Distributed real-time calculation scheduling system and method

Abstract

The invention relates to a distributed real-time calculation scheduling system and method, and belongs to the technical field of non-electrical variable control or adjustment systems, and the method comprises the following steps: 1), obtaining target data after distributed non-electrical variables are converted into respective electrical signals; 2) determining a real-time calculation scheduling target according to the target data of each electric signal, and establishing a scheduling model of the real-time calculation scheduling target; step 3), establishing a real-time calculation scheduling strategy according to the scheduling target preference in the scheduling model; 4) monitoring the scheduling model of the real-time calculation scheduling target in the step 2) and the real-time calculation scheduling strategy in the step 3) in real time; the method has the beneficial effects that according to the established scheduling model of the real-time calculation scheduling target and the established real-time calculation scheduling strategy, real-time monitoring and scheduling of the scheduling model of the real-time calculation scheduling target and the real-time calculation scheduling strategy are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of control or regulation systems for non-electrical variables, and in particular to a distributed real-time computing scheduling system and method. Background Art

[0002] Non-electrical variables are non-electrical quantities, such as temperature, pressure, speed, displacement, strain, flow, and liquid level. The measurement of non-electrical variables usually requires the use of sensors to convert non-electrical physical quantities into electrical signals, which are then processed and displayed. For example, a temperature sensor can convert temperature changes into electrical signals, and a pressure sensor can convert pressure changes into electrical signals. These electrical signals can be further used for control and monitoring. Distributed non-electrical variables convert each decentralized non-electrical variable in the system into different electrical signals. The electrical signals of different types of non-electrical variables can be closely linked together through scheduling. After the distributed non-electrical variables are converted into their own electrical signals, each electrical signal is scheduled in real time. The real-time scheduling of each electrical signal has changed the traditional scheduling of different electrical signals and developed in a more flexible, sustainable and intelligent direction. This real-time scheduling of the distributed non-electrical variable system can maximize the utilization and scheduling of non-electrical variables and improve the scheduling efficiency of distributed non-electrical variables. Summary of the invention

[0003] The technical problem to be solved by the present invention is how to perform real-time scheduling of various electrical signals. The present invention provides a distributed real-time computing scheduling system and method, which finds out a certain sub-data corresponding to each target data, classifies each found sub-data, establishes a scheduling model of the real-time computing scheduling target according to the classified sub-data, and then establishes a real-time computing scheduling strategy according to the scheduling target preference, so as to realize real-time monitoring and scheduling of the scheduling model of the real-time computing scheduling target and the real-time computing scheduling strategy.

[0004] In order to achieve the above object, the present invention is implemented by the following technical solutions: A distributed real-time computing scheduling method comprises the following steps: Step 1): Obtain target data after the distributed non-electrical variables are converted into respective electrical signals; Step 2): Determine the real-time calculation scheduling target according to the target data of each electrical signal, and establish a scheduling model for the real-time calculation scheduling target; wherein the scheduling model for the real-time calculation scheduling target is established by using the target vector method; Step 3): Establish a real-time computing scheduling strategy according to the scheduling target preference in the scheduling model; wherein the real-time computing scheduling strategy is established by adopting a real-time preference method; Step 4): monitor in real time the scheduling model of the real-time computing scheduling target in step 2) and the real-time computing scheduling strategy in step 3).

[0005] Optionally, in step 1), the target data of the respective electrical signals are classified using a data grid method, and the data grid method is the following formula (1): (1); in, , and They are first target data, second target data and third target data respectively; , and They are respectively a certain sub-data of the first target data, a certain sub-data of the second target data, and a certain sub-data of the third target data; To target the operation; , and The first target data is targeted at a certain sub-data of the first target data, the second target data is targeted at a certain sub-data of the second target data, and the third target data is targeted at a certain sub-data of the third target data; For data connection operations, Connect each sub-data; For a certain period of time, To adjust a certain time period; To extract a certain sub-data of the first target data, a certain sub-data of the second target data and a certain sub-data of the third target data; Extracting a certain sub-data of the first target data, a certain sub-data of the second target data and a certain sub-data of the third target data within a unit time; for classifying a certain sub-data of the first target data, a certain sub-data of the second target data, and a certain sub-data of the third target data; For each sub-data after classification.

[0006] Optionally, in step 1), the target data of the respective electrical signals are classified using a data grid method, using the following steps: Step a): within a certain time period, each target data is directed to the corresponding sub-data in each target data; Step b): extracting the sub-data corresponding to each target data within a unit time; Step c): classify each sub-data separately.

[0007] Optionally, in step 2), the target vector method is the following formula (2)-formula (3): (2); (3); In formula (2), For each sub-data after classification, To select a sub-data after classification, For trigger operations performed within a fixed unit of time, is a fixed unit of time, To select a certain sub-data after classification from various sub-data after classification within a fixed unit time; is the scheduling model; To feed the selected and classified sub-data into the scheduling model Operations within To send the selected and classified sub-data to the scheduling model Internal delivery; In order to select a certain sub-data from the classified sub-data to the scheduling model within a fixed unit time Internal delivery, Scheduling model for real-time calculation of scheduling targets; In formula (3), is a sub-data of the first target data, is a sub-data of the second target data, For a sub-data of the third target data, a scheduling model is formed by scheduling each sub-data .

[0008] Optionally, in step 2), the target vector method is as follows: Step A): triggering selection of a sub-data after classification within a fixed unit time; Step B): The selected and classified sub-data is fed into the scheduling model to construct a scheduling model for real-time calculation of scheduling targets.

[0009] Optionally, in step 3), the real-time preference mode is the following formula (4): (4); in, is a sub-data randomly selected from the first target data, is a sub-data randomly selected from the second target data; is a sub-data randomly selected from the third target data; To connect the randomly selected sub-data; is a fixed unit of time; Preference for scheduling objectives; For scheduling operations; To establish a real-time computing scheduling strategy based on the scheduling target preference in the scheduling model; Calculate scheduling strategies for real-time computing.

[0010] Optionally, in step 3), the real-time preference method is as follows: Step a'): randomly selecting sub-data corresponding to each target data in each target data within a fixed unit time; Step b'): Establish a real-time computing scheduling strategy based on the scheduling target preference in the scheduling model.

[0011] Optionally, in step 4), the scheduling model of the real-time computing scheduling target and the real-time computing scheduling strategy are monitored in real time according to the real-time computing scheduling target and the scheduling target preference.

[0012] A distributed real-time computing scheduling system, comprising: A data classification module, used for classifying a sub-data corresponding to each of the target data; A data scheduling module, used to establish a scheduling model for real-time calculation of scheduling targets, wherein the data scheduling module is connected to the data classification module; A scheduling target preference module, used to establish a real-time computing scheduling strategy, wherein the scheduling target preference module is connected to the data scheduling module; A monitoring module, used for real-time monitoring of the scheduling model of the real-time computing scheduling target in the data scheduling module and the real-time computing scheduling strategy in the data scheduling module, the monitoring module being connected to the data scheduling module and the scheduling target preference module; The data classification module is used to classify the sub-data corresponding to each target data. The classified sub-data establishes a scheduling model of the real-time computing scheduling target in the data scheduling module and a real-time computing scheduling strategy in the scheduling target preference module. The monitoring module then monitors the scheduling model of the real-time computing scheduling target and the real-time computing scheduling strategy in real time.

[0013] Beneficial effects of the present invention: The present invention mainly finds out a certain sub-data corresponding to each target data, classifies each sub-data found, establishes a scheduling model of the real-time computing scheduling target according to each classified sub-data, and then establishes a real-time computing scheduling strategy according to the scheduling target preference, so as to realize real-time monitoring and scheduling of the scheduling model of the real-time computing scheduling target and the real-time computing scheduling strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 It is a schematic diagram of the system structure of the present invention; Figure 2 It is the work flow chart of the present invention. DETAILED DESCRIPTION

[0016] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0017] The terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0018] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense, for example, it can be welding, bolt connection, riveting, or data-related connection; it can be fixed connection, detachable connection, or integral connection; it can be mechanical connection or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be internal communication between two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances. Example

[0019] like Figure 1 As shown, Figure 1 As shown, this embodiment provides a distributed real-time computing scheduling system, including: The data acquisition module is used to obtain target data after the distributed non-electrical variables are converted into respective electrical signals; The data classification module is used for classifying a certain sub-data corresponding to each of the target data; The data scheduling module is used to establish a scheduling model for real-time calculation scheduling targets, and the data scheduling module is connected to the data classification module; The scheduling target preference module is used to establish a real-time computing scheduling strategy, and the scheduling target preference module is connected to the data scheduling module; The monitoring module is used to monitor in real time the scheduling model of the real-time computing scheduling target in the data scheduling module and the real-time computing scheduling strategy in the data scheduling module, and the monitoring module is connected to the data scheduling module and the scheduling target preference module; The data acquisition module obtains the target data after the distributed non-electrical variables are converted into their respective electrical signals, and classifies the corresponding sub-data in each target data through the data classification module. The classified sub-data establishes the scheduling model of the real-time computing scheduling target in the data scheduling module and the real-time computing scheduling strategy in the scheduling target preference module. The monitoring module then monitors the scheduling model and real-time computing scheduling strategy of the real-time computing scheduling target in real time. Example

[0020] Based on Example 1, Figure 2 As shown, this embodiment provides a distributed real-time computing scheduling method, including the following steps: Step 1): Obtain target data after converting distributed (pressure, displacement or strain) non-electrical variables into respective electrical signals; Step 2): Determine the real-time calculation scheduling target according to the target data of each electrical signal, and establish a scheduling model for the real-time calculation scheduling target; wherein the scheduling model for the real-time calculation scheduling target is established by using the target vector method; Step 3): Establish a real-time computing scheduling strategy according to the scheduling target preference in the scheduling model; wherein the real-time computing scheduling strategy is established by adopting a real-time preference method; Step 4): Monitor the scheduling model in real time.

[0021] Specifically, in step 1), the target data of each electrical signal is classified using a data grid method, and the data grid method is the following formula (1): (1); in, , and They are first target data, second target data and third target data respectively; , and They are a sub-data of the first target data, a sub-data of the second target data, and a sub-data of the third target data (the first target data There are multiple sub-data, second target data Having multiple sub-data and third target data There are multiple sub-data in the first target data Select a sub-data in the second target data Select a sub-data in the third target data Select a sub-data in ); To target the operation; , and The first target data is targeted at a certain sub-data of the first target data, the second target data is targeted at a certain sub-data of the second target data, and the third target data is targeted at a certain sub-data of the third target data; For data connection operations, Connect each sub-data; For a certain period of time, To adjust a certain time period (i.e. the length of a certain time period can be adjusted); What needs to be explained here is that target-oriented operation The operation is performed according to the setting, that is, the first target data Targeted operation The setting is targeted to a sub-data of the first target data; similarly, the second target data Targeted operation The setting is targeted at a sub-data of the second target data; the third target data Targeted operation The setting is targeted at a sub-data of the third target data.

[0022] To extract a certain sub-data of the first target data, a certain sub-data of the second target data and a certain sub-data of the third target data; Extracting a certain sub-data of the first target data, a certain sub-data of the second target data and a certain sub-data of the third target data within a unit time; for classifying a certain sub-data of the first target data, a certain sub-data of the second target data, and a certain sub-data of the third target data; For each sub-data after classification.

[0023] In addition, in step 1), the target data of the respective electrical signals are classified using a data grid method, using the following steps: Step a): within a certain time period, each target data is directed to the corresponding sub-data in each target data; Step b): extracting the corresponding sub-data from each target data within a unit time; Step c): classify each sub-data separately.

[0024] In step 2), the target vector method is as follows: Formula (2) - Formula (3): (2); (3); In formula (2), For each sub-data after classification, To select a sub-data after classification, For trigger operations performed within a fixed unit of time (i.e., trigger operations are performed every few seconds), is a fixed unit of time, To select a certain sub-data after classification from various sub-data after classification within a fixed unit time; is the scheduling model; To feed the selected and classified sub-data into the scheduling model Operations within To send the selected and classified sub-data to the scheduling model Internal delivery; In order to select a certain sub-data from the classified sub-data to the scheduling model within a fixed unit time Internal delivery, Scheduling model for real-time calculation of scheduling targets; In formula (3), is a sub-data of the first target data, is a sub-data of the second target data, For a sub-data of the third target data, a scheduling model is formed by scheduling each sub-data .

[0025] Combining formula (2) and formula (3), a sub-data after classification in the first target data is , a sub-data after classification in the second target data , a sub-data after classification in the third target data Feed into the scheduling model Finally, a scheduling model for real-time computing scheduling objectives is constructed. .

[0026] In addition, in step 2), the target vector method is as follows: Step A): triggering selection of a sub-data after classification within a fixed unit time; Step B): The selected and classified sub-data is fed into the scheduling model to construct a scheduling model for real-time calculation of scheduling targets.

[0027] In step 3), the real-time preference method is as follows: (4); in, is a sub-data randomly selected from the first target data, is a sub-data randomly selected from the second target data; is a sub-data randomly selected from the third target data; To connect the randomly selected sub-data; is a fixed unit of time; For the scheduling target preference ( Biased towards a randomly selected sub-data in the first target data , or a randomly selected sub-data in the second target data , or a randomly selected sub-data in the third target data ); For scheduling operations; To establish a real-time computing scheduling strategy based on the scheduling target preference in the scheduling model; Calculate scheduling strategies for real-time computing.

[0028] Formula (4) is a real-time random selection of sub-data according to the scheduling target preference or subdata or subdata .

[0029] In addition, in step 3), the real-time preference method is as follows: Step a'): randomly selecting sub-data corresponding to each target data in each target data within a fixed unit time; Step b'): Establish a real-time computing scheduling strategy based on the scheduling target preference in the scheduling model.

[0030] In step 4), the scheduling model and the real-time computing scheduling strategy of the real-time computing scheduling target are monitored in real time according to the real-time computing scheduling target and the scheduling target preference. Example

[0031] Based on Example 2, the present invention mainly finds out a certain sub-data corresponding to each target data, classifies each sub-data found, establishes a scheduling model of the real-time computing scheduling target according to the classified sub-data, and then establishes a real-time computing scheduling strategy according to the scheduling target preference. Example

[0032] Based on Example 2, in formula (1), the first target data There are multiple sub-data in the system, which can be operated according to the target The setting of the target is a sub-data in the first target data. ; Similarly, the second target data There are multiple sub-data in the system, which can be operated according to the target The setting of the target is a sub-data in the second target data. ; Third target data There are multiple sub-data in the system, which can be operated according to the target Setting, target oriented third target data A sub-data in .

[0033] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope of the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A distributed real-time computing scheduling method, characterized in that: The steps include: Step 1): Obtain target data after the distributed non-electrical variables are converted into respective electrical signals; Step 2): Determine the real-time calculation scheduling target according to the target data of each electrical signal, and establish a scheduling model for the real-time calculation scheduling target; wherein the scheduling model for the real-time calculation scheduling target is established by using the target vector method; Step 3): Establish a real-time computing scheduling strategy according to the scheduling target preference in the scheduling model; wherein the real-time computing scheduling strategy is established by adopting a real-time preference method; Step 4): monitor in real time the scheduling model of the real-time computing scheduling target in step 2) and the real-time computing scheduling strategy in step 3).

2. A distributed real-time computing scheduling method according to claim 1, characterized in that: In the step 1), the target data of the respective electrical signals are classified using a data grid method, and the data grid method is the following formula (1): (1)； in, , and They are first target data, second target data and third target data respectively; , and They are respectively a certain sub-data of the first target data, a certain sub-data of the second target data, and a certain sub-data of the third target data; To target the operation; , and The first target data is targeted at a certain sub-data of the first target data, the second target data is targeted at a certain sub-data of the second target data, and the third target data is targeted at a certain sub-data of the third target data; For data connection operations, Connect each sub-data; For a certain period of time, To adjust a certain time period; To extract a certain sub-data of the first target data, a certain sub-data of the second target data and a certain sub-data of the third target data; Extracting a certain sub-data of the first target data, a certain sub-data of the second target data and a certain sub-data of the third target data within a unit time; for classifying a certain sub-data of the first target data, a certain sub-data of the second target data, and a certain sub-data of the third target data; For each sub-data after classification.

3. A distributed real-time computing scheduling method according to claim 1, characterized in that: In the step 1), the target data of the respective electrical signals are classified using a data grid method, using the following steps: Step a): within a certain time period, each target data is directed to the corresponding sub-data in each target data; Step b): extracting the sub-data corresponding to each target data within a unit time; Step c): classify each sub-data separately.

4. A distributed real-time computing scheduling method according to claim 1, characterized in that: In the step 2), the target vector method is the following formula (2)-formula (3): (2)； (3)； In formula (2), For each sub-data after classification, To select a sub-data after classification, For trigger operations performed within a fixed unit of time, is a fixed unit of time, To select a certain sub-data after classification from various sub-data after classification within a fixed unit time; is the scheduling model; To feed the selected and classified sub-data into the scheduling model Operations within To send the selected and classified sub-data to the scheduling model Internal delivery; In order to select a certain sub-data from the classified sub-data to the scheduling model within a fixed unit time Internal delivery, Scheduling model for real-time calculation of scheduling targets; In formula (3), is a sub-data of the first target data, is a sub-data of the second target data, For a sub-data of the third target data, a scheduling model is formed by scheduling each sub-data .

5. A distributed real-time computing scheduling method according to claim 1, characterized in that: In step 2), the target vector method is as follows: Step A): triggering selection of a sub-data after classification within a fixed unit time; Step B): The selected and classified sub-data is fed into the scheduling model to construct a scheduling model for real-time calculation of scheduling targets.

6. A distributed real-time computing scheduling method according to claim 1, characterized in that: In step 3), the real-time preference method is the following formula (4): (4)； in, is a sub-data randomly selected from the first target data, is a sub-data randomly selected from the second target data; is a sub-data randomly selected from the third target data; To connect the randomly selected sub-data; is a fixed unit of time; Preference for scheduling objectives; For scheduling operations; To establish a real-time computing scheduling strategy based on the scheduling target preference in the scheduling model; Calculate scheduling strategies for real-time computing.

7. A distributed real-time computing scheduling method according to claim 1, characterized in that: In step 3), the real-time preference method is as follows: Step a'): randomly selecting sub-data corresponding to each target data in each target data within a fixed unit time; Step b'): Establish a real-time computing scheduling strategy based on the scheduling target preference in the scheduling model.

8. A distributed real-time computing scheduling method according to claim 1, characterized in that: In the step 4), the scheduling model of the real-time computing scheduling target and the real-time computing scheduling strategy are monitored in real time according to the real-time computing scheduling target and the scheduling target preference.

9. A distributed real-time computing scheduling system, used to execute a distributed real-time computing scheduling method according to any one of claims 1 to 8, characterized in that: include: A data classification module, used for classifying a sub-data corresponding to each of the target data; A data scheduling module, used to establish a scheduling model for real-time calculation of scheduling targets, wherein the data scheduling module is connected to the data classification module; A scheduling target preference module, used to establish a real-time computing scheduling strategy, wherein the scheduling target preference module is connected to the data scheduling module; A monitoring module, used for real-time monitoring of the scheduling model of the real-time computing scheduling target in the data scheduling module and the real-time computing scheduling strategy in the data scheduling module, the monitoring module being connected to the data scheduling module and the scheduling target preference module; The data classification module is used to classify the sub-data corresponding to each target data. The classified sub-data establishes a scheduling model of the real-time computing scheduling target in the data scheduling module and a real-time computing scheduling strategy in the scheduling target preference module. The monitoring module then monitors the scheduling model of the real-time computing scheduling target and the real-time computing scheduling strategy in real time.

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