Remote Sensing Common Product Production Scheduling Method, Device and Medium

Abstract

The present application provides a production scheduling method for remote sensing common products. The method includes: constructing a set of product models corresponding to the products; differentiating the levels of each product model in the set of product models; and gradually dispatching the product models at the same level to production nodes. Based on the above technical solution, before dispatching tasks to computer nodes, the method provided by the present application can classify and grade the product models corresponding to the product names, and then produce the product models at the same level according to the grades. Compared with the prior art, the production process is analyzed and simplified, and the efficiency of remote sensing product production scheduling is greatly improved.

Description

Technical Field

[0001] The present disclosure specifically discloses a production scheduling method, device and medium for remote sensing common products. Background Art

[0002] Existing remote sensing product production scheduling methods support the "first come, first served" scheduling strategy. When a new task arrives at the scheduling server, the scheduling server distributes the task to the first idle computing node according to the "first come, first served" processing strategy. The specific implementation steps are as follows:

[0003] The scheduling server receives a remote sensing product production order and checks whether the idle computing node queue is empty. If it is empty, the scheduling server defends the order in the order buffer area. Otherwise, the task is assigned to the first node in the idle computing node queue.

[0004] The computing node executes the production task. At the same time, the scheduling server removes this node from the idle queue and adds it to the working computing node queue. Until the computing node completes the production of the task and rejoins the idle queue.

[0005] The scheduling server checks whether there is an order in the order buffer area to be executed. If there is, the order is assigned to an idle node; if not, the process ends.

[0006] Based on the above information, the existing remote sensing product production scheduling method has the following defects: on the one hand, it is easy to cause resource waste: when a remote sensing product production task has high resource requirements for a computing node, the computing node cannot meet them. On the contrary, assigning a task with low resource requirements to a node with high production capacity is a waste of the computing node's resources. On the other hand, it takes a long time: the amount of remote sensing data is large, and the production process of a single product is complex and time-consuming. Summary of the Invention

[0007] In view of the above defects or deficiencies in the prior art, the present application aims to provide a production scheduling method, device and medium for remote sensing common products that can analyze and schedule the product production process compared with the prior art, so as to significantly improve the production efficiency of remote sensing products.

[0008] In the first aspect, a production scheduling method for remote sensing common products, the method includes: constructing a set of product models corresponding to the products; distinguishing the levels of each product model in the set of product models; and gradually distributing the product models at the same level to production nodes.

[0009] According to the technical solution provided by the embodiment of the present application, constructing the product model set corresponding to the product includes the following steps: constructing the product dependency tree corresponding to the product; analyzing the product models corresponding to each node in the product dependency tree, where the product model includes: product level, product dependency, and execution status; summarizing all the product models to obtain the product model set.

[0010] According to the technical solution provided by the embodiment of the present application, before constructing the product dependency tree corresponding to the product, it further includes: obtaining the product information, where the product information includes: product name, time period information, and location information.

[0011] According to the technical solution provided by the embodiment of the present application, the step of obtaining the product information includes: splitting the natural sentence to obtain a keyword sequence; querying the remote sensing semantic sequence corresponding to each keyword in the keyword sequence one by one; constructing a standardized keyword group sequence, where each standardized keyword group includes: a keyword and the remote sensing semantics corresponding to the keyword; screening the standardized keyword group sequence to obtain a task keyword group; and normalizing the task keyword group to obtain the product information.

[0012] According to the technical solution provided by the embodiment of the present application, retrieving with the time period information and location information corresponding to the product name as keywords to obtain the data source information corresponding to the product name; the data source information includes: data source name and file path.

[0013] According to the technical solution provided by the embodiment of the present application, deriving the output information corresponding to the product name based on the data source information corresponding to the product name.

[0014] In a second aspect, a computer device includes: a memory for storing executable program code; and one or more processors for reading the executable program code stored in the memory to execute a remote sensing common product production scheduling method as described in the first aspect.

[0015] In a third aspect, a computer-readable storage medium, characterized in that the computer-readable storage medium includes instructions, and when the instructions run on a computer, the computer is caused to execute a remote sensing common product production scheduling method as described in the first aspect.

[0016] In summary, the present application discloses a remote sensing common product production scheduling method.

[0017] Before dispatching tasks to computer nodes, this technical solution obtains a set of product models composed of multiple product models corresponding to the product. Then, it divides each product model in the product model set according to the level of the product model. From low to high, it dispatches the product models at the same level to production nodes. Based on the above technical solution, before dispatching tasks to computer nodes, the method provided by this application can divide and classify the product models corresponding to the product name. Then, according to the level, it produces the product models at the same level. Compared with the prior art, the production process is analyzed and simplified, and the efficiency of remote sensing product production scheduling is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Other features, objects, and advantages of the present application will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0019] Figure 1 A method for remote sensing common product production scheduling as shown;

[0020] Figure 2 A combined schematic diagram of the product dependency tree as shown;

[0021] Figure 3 A schematic diagram of the structure for dispatching tasks by computer nodes as shown;

[0022] Figure 4 A schematic diagram of the hardware structure of the computer device given. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The present application will be further described in detail below with reference to the drawings and embodiments. It can be understood that the specific embodiments described herein are only for explaining the related invention and not for limiting the invention. In addition, it should be noted that for the sake of description, only parts related to the invention are shown in the drawings.

[0024] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and embodiments.

[0025] Please refer to Figure 1 An implementation manner of a method for remote sensing common product production scheduling as shown.

[0026] Figure 1 The method for remote sensing common product production scheduling described in

[0027] S1: Construct a set of product models corresponding to the product;

[0028] S2: Distinguish the levels of each product model in the product model set;

[0029] S3: Distribute product models at the same level to production nodes step by step.

[0030] in,

[0031] Constructing a product model set corresponding to the product. This step divides the remote sensing product to be produced into a plurality of product models to obtain a set of product models.

[0032] Specifically, the product model can be expressed in the form of a sequence, such as: any product model (product level, product dependency, execution status), the product level and the product dependency at this level can be obtained in the following way: construct a product dependency tree corresponding to the product; after obtaining the product name, take the product name as the root node and its dependent products as child nodes. Similarly, if the child node still has dependent products, the dependent products of the child node are the grandchild nodes of the product name, and so on, to establish a product dependency tree for the product name, such as Figure 2 By analyzing the product dependency tree, we can get the description of the product model at each node in the product dependency tree, as shown in Table 1:

[0033] Table 1

[0034]

[0035]

[0036] Analyze the product model corresponding to each node in the product dependency tree. The product model includes: product level, product dependency, and execution status. In Table 1, the product level is determined by the level of the node corresponding to the product model name in the dependency tree. A leaf node represents a level 0 product model with no dependency. Please refer to Figure 2 , where the 0-level algorithms are P1, P4, P7, and P8. The lowest-level nodes except the leaf nodes are considered as the 1-level product model. Please refer to Figure 2 , the algorithm of level 1 is P6; the dependent product model of this level is a leaf node. And so on, Figure 2 The level 2 algorithm in the figure is P5; the level 3 algorithms in Figure 2 are P2 and P3.

[0037] Summarize all product models and get the product model set as follows:

[0038] P(4, p1&p2&p3&p4, 0)

[0039] P1(0, no dependency, 0)

[0040] P2(3, p1&p5, 0)

[0041] P3(3, p5, 0)

[0042] P4(0, no dependency, 0)

[0043] P5(2, p6, 0)

[0044] P6(1, p7&p8, 0)

[0045] P7(0, no dependency, 0)

[0046] P8(0, no dependency, 0)

[0047] For each product model in the set of product models, divide them according to the level of each product model, and group the product models at the same level into one category. Specifically, from the set of product models shown in the above example, it can be known that:

[0048] The first category is:

[0049] P1(0, no dependency, 0)

[0050] P4(0, no dependency, 0)

[0051] P7(0, no dependency, 0)

[0052] P8(0, no dependency, 0)

[0053] The second category is:

[0054] P5(2, p6, 0)

[0055] The third category is:

[0056] P2(3, p1&p5, 0)

[0057] P3(3, p5, 0)

[0058] The fourth category is:

[0059] P(4, p1&p2&p3&p4, 0)

[0060] After the division is completed, multiple categories of product models are obtained. Then, the levels corresponding to the product models in each category are the same. Arrange all categories of product models in ascending order according to their corresponding levels, and then dispatch them to the production nodes level by level for production. That is:

[0061] The first category → The second category → The third category → The fourth category.

[0062] Please refer to Figure 3 , when each production node receives the production task of product models at the same level, it completes the production task by calling the product production algorithm. When it is detected that all product models within the product models at the same level are produced, the scheduling node dispatches the production tasks of the product models within another level of product models to each computing node until all levels of product models are completely produced.

[0063] Specifically, after the execution of the first category is completed, the execution status in each product model in the first category changes to 1, indicating successful execution; then, the second category, the third category, and the fourth category are executed in sequence.

[0064] Based on the above technical solution, before dispatching tasks to computer nodes, the method provided by the present application can classify and grade the product models corresponding to product names, and then, according to the levels, produce the product models at the same level. Compared with the prior art, the production process is analyzed and simplified, and the efficiency of remote sensing product production scheduling is greatly improved.

[0065] In another embodiment, before constructing the product dependency tree corresponding to the product, it further includes:

[0066] Obtain the product information, where the product information includes: product name, time period information, and location information. Specifically, the steps of obtaining the product information include:

[0067] Split the natural sentence to obtain a keyword sequence; for example, if the user needs to view the air quality situation in Langfang in 2019, the obtained keyword sequence after analysis is (2019; Langfang; air quality).

[0068] Query the remote sensing semantic sequence corresponding to each keyword in the keyword sequence one by one; taking the above example as an example, the keyword sequence (2019; Langfang; air quality) can be converted into: the remote sensing semantic sequence (2019 whole year; Langfang City; aerosol optical depth).

[0069] Construct a standardized keyword group sequence, and each standardized keyword group includes: a keyword and the remote sensing semantics corresponding to the keyword; taking the above example as an example, the remote sensing semantic sequence (20190101 - 20191231; Langfang City, Hebei Province; aerosol optical depth) can be converted into: the standardized keyword group sequence (timekey; lokey; prokey) after standardization, where timekey is defined as <time, 20190101 - 20191231>, lokey is defined as <location, Langfang City, Hebei Province>, and prokey is <product, AOD>.

[0070] Filter the standardized keyword group sequence to obtain a task keyword group; normalize the task keyword group to obtain product information. Taking the above example as an example, the standardized keyword group sequence Task(AOD, 20190101 - 20191231, Hebei Langfang).

[0071] In another embodiment, retrieve using the time period information and location information corresponding to the product name as keywords to obtain the data source information corresponding to the product name; the data source information includes: the data source name and the file path.

[0072] In another embodiment, deduce the corresponding output information based on the data source information corresponding to the product name.

[0073] According to the fixed product production naming rules and storage rules, represent it with Inputs based on the data source information, and deduce the product output information, which is represented by OutPuts. The Inputs and OutPuts are for the input and output of the product model and are necessary parameters for the operation of the product model to facilitate the normal operation of the product model.

[0074] In a preferred embodiment, the present application further provides a computer device, which includes: a memory for storing executable program code; one or more processors for reading the executable program code stored in the memory to execute a remote sensing common product production scheduling method as described above. Please refer to Figure 4 the schematic diagram of the computer device hardware structure given.

[0075] The computer system includes a central processing unit (CPU) 501, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 502 or the program loaded from the storage part into the random access memory (RAM) 503. In the RAM 503, various programs and data required for system operation are also stored. The CPU 501, ROM 502, and RAM 503 are connected to each other through a bus 504. The input / output (I / O) interface 505 is also connected to the bus 504.

[0076] The following components are connected to the I / O interface 505: an input part 506 including a keyboard, a mouse, etc.; an output part including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker; a storage part 508 including a hard disk, etc.; and a communication part 509 including a network interface card such as a LAN card, a modem, etc. The communication part 509 performs communication processing via a network such as the Internet. A drive is also connected to the I / O interface 505 as needed. A removable medium 511, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 510 as needed so that the computer program read from it can be installed into the storage part 508 as needed.

[0077] In particular, according to an embodiment of the present invention, the process described in the above-mentioned method for scheduling the production of remote sensing common products can be implemented as a computer software program. For example, an embodiment of the present invention regarding a method for scheduling the production of remote sensing common products includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication part, and / or installed from a removable medium. When the computer program is executed by the central processing unit (CPU) 501, the above-mentioned functions defined in the system of the present application are executed.

[0078] It should be noted that the computer-readable medium shown in the present invention can be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device. In the present invention, the computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which the computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, and the computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0079] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of a method, apparatus, and computer program product for producing and scheduling remote sensing common products according to various aspects of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code, and the above-mentioned module, segment of a program, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as combinations of blocks in the block diagram or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0080] The units involved in the embodiments described in the present invention can be implemented in software or in hardware, and the described units can also be provided in a processor. Among them, the names of these units do not, in some cases, constitute a limitation on the units themselves. The described units or modules can also be provided in a processor. For example, it can be described as: a processor includes a first generation module, an acquisition module, a search module, a second generation module, and a merging module. Among them, the names of these units or modules do not, in some cases, constitute a limitation on the units or modules themselves.

[0081] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or may exist separately without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device is caused to implement a method for producing and scheduling remote sensing common products as described in the above embodiments.

[0082] It should be noted that although several modules or units of a device for performing actions are mentioned in the above detailed description, such a division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more of the above-mentioned modules or units can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0083] In addition, although the various steps of the methods in the present disclosure are described in a specific order in the drawings, this does not require or imply that these steps must be performed in that specific order, or that all of the steps shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step and performed, and / or one step may be decomposed into multiple steps and performed, etc.

[0084] The above description is only a preferred embodiment of the present application and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present application.

Claims

1. A production scheduling method for remote sensing common products, characterized in that The method includes: Constructing a set of product models corresponding to the product; Distinguishing the levels of each product model in the set of product models; Gradually dispatching the product models at the same level to production nodes; Constructing a set of product models corresponding to the product includes the following steps: Constructing a product dependency tree corresponding to the product; Analyzing the product models corresponding to each node in the product dependency tree, where the product models include: product level, product dependency, and execution status; Summarizing all product models to obtain a set of product models; Classifying the product models in the set of product models according to the levels of each product model, and grouping the product models at the same level into one category; the levels corresponding to each category of product models are the same. Arrange all categories of product models in ascending order according to their corresponding levels, and then gradually dispatch them to production nodes for production.

2. The remote sensing common product production scheduling method according to claim 1, wherein Before constructing the product dependency tree corresponding to the product, it further includes: obtaining the product information, where the product information includes: product name, time period information, and location information.

3. The remote sensing common product production scheduling method according to claim 2, wherein The step of obtaining the product information includes: Splitting natural sentences to obtain a keyword sequence; Querying the remote sensing semantic sequence corresponding to each keyword in the keyword sequence one by one; Constructing a standardized keyword group sequence, where each standardized keyword group includes: a keyword and the remote sensing semantics corresponding to the keyword; Filtering the standardized keyword group sequence to obtain a task keyword group; Normalizing the task keyword group to obtain product information.

4. The remote sensing common product production scheduling method according to claim 2, wherein, It further includes: Retrieving with the time period information and location information corresponding to the product name as keywords to obtain the data source information corresponding to the product name; The data source information includes: data source name and file path.

5. The remote sensing common product production scheduling method according to claim 3, wherein It further includes: Deriving the corresponding output information based on the data source information corresponding to the product name.

6. A computer device, characterized in that, The device includes: a memory for storing executable program code; one or more processors for reading the executable program code stored in the memory to execute a remote sensing common product production scheduling method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes instructions that, when run on a computer, cause the computer to execute a remote sensing common product production scheduling method according to any one of claims 1 to 5.

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