Rain and snow phase identification product quality inspection and evaluation method, device and equipment and storage medium

Abstract

The embodiment of the invention provides a rain and snow phase state identification product quality inspection and evaluation method, device and equipment and a storage medium, and is applied to the technical field of meteorological monitoring. The method comprises the steps of obtaining a rain and snow phase state identification product; obtaining winter rainfall phase state live data; performing time matching and space matching on the rain and snow phase state identification product and the winter rainfall phase state live data; and performing quality evaluation on the rain and snow phase state identification product according to a space-time matching result. In this way, simple and efficient quality evaluation of the rain and snow phase state identification product can be realized.

Description

Technical Field

[0001] The present disclosure relates to the field of meteorological monitoring technology, and in particular to a method, device, equipment and storage medium for quality inspection and evaluation of rain and snow phase recognition products. Background Art

[0002] The importance of rain and snow phase identification is reflected in the following aspects:

[0003] (1) Improve the accuracy of weather forecasts: Rain and snow phase identification is crucial to the accuracy of precipitation forecasts. Accurate identification of rain and snow phases can help meteorologists better predict precipitation types, thereby providing the public with more accurate weather information.

[0004] (2) Disaster prevention and reduction: Rain and snow phase identification is of great significance for disaster prevention and reduction. For example, accurate prediction of sleet can reduce the risk of traffic accidents and infrastructure damage.

[0005] (3) Improving precipitation forecast products: The rain and snow phase judgment criterion based on the proportion of ice-phase hydrometeors can significantly improve the problem of overestimating the range of sleet in a certain eastern region and underreporting scattered sleet based on the thickness judgment criterion based on temperature and height fields.

[0006] (4) Improving forecast scores: Combining new criteria can significantly improve the threat score of sleet forecasts.

[0007] (5) Indication of phase transition process: Rain and snow phase identification has a good indication effect on the phase transition process. For example, the error of the phase transition start time of the representative station is 1 to 2 hours. The phase transition and sleet duration judgment of the representative station in the eastern region is better than the thickness judgment.

[0008] (6) Technical support: It can provide important technical support for the objective identification and forecasting of precipitation phases.

[0009] (7) Improve the accuracy of precipitation phase identification.

[0010] (8) Understanding of precipitation phase distribution: Rain and snow phase identification helps to understand the relationship between precipitation phase distribution and frontal position, as well as the impact of different terrains on precipitation phase.

[0011] In summary, rain and snow phase identification is of great significance in many fields such as weather forecasting, disaster prevention, technological development and scientific research.

[0012] The accuracy of existing rain and snow phase identification products is mainly judged through deep learning networks and setting thresholds. The judgment process is time-consuming and consumes a lot of resources. There is a lack of a simple and efficient method for quality assessment of rain and snow phase identification products. Summary of the invention

[0013] The present disclosure provides a method, apparatus, device, and storage medium for quality inspection and evaluation of rain and snow phase identification products.

[0014] According to a first aspect of the present disclosure, there is provided a method for quality inspection and evaluation of rain and snow phase identification products. The method includes:

[0015] Obtain a rain and snow phase identification product;

[0016] Obtain actual situation data of winter precipitation phases;

[0017] Perform time matching and space matching on the rain and snow phase identification product and the actual situation data of winter precipitation phases;

[0018] Evaluate the quality of the rain and snow phase identification product according to the spatio-temporal matching result.

[0019] In the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided.

[0020] The actual situation data of winter precipitation phases is the winter precipitation phase observed by a ground observation station and the corresponding time point;

[0021] Performing time matching and space matching on the rain and snow phase identification product and the actual situation data of winter precipitation phases includes:

[0022] Select a rain and snow phase identification product that is consistent with the time point as an initial rain and snow phase identification product; with the ground observation station as the center, match the initial rain and snow phase identification product that is closest to the position of the ground observation station within the range of N×N above the ground observation station as the target initial rain and snow phase identification product; where N is a positive integer; and use the target initial rain and snow phase identification product and the winter precipitation phase as a matching data pair.

[0023] In the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided.

[0024] Evaluating the quality of the rain and snow phase identification product according to the spatio-temporal matching result includes:

[0025] Compare the target initial rain and snow phase identification product and the winter precipitation phase that are paired;

[0026] Determine the hit rate, miss rate, false alarm rate, false positive rate, and critical success index of the rain and snow phase identification product according to the comparison result.

[0027] In the above-mentioned aspect and any possible implementation manner, a further implementation manner is provided.

[0028] Determining the hit rate, miss rate, false alarm rate, misclassification rate, and critical success index of the rain and snow phase identification product based on the comparison result includes:

[0029] Counting the number of hits, misses, false alarms, and misclassifications of the rain and snow phase identification product respectively according to the comparison result;

[0030] Calculating the hit rate, miss rate, misclassification rate, false alarm rate, and critical success index respectively according to the counted numbers.

[0031] In the above aspect and any possible implementation manner, a further implementation manner is provided.

[0032] The formulas for calculating the hit rate, miss rate, misclassification rate, and false alarm rate respectively according to the counted numbers are as follows:

[0033] Hit rate: POD = H / (H + M),

[0034] Miss rate: MAR = M / (H + M),

[0035] False alarm rate: FAR = FA / (H + FA),

[0036] Misclassification rate: WRR = WR / (H + WR),

[0037] Critical success index:

[0038] Wherein: H, M, FA, and WR respectively represent the number of hits, misses, false alarms, and misclassifications.

[0039] In the above aspect and any possible implementation manner, a further implementation manner is provided.

[0040] Obtaining the rain and snow phase identification product and obtaining the actual situation data of winter precipitation phase include:

[0041] Determining the rain and snow identification area;

[0042] Obtaining the rain and snow phase identification product and the actual situation data of winter precipitation phase within the corresponding area according to the determined rain and snow identification area.

[0043] In the above aspect and any possible implementation manner, a further implementation manner is provided.

[0044] The rain and snow phase and / or precipitation phase refer to three states: rainfall, snowfall, and sleet.

[0045] According to the second aspect of the present disclosure, a device for inspecting and evaluating the quality of a rain and snow phase identification product is provided. The device includes:

[0046] A data acquisition module, configured to acquire the rain and snow phase identification product;

[0047] The data acquisition module is further configured to acquire the actual situation data of winter precipitation phase states.

[0048] The matching module is configured to perform time matching and spatial matching on the rain and snow phase state recognition products and the actual situation data of winter precipitation phase states.

[0049] The evaluation module is configured to perform quality evaluation on the rain and snow phase state recognition products according to the spatio-temporal matching results.

[0050] According to a third aspect of the present disclosure, an electronic device is provided. The electronic device includes: a memory and a processor. A computer program is stored on the memory, and when the processor executes the program, the method as described above is implemented.

[0051] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the method as described in the first aspect of the present disclosure is implemented.

[0052] The method, device, equipment, and storage medium for quality inspection and evaluation of rain and snow phase state recognition products provided by the embodiments of the present disclosure obtain rain and snow phase state recognition products and actual situation data of winter precipitation phase states, perform time matching and spatial matching on them, and then perform quality evaluation on the rain and snow phase state recognition products according to the spatio-temporal matching results. It is a set of general quality evaluation methods applicable to all rain and snow phase state recognition products. While the evaluation means is simple and efficient, it also ensures the accuracy of the evaluation.

[0053] It should be understood that the content described in the summary of the invention section is not intended to limit the key or important features of the embodiments of the present disclosure, nor is it used to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In combination with the drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present disclosure will become more apparent. The drawings are used to better understand the solution and do not constitute a limitation to the present disclosure. In the drawings, the same or similar reference numerals represent the same or similar elements, where:

[0055] Figure 1 shows a flowchart of a method for quality inspection and evaluation of rain and snow phase state recognition products according to an embodiment of the present disclosure;

[0056] Figure 2 shows a block diagram of a device for quality inspection and evaluation of rain and snow phase state recognition products according to an embodiment of the present disclosure;

[0057] Figure 3 shows a schematic block diagram of an exemplary electronic device capable of implementing the embodiments of the present disclosure. Detailed implementation manners

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure. Apparently, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts shall fall within the protection scope of the present disclosure.

[0059] In addition, the term "and / or" in this article is merely an association relationship describing associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0060] Figure 1 The flowchart of a rain and snow phase state recognition product quality inspection and evaluation method 100 according to an embodiment of the present disclosure is shown. The method 100 includes:

[0061] Step 110, obtaining a rain and snow phase state recognition product.

[0062] In some embodiments, the rain and snow phase state recognition product is: a rain and snow phase state recognition product given by a dual-polarization radar winter precipitation phase state (rain, snow, sleet) recognition algorithm, etc.

[0063] Step 120, obtaining actual situation data of winter precipitation phase state.

[0064] In some embodiments, the actual situation data of the winter precipitation phase state is the winter precipitation phase state observed by a ground observation station and the corresponding time points. For example, the actual situation data is selected as the hourly weather phenomenon data observed by a precipitation phenomenon instrument. Since the data observed by the precipitation phenomenon instrument is hourly integral point data, in order to maintain the consistency of the recognition data and the actual situation data in time, the integral point rain and snow phase state recognition product is selected.

[0065] In some embodiments, before evaluation, that is, before obtaining the data, the rain and snow recognition area range and the data of the ground precipitation phenomenon instrument within the recognition area are delimited first.

[0066] Step 130, performing time matching and space matching on the rain and snow phase state recognition product and the actual situation data of the winter precipitation phase state.

[0067] In some embodiments, time matching and spatial matching are performed on the rain and snow phase identification products and the actual winter precipitation phase data, including: selecting the rain and snow phase identification product that is consistent with the time point as the initial rain and snow phase identification product; centering on the ground observation station, matching the initial rain and snow phase identification product within the range of N×N (default value 5km×5km) above the ground observation station and closest to the position of the ground observation station as the target initial rain and snow phase identification product; where N is a positive integer; pairing the target initial rain and snow phase identification product with the winter precipitation phase as a matching data pair. Specifically, centering on the ground observation station, matching the weather phenomenon values identified by the rain and snow identification products within the N×N window area above to form a matching data pair. If there are two or more phenomenon values within the N×N window area, use the most frequent phenomenon value as the matching data pair.

[0068] Step 140, perform quality assessment on the rain and snow phase identification product according to the spatio-temporal matching result.

[0069] In some embodiments, performing quality assessment on the rain and snow phase identification product according to the spatio-temporal matching result includes: comparing the paired target initial rain and snow phase identification product with the winter precipitation phase; determining the hit rate, miss rate, false alarm rate, misclassification rate, critical success index, etc. of the rain and snow phase identification product according to the comparison result. Specifically, the rules for hits, misclassifications, misses, and false alarms of the rain and snow phase identification product are shown in Table 1. Among them, if the identified result is consistent with the actual situation, it is rated as a hit; if not, it is rated as a misclassification; if it exists in the actual situation but is not identified, it is rated as a miss; if it does not exist in the actual situation but is identified, it is rated as a false alarm.

[0070] Table 1: Comparison table of rain and snow phase identification products and actual situation data

[0071]

[0072] Among them, the calculation formulas for the hit rate, miss rate, false alarm rate, and critical success index are as follows:

[0073] Hit rate:

[0074] Miss rate:

[0075] False alarm rate:

[0076] Misclassification rate:

[0077] Critical success index:

[0078] Where: H, M, FA, WR represent the number of hits, misses, false alarms, and misclassifications respectively.

[0079] Therefore, in combination with the corresponding actual situation data, the hit rate, false negative rate, false alarm rate, and misclassification rate can be accurately calculated. In addition, the problem of difficult to accurately evaluate the recognition lead due to the lag in data collection at the time of actual situation occurrence is also solved. It has wide applicability and is simple and efficient.

[0080] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present disclosure is not limited by the described action sequence, because according to the present disclosure, certain steps can be carried out in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present disclosure.

[0081] The above is the introduction of the method embodiments. The following further illustrates the solution of the present disclosure through device embodiments.

[0082] Figure 2 The block diagram of a rain and snow phase state recognition product quality inspection and evaluation device 200 according to an embodiment of the present disclosure is shown. As Figure 2 shown, the device 200 includes:

[0083] A data acquisition module 210, configured to acquire rain and snow phase state recognition products;

[0084] The data acquisition module 210 is further configured to acquire actual situation data of winter precipitation phase states;

[0085] A matching module 220, configured to perform time matching and space matching on the rain and snow phase state recognition products and the actual situation data of winter precipitation phase states;

[0086] An evaluation module 230, configured to perform quality evaluation on the rain and snow phase state recognition products according to the spatio-temporal matching results.

[0087] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the described modules can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0088] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.

[0089] Figure 3FIG. 0 shows a schematic block diagram of an electronic device 300 that can be used to implement embodiments of the present disclosure. 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 can also represent various forms of mobile devices, such as, personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0090] The electronic device 300 includes a computing unit 301 that can perform various appropriate actions and processes according to a computer program stored in the ROM 302 or a computer program loaded from the storage unit 308 into the RAM 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 can also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. The I / O interface 305 is also connected to the bus 304.

[0091] A plurality of components in the electronic device 300 are connected to the I / O interface 305, including: an input unit 306, such as a keyboard, a mouse, etc.; an output unit 307, such as various types of displays, speakers, etc.; a storage unit 308, such as a magnetic disk, an optical disk, etc.; and a communication unit 309, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0092] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated 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 301 executes the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 300 via the ROM 302 and / or the communication unit 309. When the computer program is loaded into the RAM 303 and executed by the computing unit 301, one or more steps of the method 100 described above can be executed. Alternatively, in other embodiments, the computing unit 301 can be configured to execute method 100 in any other suitable manner (e.g., by means of firmware).

[0093] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-a-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0094] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0095] In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, 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 a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0096] In order to provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0097] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0098] A computer system can include a client and a server. The client and the server are generally remote from each other and typically interact through a communication network. The client-server relationship is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, or a server of a distributed system, or a server incorporating a blockchain.

[0099] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0100] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. 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 disclosure shall be included within the protection scope of this disclosure.

Claims

1. A method for inspecting and evaluating the quality of a rain and snow phase identification product, characterized in that Comprising: Obtaining a rain and snow phase identification product; Obtaining actual winter precipitation phase data; Performing time matching and spatial matching on the rain and snow phase identification product and the actual winter precipitation phase data; Evaluating the quality of the rain and snow phase identification product according to the spatio-temporal matching result.

2. The method according to claim 1, wherein The actual winter precipitation phase data is the winter precipitation phase observed at a ground observation station and the corresponding time points; Performing time matching and spatial matching on the rain and snow phase identification product and the actual winter precipitation phase data includes: Selecting a rain and snow phase identification product that is consistent with the time point as an initial rain and snow phase identification product; centering on the ground observation station, matching the initial rain and snow phase identification product that is closest to the position of the ground observation station within the range of N×N above the ground observation station as a target initial rain and snow phase identification product; where N is a positive integer; and taking the target initial rain and snow phase identification product and the winter precipitation phase as a matching data pair.

3. The method according to claim 2, wherein Evaluating the quality of the rain and snow phase identification product according to the spatio-temporal matching result includes: Comparing the target initial rain and snow phase identification product paired with the winter precipitation phase; Determining the hit rate, miss rate, false alarm rate, misclassification rate, and critical success index of the rain and snow phase identification product according to the comparison result.

4. The method according to claim 3, wherein Determining the hit rate, miss rate, false alarm rate, misclassification rate, and critical success index of the rain and snow phase identification product according to the comparison result includes: Statistically counting the number of hits, misses, false alarms, and misclassifications of the rain and snow phase identification product according to the comparison result; Calculating the hit rate, miss rate, false alarm rate, misclassification rate, and critical success index respectively according to the statistically counted number of times.

5. The method according to claim 4, wherein The formulas for calculating the hit rate, miss rate, false alarm rate, and misclassification rate respectively according to the statistically counted number of times are as follows: Hit rate: Omission rate: False alarm rate: Misstatement rate: Critical Success Index: Where: H, M, FA, and WR respectively represent the number of hits, misses, false alarms, and misclassifications.

6. The method according to claim 1, wherein Obtaining the rain and snow phase identification product and obtaining the actual winter precipitation phase data includes: Determining a rain and snow identification area; Obtaining the rain and snow phase identification product and the actual winter precipitation phase data within the corresponding area according to the determined rain and snow identification area.

7. The method according to any one of claims 1-6, wherein The rain and snow phase and / or precipitation phase refer to three states: rainfall, snowfall, and sleet.

8. An inspection and evaluation device for the quality of a rain and snow phase identification product, characterized in that Comprising: A data acquisition module for obtaining a rain and snow phase identification product; The data acquisition module is further configured to obtain actual winter precipitation phase data; A matching module for performing time matching and spatial matching on the rain and snow phase identification product and the actual winter precipitation phase data; An evaluation module for evaluating the quality of the rain and snow phase identification product according to the spatio-temporal matching result.

9. An electronic device, characterized in that, Comprising: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are for causing the computer to execute the method according to any one of claims 1-7.

Images