radiosonde second data quality assessment model based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode, can be implemented according to the following steps: The first step is to determine evaluation indicators from the attribute parameters used to characterize the quality of radiosonde second data in multiple dimensions, based on the radiosonde and radiosonde receivers deployed in the current radiosonde networking mode. The evaluation indicators include: radiosonde positioning status, number of radiosonde satellites, radiosonde frequency drift deviation, radiosonde health, received signal strength of radiosonde receiver, and historical reliability.

[0045] The second step is to determine the weights of each evaluation indicator based on the current sounding network mode. The weights of each indicator are dynamically adjusted as the sounding network mode and sounding scenario change.

[0046] The third step is to establish a sounding second data quality assessment model that combines multi-dimensional evaluation indicators with corresponding indicator weights.

[0047] In this application, considering that the attribute parameters of the radiosonde and radiosonde receiver can characterize the quality of radiosonde second data from multiple dimensions, evaluation indicators can be selected and determined from these attribute parameters to assess data quality. Different weights are assigned to these indicators using an entropy weighting method, and the weighted sum is used as the radiosonde second data quality evaluation model. This radiosonde second data quality evaluation model can assess the data quality of radiosonde second data from multiple dimensions, improving the reliability of quality assessment.

[0048] Step 208: Group the multiple radiosonde second data points retained after filtering according to the radiosonde identifier.

[0049] Step 210: Using the multi-dimensional radiosonde second data quality assessment model, calculate and evaluate the data quality of each radiosonde second data in the group, and select the radiosonde second data with the highest quality score to obtain the radiosonde second data of the corresponding group.

[0050] Optionally, each radiosonde second data point also carries the attribute parameter values ​​of the radiosonde and the radiosonde receiver; Using the multi-dimensional radiosonde second data quality assessment model, the data quality of each radiosonde second data in the group is calculated and evaluated. Specifically, this includes: for each radiosonde second data in the group, the following steps are performed: determining the score of each evaluation indicator based on the attribute parameter values ​​of the radiosonde second data; finding the indicator weight corresponding to each evaluation indicator from the multi-dimensional radiosonde second data quality assessment model; and summing the products of all evaluation indicator scores and their corresponding indicator weights to obtain the data quality score of the radiosonde second data.

[0051] Optionally, the scores for each evaluation index are determined based on the attribute parameter values ​​of the radiosonde data. Specifically, this may include: assigning 0 or 1 point based on whether the radiosonde is in a positioning state; calculating the radiosonde satellite number evaluation index score using a normalization formula based on the number of radiosonde satellites; calculating the radiosonde frequency drift deviation evaluation index score by subtracting the deviation value from 1 based on the radiosonde frequency drift deviation; assigning 0 or 1 point based on whether the radiosonde battery voltage is within the normal range; determining the radiosonde receiver's received signal strength evaluation index score by comparing the received signal strength of the radiosonde receiver with the maximum received signal strength; and subtracting the ratio of the daily warning count of the radiosonde receiver to the daily maximum warning count threshold from 1 to obtain the radiosonde receiver's historical reliability evaluation index score.

[0052] For example, the raw data from a radiosonde shows: radiosonde positioning status: not located, number of satellites: 11, radiosonde set frequency: 400.8275MHz, radiosonde current real-time frequency: 400.8775MHz, radiosonde box internal temperature: 25°C, battery voltage: 3.5V, received signal strength: -90dBm. The process for calculating the overall score is as follows: Location status score: 0 points for no location, 1 point for location. Satellite count score: Satellite count range 0-24, normalized first. More satellites are better, so a simple Min-Max normalization formula is used: Satellite count score = 11 / 24 = 0.46. Radiosonde frequency deviation score calculation: Difference |400.8775-400.8776| = 0.0001MHz. The smaller the deviation, the higher the score. A deviation of 0MHz scores 1, and a deviation exceeding 0.025MHz scores 0. Frequency drift deviation score = 1 - 0.04 = 0.96. Radiosonde health score: Battery voltage range: 0~10.0V. Normal range is 2.0-5.5V, score 1. Exceeding this range is abnormal, score 0. Received signal strength range: 0~-128dBm, the stronger the signal, the better, score = 90 / 128 = 0.70. Historical reliability: If the receiver equipment malfunctions, it will alarm once per minute, ranging from 0-1440. Assuming there were 10 abnormal data alarms in the past 24 hours, score = 1 - 10 / 1440 = 0.99. Overall score Sum = (Score of radiosonde positioning satellites * 0.2) + (Score of radiosonde frequency drift deviation * 0.3) + (Score of radiosonde health * 0.1) + (Score of received signal strength * 0.2) + (Score of historical reliability * 0.2) = (1 + 0.46) * 0.1 + 0.96 * 0.3 + 1 * 0.2 + 0.7 * 0.2 + 0.99 * 0.1 = 0.87 (out of 1 point).

[0053] Furthermore, when selecting the radiosonde data with the highest quality score from the radiosonde data in this group, specifically, the timestamps of the radiosonde data appearing in this group can be counted, and the hash value of the timestamp can be calculated using a hash algorithm; a bit array can be created, and the address can be marked using the hash value, and this address can be set to store the quality score; wherein, the index of the address in the bit array is the same as the index of the hash value marking the address, and the length of each address is 4 bits; the quality scores of the radiosonde data in this group are stored sequentially into the address, and each time they are stored, they are compared with the already stored quality scores, only the quality scores with higher scores are retained, and the quality scores with lower scores are removed, and this process is repeated until the quality scores of all radiosonde data are compared, and the radiosonde data corresponding to the final retained quality score is taken as the radiosonde data with the highest quality score under the timestamp corresponding to that address.

[0054] It should be noted that in this application's scheme, before data fusion processing, the volume of radiosonde data per second can be estimated based on information such as the number of radiosondes and receivers in the radiosonde network, and then a bit array can be created appropriately. Alternatively, the number of bits can be created in real time every second; this application does not limit this approach. When radiosonde data is received each second, the hash value of the timestamp can be calculated using a hash algorithm based on the timestamp of the radiosonde data, and the address of the corresponding bit can be marked using the hash value. This address is used to store the quality score of the radiosonde data corresponding to that timestamp.

[0055] In addition, a hash value can be generated by using the radiosonde identifier and timestamp as unique keys for the number of bits, and the number of bits can be marked in the same way as above. The address is also used to store the quality score of the radiosonde second data corresponding to the same hash value.

[0056] For example, if there are three sounding second data points in a group, the quality score of one of the sounding second data points is stored at its current address. Then, the quality score of the second sounding second data point is compared with the already stored first one. If the quality score of the first sounding second data point is higher than that of the second, the currently stored quality score is retained, and the comparison continues with the quality score of the third sounding second data point. If the retained quality score of the first sounding second data point is higher than that of the second, the first sounding second data point is retained; otherwise, the quality score of the second sounding second data point is used instead. In other words, after each comparison, the quality score with the higher value is retained, thus ensuring that the data quality of the sounding second data points at each timestamp is optimal.

[0057] It should be understood that each timestamp can determine the radiosonde second data with the highest quality score in each group in the above manner, and thus, each timestamp can obtain an optimal radiosonde second data received by a different receiver.

[0058] Step 212: Collect and organize the radiosonde second data of each radiosonde in the current radiosonde networking mode, and splice and fuse the radiosonde second data according to the time series to obtain the complete radiosonde data of each radiosonde; wherein, there are no radiosonde second data with overlapping time in the complete radiosonde data of each radiosonde.

[0059] Furthermore, after compiling and organizing the complete radiosonde data of each radiosonde under the current radiosonde networking mode, all the compiled and organized complete radiosonde data can be classified and stored according to the radiosonde identifier; and / or, all the compiled and organized complete radiosonde data can be classified and stored according to the radiosonde receiver identifier.

[0060] Reference Figure 3The diagram illustrates the fusion and stitching of radiosonde second data provided in this application embodiment. Each box represents a radiosonde second data point, which is received from radiosonde A by receivers 1-4 respectively. Taking 10 seconds as an example, each receiver receives the radiosonde second data from radiosonde A at the same time. Taking each second of radiosonde second data as an example, a quality score is obtained by evaluating the data quality of each radiosonde second data point in that second according to the data quality assessment described in this application. Then, the radiosonde second data point with the highest quality score in that second is selected as the radiosonde second data point for that second, and they are stitched and fused sequentially according to the time sequence to obtain a fused radiosonde data point from radiosonde A. In this fused radiosonde data point, each radiosonde second data point has the highest quality score among all radiosonde second data points in that second. Therefore, the solution of this application can not only eliminate duplicate radiosonde second data points, but also ensure that the fused radiosonde data has high data quality and reliability, thereby providing more efficient, accurate, and reliable high-quality radiosonde data for data center equipment, and thus providing better data support for subsequent meteorological application services.

[0061] Similarly, by applying the same processing method to the radiosonde data of other radiosondes with similar conditions, we can obtain complete, high-quality radiosonde data from all radiosondes.

[0062] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0063] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0064] Figure 4 This invention provides a structural block diagram of a multi-source sounding data fusion processing apparatus according to an embodiment of the present application. Figure 4As shown. The multi-source radiosonde data fusion processing device 400 of this embodiment may include a receiving module 401, a filtering module 402, an establishment module 403, a grouping module 404, a fusion module 405, and a statistics module 406. The receiving module 401 is used to receive in real time a plurality of radiosonde second data points received by a plurality of radiosonde receivers; each radiosonde second data point carries at least a radiosonde identifier and a radiosonde receiver identifier. The filtering module 402 is used to filter radiosonde second data points that are outside the detection range and geographical location range of the radiosonde-related equipment. The establishment module 403 is used to establish a multi-dimensional radiosonde second data quality assessment model based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode. The grouping module 404 is used to group the multiple radiosonde second data points retained after filtering according to the radiosonde identifier. The fusion module 405 is used to, for groups containing at least two radiosonde second data points: use the multi-dimensional radiosonde second data quality assessment model to calculate and evaluate the data quality of each radiosonde second data point in each radiosonde data point within the group, and select the radiosonde second data point with the highest quality score to obtain the radiosonde second data point corresponding to the group. The statistics module 406 is used to statistically organize the radiosonde second data point of each radiosonde in the current radiosonde networking mode, and splice and fuse the radiosonde second data point according to the time series to obtain the complete radiosonde data point of each radiosonde; wherein, the complete radiosonde data point of each radiosonde does not contain radiosonde second data point data with overlapping times.

[0065] It should be noted that some or all of the multi-source sounding data fusion processing device in this embodiment can be an application located on a local terminal, or it can be a plugin or software development kit (SDK) or other functional unit set in the application located on the local terminal, or it can be a processing engine located on the network side server, or it can be a distributed system located on the network side. This embodiment does not make any special limitations on this.

[0066] It is understood that the application may be a native program installed on the local terminal, or it may be a web application of a browser on the local terminal. This embodiment does not limit this.

[0067] Optionally, in one possible implementation of this embodiment, when the establishment module 403 establishes a multi-dimensional radiosonde second data quality assessment model based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode, it is specifically used to determine assessment indicators from the attribute parameter information used to characterize the radiosonde second data quality in a multi-dimensional way based on the radiosonde and radiosonde receiver deployed under the current radiosonde networking mode; wherein, the assessment indicators include: radiosonde positioning status, number of radiosonde satellites, radiosonde frequency drift deviation, radiosonde health, received signal strength of radiosonde receiver, and historical reliability; according to the current radiosonde networking mode, the entropy weight method is used to determine the indicator weights for each assessment indicator, wherein the indicator weights are dynamically adjusted as the radiosonde networking mode and radiosonde scenario change; and a radiosonde second data quality assessment model is established by combining the multi-dimensional assessment indicators with the corresponding indicator weights.

[0068] Optionally, in one possible implementation of this embodiment, each radiosonde second data point also carries the attribute parameter values ​​of the radiosonde and the radiosonde receiver; when the fusion module 405 uses the multi-dimensional radiosonde second data quality assessment model to calculate and evaluate the data quality of each radiosonde second data point in the group, it specifically performs the following for each radiosonde second data point in the group: determining the score of each evaluation index based on the attribute parameter values ​​of the radiosonde second data point; finding the index weight corresponding to each evaluation index from the multi-dimensional radiosonde second data quality assessment model; and summing the products of all evaluation index scores and corresponding index weights to obtain the data quality score of the radiosonde second data point.

[0069] Optionally, in one possible implementation of this embodiment, when the fusion module 405 determines the scores of each evaluation index based on the attribute parameter values ​​of the radiosonde second data, it specifically performs the following: determining whether to assign 0 or 1 points based on the radiosonde's positioning status; calculating the radiosonde satellite quantity evaluation index score using a normalization formula based on the number of radiosonde satellites; calculating the radiosonde frequency drift deviation evaluation index score by subtracting the deviation value from 1 based on the radiosonde frequency drift deviation; determining whether to assign 0 or 1 points based on whether the radiosonde's battery voltage is within the normal range; determining the radiosonde receiver's received signal strength evaluation index score based on the ratio of the received signal strength of the radiosonde receiver to the maximum received signal strength; and subtracting the ratio from the daily warning count of the radiosonde receiver to the daily maximum warning count threshold as the radiosonde receiver's historical reliability evaluation index score.

[0070] Optionally, in one possible implementation of this embodiment, when the fusion module 405 selects the radiosonde data with the highest quality score from the radiosonde data, it specifically performs the following steps: counts the timestamps of radiosonde data appearing in the group, calculates the hash value of the timestamp using a hash algorithm; creates a bit array and marks the address using the hash value, setting the address to store the quality score; wherein, the index of the address in the bit array is the same as the index of the hash value marking the address, and the length of each address is 4 bits; sequentially stores the quality scores of the radiosonde data in the group into the address, compares them with the already stored quality scores each time they are stored, retains only the quality scores with higher scores and removes the quality scores with lower scores, and repeats this process until the quality scores of all radiosonde data are compared, and takes the radiosonde data corresponding to the finally retained quality score as the radiosonde data with the highest quality score under the timestamp corresponding to the address.

[0071] Optionally, in one possible implementation of this embodiment, the device further includes: a storage module, used to classify and store all the complete radiosonde data obtained through statistical processing according to the radiosonde identifier; and / or, to classify and store all the complete radiosonde data obtained through statistical processing according to the radiosonde receiver identifier.

[0072] In this embodiment, several real-time received radiosonde data points can be filtered based on the detection range and geographical location of the radiosonde-related equipment. Then, a multi-dimensional radiosonde data quality assessment model is used to calculate the data quality score for each radiosonde data point. The radiosonde data points with the highest quality score from the same radiosonde are then spliced ​​and fused into a complete radiosonde data point for that radiosonde. Finally, the complete radiosonde data for each radiosonde is obtained through statistical analysis. This application can initially filter radiosonde data based on detection range and location range, eliminating radiosonde data that does not meet objective environmental and transmission requirements. Then, a multi-dimensional radiosonde data quality assessment model is used to assess the quality of each radiosonde data point, selecting the radiosonde data points with the highest quality scores from different radiosonde receivers from multiple radiosonde data points from the same radiosonde, and then splicing and fusing them. This ensures high data quality for each radiosonde data point, thus guaranteeing the data quality, integrity, and reliability of the spliced ​​and fused radiosonde data. Furthermore, this data fusion method is more efficient and reliable.

[0073] One embodiment of this application provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the method for fusion processing of multi-source sounding data as described above.

[0074] One embodiment of this application provides an electronic device, which includes a processor and a memory. The memory stores at least one instruction, which is loaded and executed by the processor to implement the method for fusion processing of multi-source sounding data as described above.

[0075] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0076] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0077] like Figure 5 As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0078] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0079] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the method for fusion processing of multi-source sounding data. For example, in some embodiments, the method for fusion processing of multi-source sounding data can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the method for fusion processing of multi-source sounding data described above can be performed. Alternatively, in other embodiments, computing unit 501 may be configured by any other suitable means (e.g., by means of firmware) to perform a method for fusion processing of multi-source sounding data.

[0080] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, at least one input device, and at least one output device.

[0081] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0082] In the context of this application, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0083] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0084] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0085] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0086] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this application can be achieved, and this is not limited herein.

[0087] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for fusion processing of multi-source radiosonde data, characterized in that, include: It receives in real time a number of sounding seconds of data from several sounding receivers; Each radiosonde second data point carries at least the radiosonde identifier and the radiosonde receiver identifier; Based on the detection range of the radiosonde-related equipment and its geographical location, radiosonde second data that are outside the range are filtered out. Based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode, a multi-dimensional radiosonde second data quality assessment model is established. The multiple radiosonde second data points retained after filtering are grouped according to the radiosonde identifier; For a group containing at least two radionuclide data points: using the multi-dimensional radionuclide data quality assessment model, calculate and evaluate the data quality of each radionuclide data point in the group, and select the radionuclide data point with the highest quality score to obtain the radionuclide data point of the radiosonde corresponding to the group; The radiosonde second data of each radiosonde under the current radiosonde networking mode are statistically analyzed and merged according to the time series to obtain the complete radiosonde data of each radiosonde; among them, there are no radiosonde second data with overlapping time in the complete radiosonde data of each radiosonde.

2. The method as described in claim 1, characterized in that, Based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode, a multi-dimensional radiosonde second data quality assessment model is established, specifically including: Based on the radiosondes and receivers deployed in the current radiosonde networking mode, evaluation indicators are determined from attribute parameters used to characterize the quality of radiosonde second data in multiple dimensions. The evaluation indicators include: radiosonde positioning status, number of radiosonde satellites, radiosonde frequency drift deviation, radiosonde health, received signal strength of radiosonde receiver, and historical reliability. Based on the current sounding network mode, the entropy weight method is used to determine the weight of each evaluation indicator. The weight of each indicator is dynamically adjusted as the sounding network mode and sounding scenario change. Establish a sounding second data quality assessment model that combines multi-dimensional evaluation indicators with corresponding indicator weights.

3. The method as described in claim 2, characterized in that, Each radiosonde second also carries the attribute parameter values ​​of the radiosonde and the radiosonde receiver; Using the aforementioned multi-dimensional radiosonde second data quality assessment model, the data quality of each radiosonde second data point in this group is calculated and evaluated, specifically including: For each sounding second data point in the aforementioned group, perform the following: The scores of each evaluation index are determined based on the attribute parameter values ​​of the radiosonde second data. Find the weight of each evaluation indicator from the multi-dimensional sounding second data quality assessment model. The data quality score of the radiosonde data is obtained by summing the products of the scores of all evaluation indicators and their corresponding weights.

4. The method as described in claim 3, characterized in that, The scores for each evaluation index are determined based on the attribute parameter values ​​of this radiosonde data, specifically including: The score is determined by whether the radiosonde is in a fixed position or not; Based on the number of radiosonde satellites, a normalization formula is used to calculate the score of the evaluation index for the number of radiosonde satellites. Based on the frequency drift deviation of the radiosonde, the score of the frequency drift deviation evaluation index is calculated by subtracting the deviation value from 1. The score is determined by whether the radiosonde's battery voltage is within the normal range, and either 0 or 1 point is assigned. The score of the received signal strength evaluation index of the radiosonde receiver is determined based on the ratio of the received signal strength of the radiosonde receiver to the maximum received signal strength. The ratio of the daily number of warnings from the radiosonde receiver to the daily maximum warning threshold is used as the historical reliability assessment score for the radiosonde receiver.

5. The method according to any one of claims 1-4, characterized in that, The radiosonde data with the highest quality score was selected from the radiosonde data, specifically including: The timestamps of radiosonde data appearing in this group are counted, and the hash value of the timestamp is calculated using a hash algorithm; Create a bit and use a hash value to label the address of that bit, setting that address to store the quality score; where the index of the address in the bit array is the same as the index of the hash value labeling that address, and each address is 4 bits long; The quality scores of the radiosonde second data in the group are sequentially stored into the address. Each time a data is stored, it is compared with the already stored quality scores. Only the quality scores with higher scores are retained, and the quality scores with lower scores are removed. This process is repeated until the quality scores of all radiosonde second data are compared. The radiosonde second data corresponding to the final retained quality score is taken as the radiosonde second data with the highest quality score under the timestamp corresponding to that address.

6. The method as described in claim 1, characterized in that, The method further includes: All complete radiosonde data obtained through statistical analysis will be categorized and stored according to the radiosonde identification; and / or, All complete radiosonde data obtained through statistical analysis will be classified and stored according to the radiosonde receiver identifier.

7. A fusion processing device for multi-source radiosonde data, characterized in that, include: The receiving module is used to receive in real time a number of radiosonde seconds of data from several radiosonde receivers; Each radiosonde second data point carries at least the radiosonde identifier and the radiosonde receiver identifier; The filtering module is used to filter radiosonde second data that are outside the detection range and geographical location range of the radiosonde-related equipment. A module is established to build a multi-dimensional radiosonde second data quality assessment model based on the attribute parameter information of the radiosonde and radiosonde receiver under the current radiosonde networking mode. The grouping module is used to group multiple radiosonde second data points that have been filtered and retained according to the radiosonde identifier; The fusion module is used to calculate and evaluate the data quality of each radionuclide data in a group containing at least two radionuclide data points using the multi-dimensional radionuclide data quality assessment model, and select the radionuclide data with the highest quality score to obtain the radionuclide data of the radiosonde corresponding to the group. The statistics module is used to collect and organize the radiosonde second data of each radiosonde in the current radiosonde networking mode, and to splice and fuse the radiosonde second data according to the time series to obtain the complete radiosonde data of each radiosonde; wherein, there are no radiosonde second data with overlapping time in the complete radiosonde data of each radiosonde.

8. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, in, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-6.

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