Fuel monitoring control system for industrial automation

Abstract

The invention provides a fuel monitoring control system for industrial automation, and relates to the technical field of fuel monitoring control, and the system comprises a state determination module which is used for carrying out data collection, obtaining first fuel data, and analyzing the real-time fuel state of target fuel; the first prediction module is used for extracting a historical fuel state corresponding to the real-time fuel state and predicting a fuel state change trend in combination with the real-time fuel state to obtain a first prediction result; the strategy determination module is used for comparing the first prediction result with a standard fuel state and determining a fuel control strategy; and the optimization adjustment module is used for performing strategy optimization based on the fuel energy consumption demand in the current industrial automation scene so as to adjust the target fuel. By predicting the state trend of the real-time fuel state and comparing the state trend with the standard fuel state, the fuel control strategy is determined, adjustment of the target fuel can be more accurate, and application requirements of multiple industrial automation application scenarios can be better met.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel monitoring and control, and in particular to a fuel monitoring and control system for industrial automation. Background Art

[0002] At present, the fuel monitoring and control system based on industrial automation is one of the important applications of industrial automation technology in the field of energy management. The existing fuel monitoring and control system is generally an integrated system integrating industrial automation control technology, sensor technology, data communication technology and data analysis technology, thereby realizing real-time monitoring and control of fuel consumption.

[0003] However, the existing real-time monitoring of fuel consumption can only monitor the fuel in real time, but cannot predict the fuel status, resulting in inaccurate fuel control and waste of resources.

[0004] Therefore, the present invention provides a fuel monitoring control system for industrial automation. Summary of the invention

[0005] The present invention provides a fuel monitoring and control system for industrial automation, which is used to solve the defects of the prior art that the fuel control is not accurate, which easily causes fuel waste and increased energy consumption.

[0006] The present invention provides a fuel monitoring and control system for industrial automation, comprising: A state determination module: used for collecting fuel-related data based on a preset sensor, obtaining first fuel data, and analyzing the real-time fuel state of the target fuel based on the first fuel data; The first prediction module is used to extract the historical fuel state corresponding to the industrial automation scenario corresponding to the real-time fuel state, and predict the fuel state change trend in combination with the real-time fuel state to obtain a first prediction result; A strategy determination module: used for comparing the first prediction result with the standard fuel state, so as to determine the fuel control strategy of the target fuel; Optimization and adjustment module: used to optimize the fuel control strategy based on the fuel energy consumption demand in the current industrial automation scenario, and adjust the target fuel based on the optimized fuel control strategy.

[0007] The state determination module provided by the present invention includes: A data acquisition unit: used for acquiring fuel-related data of a target fuel based on a preset sensor to obtain first fuel data; A data extraction unit: used for acquiring a first data type related to the real-time fuel state of the target fuel, and extracting fuel data matching the first data type from the first fuel data to obtain second fuel data; A first state unit: used for comparing the second fuel data with the fuel data in the fuel-state database, thereby extracting a first fuel state corresponding to each sub-fuel data in the second fuel data; Real-time status unit: used to obtain the real-time fuel status of the second fuel data based on the integration of the first fuel status corresponding to each sub-fuel data in the second fuel data, that is, the real-time fuel status of the target fuel.

[0008] The first prediction module provided according to the present invention includes: A scene parameter acquisition unit: used to extract the real-time scene parameters of the industrial automation scene corresponding to the real-time fuel state, and to acquire the historical scene parameters corresponding to each valid historical fuel data based on the preset sensor; A historical state extraction unit: used to compare the historical scene parameters with the real-time scene parameters, and extract the corresponding historical fuel data and the corresponding historical fuel state based on the similarity between the historical scene parameters and the real-time scene parameters; A historical state sorting unit: used to sort the historical fuel states based on the similarity between the scene parameters corresponding to the historical fuel states and the real-time scene parameters, to obtain a first ordered fuel state set; A state group set unit: used to obtain a historical fuel state corresponding to a next fuel acquisition cycle of each historical fuel state in the first ordered fuel state set, obtain a historical fuel state group, and obtain a historical fuel state group set based on each historical fuel state group; Initial trend unit: used to determine the state change trend of each historical fuel state group, and extract the state change trend corresponding to the first historical fuel state group whose corresponding historical scene parameters have the highest similarity with the real-time scene parameters as the initial state trend; The first trend unit is used to perform weighted correction on the initial state trend one by one based on the similarity between the historical scene parameters corresponding to each remaining historical fuel state group and the real-time scene parameters to obtain the first state trend; Among them, the lower the similarity, the lower the weight of the corresponding weighted correction; A trend prediction unit: used for predicting the fuel state change trend of the real-time fuel state based on the first state trend, so as to obtain the fuel state of the next fuel acquisition cycle of the real-time fuel state; Among them, the fuel state of the next fuel acquisition cycle of the real-time fuel state is the first prediction result.

[0009] The first trend unit provided according to the present invention comprises: The first correction subunit is used to extract the state change trend corresponding to the first historical fuel state group whose corresponding historical scene parameters of the remaining first historical fuel state groups have the highest similarity with the real-time scene parameters as the first correction trend; Weight conversion subunit: used to compare the similarity between the historical scene parameters and the real-time scene parameters corresponding to the first correction trend, and perform similarity-weight conversion; Among them, the higher the similarity, the higher the weight; Weighted correction subunit: used to perform weighted correction on the initial state trend by combining the first correction trend with the similarity-weight conversion result, until the state trend corresponding to each first historical fuel state group in the first historical fuel state group set is weightedly corrected to obtain the first state trend.

[0010] The strategy determination module provided by the present invention includes: A state difference determination unit: used for comparing the first prediction result with the corresponding standard fuel state under the real-time scene parameters, so as to obtain a first difference between each sub-fuel state in the first prediction result and the sub-fuel state corresponding to the standard fuel state; Strategy mapping unit: mapping the state type of the first difference and the corresponding sub-fuel state with the difference of the corresponding state type in the difference-strategy mapping table, so as to obtain the fuel control strategy corresponding to each first difference; An initial strategy determination unit: used to obtain an initial fuel control strategy of a target fuel based on a fuel control strategy corresponding to each difference; Strategy optimization unit: used to determine whether there are fuel control strategies with repeated or conflicting strategies in the initial fuel control strategy, and to optimize the strategy to obtain the fuel control strategy of the target fuel.

[0011] The optimization and adjustment module provided by the present invention includes: Energy consumption determination unit: used for inputting the fuel control strategy based on the target fuel into the virtual machine, and performing simulation control in combination with the first fuel data, so as to obtain the first fuel energy consumption of the target fuel adjusted based on the fuel control strategy; Energy consumption comparison unit: used to obtain the maximum fuel energy consumption requirement in the current industrial automation scenario, and determine whether the first fuel energy consumption is greater than the maximum fuel energy consumption requirement; If the first fuel energy consumption is greater than the maximum fuel energy consumption requirement, the fuel control strategy is optimized to obtain a first optimization strategy, and the target fuel is adjusted based on the first optimization strategy; Otherwise, the target fuel is adjusted based on the fuel control strategy.

[0012] The energy consumption comparison unit provided by the present invention comprises: If the first fuel energy consumption is greater than the maximum fuel energy consumption requirement, then obtaining a fuel control strategy, and determining a second fuel energy consumption required for each sub-control strategy in the fuel control strategy; Sort each sub-control strategy in the fuel control strategy according to the second fuel energy consumption, and extract the remaining control strategies with the same difference from the corresponding difference-strategy mapping table based on the second fuel energy consumption from high to low to replace the current sub-control strategy; The fuel control strategy is optimized based on the strategy replacement result of the sub-control strategy to obtain a first optimization strategy, and the target fuel is adjusted based on the first optimization strategy.

[0013] The state verification module provided by the present invention includes: An optimization state determination unit: used for acquiring first optimized fuel data corresponding to the target fuel after adjustment based on the first optimization strategy, and analyzing the target fuel based on the first optimized fuel data to obtain an optimized fuel state of the target fuel; Optimization state comparison unit: used to compare the optimized fuel state with the standard fuel state in the current industrial automation scenario; If a first difference between each sub-fuel state of the optimized fuel state and the corresponding sub-fuel state of the standard fuel state is less than a preset difference, it is determined that the state control of the target fuel is completed; Otherwise, the corresponding fuel control strategy is obtained based on the first difference between each sub-fuel state of the optimized fuel state and the sub-fuel state corresponding to the standard fuel state for readjustment.

[0014] The present invention provides a fuel monitoring and control system for industrial automation, which obtains real-time fuel status by analyzing fuel data, and predicts the status trend of the real-time fuel status based on the historical fuel status, thereby comparing the predicted result with the standard fuel status, thereby determining the fuel control strategy, and adjusting the target fuel after optimizing the strategy, so that the adjustment of the target fuel can be more accurate and better meet the application requirements of each industrial automation application scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0016] Figure 1 It is a structural diagram of a fuel monitoring and control system for industrial automation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] Embodiment 1: The embodiment of the present invention provides a fuel monitoring control system for industrial automation, such as Figure 1 As shown, including: A state determination module: used for collecting fuel-related data based on a preset sensor, obtaining first fuel data, and analyzing the real-time fuel state of the target fuel based on the first fuel data; The first prediction module is used to extract the historical fuel state corresponding to the industrial automation scenario corresponding to the real-time fuel state, and predict the fuel state change trend in combination with the real-time fuel state to obtain a first prediction result; A strategy determination module: used for comparing the first prediction result with the standard fuel state, so as to determine the fuel control strategy of the target fuel; Optimization and adjustment module: used to optimize the fuel control strategy based on the fuel energy consumption demand in the current industrial automation scenario, and adjust the target fuel based on the optimized fuel control strategy.

[0019] In this embodiment, the first fuel data refers to the fuel parameters of the target fuel collected by the sensor, wherein the first fuel data includes physical property parameters, chemical property parameters, storage and use environment parameters, etc.

[0020] In this embodiment, the real-time fuel state includes: real-time physical state, real-time flow rate, real-time pressure, real-time temperature, real-time density, real-time composition, real-time combustion efficiency and other state parameters of the target fuel.

[0021] In this embodiment, the historical fuel state refers to the fuel state corresponding to the fuel data collected by the preset sensor during the historical data collection process.

[0022] In this embodiment, the state change trend refers to the change trend of the fuel state corresponding to each fuel state and the next fuel acquisition cycle.

[0023] In this embodiment, the first prediction result refers to a trend prediction of the state change trend of the real-time fuel state according to the state change trend of the historical fuel state, thereby predicting the fuel state corresponding to the next fuel acquisition cycle according to the trend prediction result.

[0024] In this embodiment, the standard fuel state refers to the standard fuel state of fuel consumption of the target fuel in the current industrial automation scenario, and the standard fuel state is predetermined based on the fuel parameters of the target fuel and the scenario parameters of the industrial automation scenario.

[0025] In this embodiment, the fuel control strategy refers to comparing the first prediction result with the standard fuel state to obtain the difference of each sub-fuel state, and extracting the fuel control strategy corresponding to the sub-fuel state according to the difference-strategy mapping table to obtain the comprehensive fuel control strategy of the first prediction result.

[0026] In this embodiment, the fuel energy consumption demand is comprehensively determined based on the equipment characteristics, production goals, fuel types, energy efficiency, and economic conditions in the current industrial automation scenario.

[0027] The beneficial effects of the above technical solution are: the real-time fuel status is obtained by analyzing the fuel data, and the status trend of the real-time fuel status is predicted based on the historical fuel status, so as to compare the predicted result with the standard fuel status, thereby determining the fuel control strategy, and adjusting the target fuel after optimizing the strategy, so that the adjustment of the target fuel can be more accurate and more in line with the application requirements of each industrial automation application scenario.

[0028] Embodiment 2: Based on Example 1, the state determination module includes: A data acquisition unit: used for acquiring fuel-related data of a target fuel based on a preset sensor to obtain first fuel data; A data extraction unit: used for acquiring a first data type related to the real-time fuel state of the target fuel, and extracting fuel data matching the first data type from the first fuel data to obtain second fuel data; A first state unit: used for comparing the second fuel data with the fuel data in the fuel-state database, thereby extracting a first fuel state corresponding to each sub-fuel data in the second fuel data; Real-time status unit: used to obtain the real-time fuel status of the second fuel data based on the integration of the first fuel status corresponding to each sub-fuel data in the second fuel data, that is, the real-time fuel status of the target fuel.

[0029] In this embodiment, the first fuel data refers to the fuel parameters of the target fuel collected by the sensor, wherein the first fuel data includes physical property parameters, chemical property parameters, storage and use environment parameters, etc.

[0030] In this embodiment, the second fuel data refers to fuel data related to the real-time fuel state extracted from the first fuel data.

[0031] In this embodiment, the first fuel state refers to the fuel state corresponding to each sub-data in the second fuel data obtained by comparing the second fuel data with the fuel data in the fuel-state database.

[0032] In this embodiment, the real-time fuel state includes: real-time physical state, real-time flow rate, real-time pressure, real-time temperature, real-time density, real-time composition, real-time combustion efficiency and other state parameters of the target fuel.

[0033] The beneficial effect of the above technical solution is: by analyzing the fuel data, the real-time fuel status is obtained, so that the status trend of the real-time fuel status can be predicted based on the historical fuel status, which can make the prediction of the target fuel more accurate, thereby making the adjustment of the target fuel more accurate.

[0034] Embodiment 3: Based on the second embodiment, the first prediction module includes: A scene parameter acquisition unit: used to extract the real-time scene parameters of the industrial automation scene corresponding to the real-time fuel state, and to acquire the historical scene parameters corresponding to each valid historical fuel data based on the preset sensor; A historical state extraction unit: used to compare the historical scene parameters with the real-time scene parameters, and extract the corresponding historical fuel data and the corresponding historical fuel state based on the similarity between the historical scene parameters and the real-time scene parameters; A historical state sorting unit: used to sort the historical fuel states based on the similarity between the scene parameters corresponding to the historical fuel states and the real-time scene parameters, to obtain a first ordered fuel state set; A state group set unit: used to obtain a historical fuel state corresponding to a next fuel acquisition cycle of each historical fuel state in the first ordered fuel state set, obtain a historical fuel state group, and obtain a historical fuel state group set based on each historical fuel state group; Initial trend unit: used to determine the state change trend of each historical fuel state group, and extract the state change trend corresponding to the first historical fuel state group whose corresponding historical scene parameters have the highest similarity with the real-time scene parameters as the initial state trend; The first trend unit is used to perform weighted correction on the initial state trend one by one based on the similarity between the historical scene parameters corresponding to each remaining historical fuel state group and the real-time scene parameters to obtain the first state trend; Among them, the lower the similarity, the lower the weight of the corresponding weighted correction; A trend prediction unit: used for predicting the fuel state change trend of the real-time fuel state based on the first state trend, so as to obtain the fuel state of the next fuel acquisition cycle of the real-time fuel state; Among them, the fuel state of the next fuel acquisition cycle of the real-time fuel state is the first prediction result.

[0035] In this embodiment, the real-time scenario parameters include parameters such as fuel consumption, dye level, fuel quality, and real-time output power of the industrial automation scenario.

[0036] In this embodiment, the historical scene parameters refer to scene parameters corresponding to the same type of industrial automation scene as the current industrial automation scene and retained in the sensor.

[0037] In this embodiment, historical fuel data and historical fuel status are extracted according to historical scenario parameters.

[0038] In this embodiment, the first ordered fuel state set refers to an ordered state set obtained by sorting the historical fuel states according to the similarity between the scene parameters corresponding to the historical fuel states and the real-time scene parameters.

[0039] In this embodiment, the historical fuel state group set refers to obtaining the historical fuel state corresponding to the next fuel acquisition cycle of each historical fuel state in the first ordered fuel state set, and forming a historical fuel state group, thereby obtaining a historical fuel state group set. For example, the historical fuel state at time A is 12, and the historical fuel state corresponding to the next fuel acquisition cycle is 15, then the corresponding historical fuel state group is 12-15.

[0040] In this embodiment, the state change trend refers to the state change trend of each historical fuel state group in the historical fuel state group set. For example, the historical fuel state at time A is 12, and the corresponding historical fuel state in the next fuel acquisition cycle is 15. The corresponding historical fuel state group is 12-15, and the corresponding state change trend is rising, and the rising value is 3.

[0041] In this embodiment, the initial state trend is a state trend obtained by extracting the state change trend corresponding to the first historical fuel state group whose corresponding historical scene parameters have the highest similarity with the real-time scene parameters.

[0042] In this embodiment, the first state trend refers to a state trend obtained by weightedly correcting the initial state trend one by one according to the similarity between the historical scene parameters corresponding to each remaining historical fuel state group and the real-time scene parameters.

[0043] In this embodiment, the first prediction result is the fuel state of the next fuel acquisition cycle of the real-time fuel state predicted according to the first state trend.

[0044] The beneficial effect of the above technical solution is: by combining the historical fuel status to predict the state trend of the real-time fuel status, the predicted result can be compared with the standard fuel status, which can make the adjustment of the target fuel more accurate and more in line with the application requirements of each industrial automation application scenario.

[0045] Embodiment 4: Based on Example 3, the first trend unit includes: The first correction subunit is used to extract the state change trend corresponding to the first historical fuel state group whose corresponding historical scene parameters of the remaining first historical fuel state groups have the highest similarity with the real-time scene parameters as the first correction trend; Weight conversion subunit: used to compare the similarity between the historical scene parameters and the real-time scene parameters corresponding to the first correction trend, and perform similarity-weight conversion; Among them, the higher the similarity, the higher the weight; Weighted correction subunit: used to perform weighted correction on the initial state trend by combining the first correction trend with the similarity-weight conversion result, until the state trend corresponding to each first historical fuel state group in the first historical fuel state group set is weightedly corrected to obtain the first state trend.

[0046] In this embodiment, the first correction trend refers to extracting the state change trend corresponding to the first historical fuel state group whose corresponding historical scene parameters have the highest similarity with the real-time scene parameters except the initial state trend as the first correction trend.

[0047] In this embodiment, the first state trend refers to a state trend obtained by weightedly correcting the initial state trend one by one according to the similarity between the historical scene parameters corresponding to each remaining historical fuel state group and the real-time scene parameters.

[0048] The beneficial effect of the above technical solution is: by combining the historical fuel status to predict the state trend of the real-time fuel status and correcting the prediction result, the adjustment of the target fuel can be more accurate and more in line with the application requirements of each industrial automation application scenario.

[0049] Embodiment 5: Based on Example 3, the strategy determination module includes: A state difference determination unit: used for comparing the first prediction result with the corresponding standard fuel state under the real-time scene parameters, so as to obtain a first difference between each sub-fuel state in the first prediction result and the sub-fuel state corresponding to the standard fuel state; Strategy mapping unit: mapping the state type of the first difference and the corresponding sub-fuel state with the difference of the corresponding state type in the difference-strategy mapping table, so as to obtain the fuel control strategy corresponding to each first difference; An initial strategy determination unit: used to obtain an initial fuel control strategy of a target fuel based on a fuel control strategy corresponding to each difference; Strategy optimization unit: used to determine whether there are fuel control strategies with repeated or conflicting strategies in the initial fuel control strategy, and to optimize the strategy to obtain the fuel control strategy of the target fuel.

[0050] In this embodiment, the standard fuel state refers to the standard fuel state of fuel consumption of the target fuel in the current industrial automation scenario, and the standard fuel state is predetermined based on the fuel parameters of the target fuel and the scenario parameters of the industrial automation scenario.

[0051] In this embodiment, the first difference refers to the difference between each sub-fuel state in the first prediction result and the sub-fuel state corresponding to the standard fuel state.

[0052] In this embodiment, the difference-strategy mapping table includes all difference ranges between the first prediction result and the sub-fuel state of each state type in the standard fuel state and the state adjustment strategy corresponding to each difference range.

[0053] In this embodiment, the fuel control strategy refers to comparing the first prediction result with the standard fuel state to obtain the difference of each sub-fuel state, and extracting the fuel control strategy corresponding to the sub-fuel state according to the difference-strategy mapping table to obtain the comprehensive fuel control strategy of the first prediction result.

[0054] The beneficial effect of the above technical solution is: by predicting the state trend of the real-time fuel state, comparing the predicted result with the standard fuel state, determining the fuel control strategy, and adjusting the target fuel after optimizing the strategy, the adjustment of the target fuel can be made more accurate.

[0055] Embodiment 6: Based on Example 5, the optimization and adjustment module includes: Energy consumption determination unit: used for inputting the fuel control strategy based on the target fuel into the virtual machine, and performing simulation control in combination with the first fuel data, so as to obtain the first fuel energy consumption of the target fuel adjusted based on the fuel control strategy; Energy consumption comparison unit: used to obtain the maximum fuel energy consumption requirement in the current industrial automation scenario, and determine whether the first fuel energy consumption is greater than the maximum fuel energy consumption requirement; If the first fuel energy consumption is greater than the maximum fuel energy consumption requirement, the fuel control strategy is optimized to obtain a first optimization strategy, and the target fuel is adjusted based on the first optimization strategy; Otherwise, the target fuel is adjusted based on the fuel control strategy.

[0056] In this embodiment, the first fuel energy consumption refers to inputting the fuel control strategy of the target fuel into the virtual machine, and performing simulation control in combination with the first fuel data, so as to obtain the fuel energy consumption of the target fuel adjusted based on the fuel control strategy.

[0057] In this embodiment, the maximum fuel energy consumption demand refers to the maximum fuel energy consumption demand in the current industrial automation scenario. For example, due to environmental factors, the maximum fuel energy consumption in the current industrial automation scenario is 2000 kWh / h.

[0058] In this embodiment, the first optimization strategy refers to a strategy for optimizing the fuel control strategy to reduce the first fuel energy consumption when the first fuel energy consumption is greater than the maximum fuel energy consumption requirement.

[0059] The beneficial effect of the above technical solution is that by optimizing the fuel control strategy and then adjusting the target fuel, the adjustment of the target fuel can be made more precise and more in line with the application requirements of each industrial automation application scenario.

[0060] Embodiment 7: Based on Example 6, the energy consumption comparison unit includes: If the first fuel energy consumption is greater than the maximum fuel energy consumption requirement, then obtaining a fuel control strategy, and determining a second fuel energy consumption required for each sub-control strategy in the fuel control strategy; Sort each sub-control strategy in the fuel control strategy according to the second fuel energy consumption, and extract the remaining control strategies with the same difference from the corresponding difference-strategy mapping table based on the second fuel energy consumption from high to low to replace the current sub-control strategy; The fuel control strategy is optimized based on the strategy replacement result of the sub-control strategy to obtain a first optimization strategy, and the target fuel is adjusted based on the first optimization strategy.

[0061] In this embodiment, the second fuel energy consumption refers to the fuel energy consumption required by each sub-control strategy in the fuel control strategy.

[0062] The beneficial effect of the above technical solution is: by comparing the first fuel energy consumption greater than the maximum fuel energy consumption requirement, the fuel control strategy is optimized and then the target fuel is adjusted, so that the adjustment of the target fuel can be more accurate and more in line with the application requirements of each industrial automation application scenario.

[0063] Embodiment 8: Based on the sixth embodiment, the present invention further includes: a status verification module, specifically including: An optimization state determination unit: used for acquiring first optimized fuel data corresponding to the target fuel after adjustment based on the first optimization strategy, and analyzing the target fuel based on the first optimized fuel data to obtain an optimized fuel state of the target fuel; Optimization state comparison unit: used to compare the optimized fuel state with the standard fuel state in the current industrial automation scenario; If a first difference between each sub-fuel state of the optimized fuel state and the corresponding sub-fuel state of the standard fuel state is less than a preset difference, it is determined that the state control of the target fuel is completed; Otherwise, the corresponding fuel control strategy is obtained based on the first difference between each sub-fuel state of the optimized fuel state and the sub-fuel state corresponding to the standard fuel state for readjustment.

[0064] In this embodiment, the first optimized fuel data refers to fuel data collected based on a preset sensor after the target fuel is adjusted based on the first optimization strategy.

[0065] In this embodiment, the optimized fuel state refers to the fuel state obtained by analyzing the first optimized fuel.

[0066] The beneficial effect of the above technical solution is that by judging the fuel state of the adjusted target fuel, the unqualified control state can be adjusted more timely, thereby improving the equipment working efficiency in the corresponding industrial automation scenario.

[0067] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fuel monitoring and control system for industrial automation, characterized in that: include: A state determination module: used for collecting fuel-related data based on a preset sensor, obtaining first fuel data, and analyzing the real-time fuel state of the target fuel based on the first fuel data; The first prediction module is used to extract the real-time fuel state corresponding to the historical fuel state, and predict the fuel state change trend in combination with the real-time fuel state to obtain a first prediction result; A strategy determination module: used for comparing the first prediction result with the standard fuel state, so as to determine the fuel control strategy of the target fuel; Optimization and adjustment module: used to optimize the fuel control strategy based on the fuel energy consumption demand in the current industrial automation scenario, and adjust the target fuel based on the optimized fuel control strategy.

2. A fuel monitoring and control system for industrial automation according to claim 1, characterized in that: A state determination module, comprising: A data collection unit: used for collecting fuel-related data of a target fuel based on a preset sensor to obtain first fuel data; A data extraction unit: used for acquiring a first data type related to the real-time fuel state of the target fuel, and extracting fuel data matching the first data type from the first fuel data to obtain second fuel data; A first state unit: used for comparing the second fuel data with the fuel data in the fuel-state database, thereby extracting a first fuel state corresponding to each sub-fuel data in the second fuel data; Real-time status unit: used to obtain the real-time fuel status of the second fuel data based on the integration of the first fuel status corresponding to each sub-fuel data in the second fuel data, that is, the real-time fuel status of the target fuel.

3. A fuel monitoring and control system for industrial automation according to claim 2, characterized in that: The first prediction module includes: A scene parameter acquisition unit: used to extract the real-time scene parameters of the industrial automation scene corresponding to the real-time fuel state, and to acquire the historical scene parameters corresponding to each valid historical fuel data based on the preset sensor; A historical state extraction unit: used to compare the historical scene parameters with the real-time scene parameters, and extract the corresponding historical fuel data and the corresponding historical fuel state based on the similarity between the historical scene parameters and the real-time scene parameters; A historical state sorting unit: used to sort the historical fuel states based on the similarity between the scene parameters corresponding to the historical fuel states and the real-time scene parameters, to obtain a first ordered fuel state set; A state group set unit: used to obtain a historical fuel state corresponding to a next fuel acquisition cycle of each historical fuel state in the first ordered fuel state set, obtain a historical fuel state group, and obtain a historical fuel state group set based on each historical fuel state group; Initial trend unit: used to determine the state change trend of each historical fuel state group, and extract the state change trend corresponding to the first historical fuel state group whose corresponding historical scene parameters have the highest similarity with the real-time scene parameters as the initial state trend; The first trend unit is used to perform weighted correction on the initial state trend one by one based on the similarity between the historical scene parameters corresponding to each remaining historical fuel state group and the real-time scene parameters to obtain the first state trend; Among them, the lower the similarity, the lower the weight of the corresponding weighted correction; A trend prediction unit: used for predicting the fuel state change trend of the real-time fuel state based on the first state trend, so as to obtain the fuel state of the next fuel acquisition cycle of the real-time fuel state; Among them, the fuel state of the next fuel acquisition cycle of the real-time fuel state is the first prediction result.

4. A fuel monitoring and control system for industrial automation according to claim 3, characterized in that: The first trend unit includes: A first correction subunit: used for extracting the state change trend corresponding to the first historical fuel state group whose corresponding historical scene parameters of the remaining first historical fuel state groups have the highest similarity with the real-time scene parameters as the first correction trend; Weight conversion subunit: used to compare the similarity between the historical scene parameters and the real-time scene parameters corresponding to the first correction trend, and perform similarity-weight conversion; Among them, the higher the similarity, the higher the weight; Weighted correction subunit: used to perform weighted correction on the initial state trend by combining the first correction trend with the similarity-weight conversion result, until the state trend corresponding to each first historical fuel state group in the first historical fuel state group set is weightedly corrected to obtain the first state trend.

5. A fuel monitoring and control system for industrial automation according to claim 3, characterized in that: The strategy determination module includes: A state difference determination unit: used for comparing the first prediction result with the corresponding standard fuel state under the real-time scene parameters, so as to obtain a first difference between each sub-fuel state in the first prediction result and the sub-fuel state corresponding to the standard fuel state; Strategy mapping unit: mapping the state type of the first difference and the corresponding sub-fuel state with the difference of the corresponding state type in the difference-strategy mapping table, so as to obtain the fuel control strategy corresponding to each first difference; An initial strategy determination unit: used to obtain an initial fuel control strategy of a target fuel based on a fuel control strategy corresponding to each difference; Strategy optimization unit: used to determine whether there are fuel control strategies with repeated or conflicting strategies in the initial fuel control strategy, and to optimize the strategy to obtain the fuel control strategy of the target fuel.

6. A fuel monitoring and control system for industrial automation according to claim 5, characterized in that: Optimization and adjustment modules, including: Energy consumption determination unit: used for inputting the fuel control strategy based on the target fuel into the virtual machine, and performing simulation control in combination with the first fuel data, so as to obtain the first fuel energy consumption of the target fuel adjusted based on the fuel control strategy; Energy consumption comparison unit: used to obtain the maximum fuel energy consumption requirement in the current industrial automation scenario, and determine whether the first fuel energy consumption is greater than the maximum fuel energy consumption requirement; If the first fuel energy consumption is greater than the maximum fuel energy consumption requirement, the fuel control strategy is optimized to obtain a first optimization strategy, and the target fuel is adjusted based on the first optimization strategy; Otherwise, the target fuel is adjusted based on the fuel control strategy.

7. A fuel monitoring and control system for industrial automation according to claim 6, characterized in that: Energy consumption comparison unit, including: If the first fuel energy consumption is greater than the maximum fuel energy consumption requirement, then obtaining a fuel control strategy, and determining a second fuel energy consumption required for each sub-control strategy in the fuel control strategy; Sort each sub-control strategy in the fuel control strategy according to the second fuel energy consumption, and extract the remaining control strategies with the same difference from the corresponding difference-strategy mapping table based on the second fuel energy consumption from high to low to replace the current sub-control strategy; The fuel control strategy is optimized based on the strategy replacement result of the sub-control strategy to obtain a first optimization strategy, and the target fuel is adjusted based on the first optimization strategy.

8. A fuel monitoring and control system for industrial automation according to claim 6, characterized in that: Also includes: Status verification module, specifically including: An optimization state determination unit: used for acquiring first optimized fuel data corresponding to the target fuel after the target fuel is adjusted based on the first optimization strategy, and analyzing the target fuel based on the first optimized fuel data to obtain an optimized fuel state of the target fuel; Optimization state comparison unit: used to compare the optimized fuel state with the standard fuel state in the current industrial automation scenario; If a first difference between each sub-fuel state of the optimized fuel state and the corresponding sub-fuel state of the standard fuel state is less than a preset difference, it is determined that the state control of the target fuel is completed; Otherwise, the corresponding fuel control strategy is obtained based on the first difference between each sub-fuel state of the optimized fuel state and the sub-fuel state corresponding to the standard fuel state for readjustment.

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