Automatic control system for operation of electrical cabinet

Abstract

The invention, which belongs to the technical field of electrical cabinet control, discloses an electrical cabinet operation automation control system comprising an information management module, a strategy module, a monitoring module and a control module. The information management module is used for performing information management on each electrical cabinet and establishing an information base; identifying the information of each electrical cabinet in the information base, and establishing a control conversion unit of each electrical cabinet according to the information of each electrical cabinet; the strategy module is used for setting a control strategy by a user according to a management requirement and sending the control strategy to the control conversion unit in each electrical cabinet; the monitoring module is used for monitoring each electrical cabinet, establishing a display model, acquiring monitoring data of each electrical cabinet in real time, and inputting the monitoring data into the display model for real-time display; and the control module is used for controlling each electrical cabinet, identifying the monitoring data of each electrical cabinet in the display model in real time, acquiring a control implementation strategy of each electrical cabinet, and performing control processing on the electrical cabinet according to the control implementation strategy and the monitoring data.

Description

Technical Field

[0001] The invention belongs to the technical field of electrical cabinet control, in particular to an electrical cabinet operation automation control system. Background Art

[0002] In the field of industrial automation, electrical cabinets are core equipment for power distribution, control and protection. The stability and efficiency of their operating status directly affect the smooth operation of the entire production line. However, the current electrical cabinet operation automation control system has significant deficiencies in standardization, which greatly limits the compatibility and flexibility between systems and brings many challenges to the production management and maintenance of enterprises.

[0003] The lack of standardization leads to significant differences between electrical cabinets of different manufacturers and models. These differences are not only reflected in the physical aspects such as hardware interfaces and communication protocols, but also in the software aspects such as control logic and management strategies. This diversity makes it difficult for enterprises to adjust and control electrical cabinets in real time according to a unified management strategy, and the lack of standardization also seriously restricts the dynamic adjustment ability of management strategies. With the rapid development of industrial automation today, the production environment and management requirements are constantly changing, requiring the electrical cabinet operation automation control system to quickly adapt to these changes and make corresponding adjustments. However, due to the differences between different electrical cabinets, enterprises often cannot directly apply a unified management strategy for dynamic adjustment, but need to spend a lot of time and manpower to modify and test the configuration of each electrical cabinet one by one, which not only increases the operating costs of the enterprise, but may also lead to errors and delays in the adjustment process.

[0004] In addition, the existing methods also have obvious deficiencies in the evaluation of the operating status of the electrical cabinet and abnormal handling. When the electrical cabinet is in an abnormal state, the system often takes a direct approach, such as immediate shutdown, to avoid further damage. However, this approach ignores the importance of emergency handling and does not have the reaction time to carry out certain emergency handling.

[0005] Based on this, in order to solve the above problems or part of the problems, the present invention provides an electrical cabinet operation automation control system. Summary of the invention

[0006] In order to solve the problems existing in the above-mentioned solutions, the present invention provides an electrical cabinet operation automation control system.

[0007] The purpose of the present invention can be achieved by the following technical solutions:

[0008] An electrical cabinet operation automation control system, comprising an information management module, a strategy module, a monitoring module and a control module;

[0009] The information management module is used to manage the information of each electrical cabinet, establish an information base, obtain the information of each electrical cabinet in real time, and send the information of each electrical cabinet to the information base for storage;

[0010] Identify the information of each electrical cabinet in the information library, establish a control conversion unit for each electrical cabinet according to the information of each electrical cabinet, and the control conversion unit is used to convert the received control strategy into the control implementation strategy of the electrical cabinet; and set each control conversion unit in the electrical cabinet.

[0011] Furthermore, the method for establishing a control conversion unit of the electrical cabinet according to the electrical cabinet information includes:

[0012] Setting a strategy identification node, wherein the strategy identification node is used to receive and identify the control strategy;

[0013] The control material data of the electrical cabinet is obtained according to the electrical cabinet information; several simulation strategies are set, and control simulation is performed through each simulation strategy and each control material data to obtain control simulation data; a control conversion model is established according to each control simulation data, and the control conversion model is used to analyze the control strategy and obtain the corresponding control implementation strategy; a control conversion unit is established according to the strategy identification node and the control conversion model.

[0014] Further, the working method of controlling the conversion unit includes:

[0015] The control strategy is received and identified in real time through the strategy identification node, and the identified control strategy is sent to the control conversion model. The control conversion model analyzes the control strategy to obtain the corresponding control implementation strategy; and the control implementation strategy is output.

[0016] The strategy module is used for the user to set the control strategy according to the management requirements, and sends the control strategy to the control conversion unit in each electrical cabinet.

[0017] Furthermore, the control strategy set by the user is checked, and when the check passes, the control strategy is sent to the control conversion unit.

[0018] Furthermore, the method for verifying the control strategy includes:

[0019] Identify control strategies, simulate and transform control strategies, and obtain simulated implementation strategies for each electrical cabinet;

[0020] Set the calibration materials for each electrical cabinet; establish a calibration model based on each calibration material. The expression of the calibration model is:

[0021]

[0022] Where: (SC, MC) is the input data, which are the calibration material and the simulation implementation strategy respectively; the output data is the calibration evaluation value HM (SC, MC), which is 1 or 0;

[0023] By analyzing the calibration materials and simulation implementation strategies of each electrical cabinet in the calibration and evaluation model, corresponding calibration and evaluation values ​​are obtained; and the calibration results are determined based on the calibration and evaluation values.

[0024] The monitoring module is used to monitor each electrical cabinet, establish a display model, obtain monitoring data of each electrical cabinet in real time, and input the monitoring data into the display model for real-time display.

[0025] The control module is used to control each electrical cabinet, identify the monitoring data of each electrical cabinet in the display model in real time, obtain the control implementation strategy of each electrical cabinet, and control the electrical cabinet according to the control implementation strategy and the monitoring data.

[0026] Furthermore, it also includes a security analysis module and a security processing module;

[0027] The safety analysis module is used to perform real-time safety analysis on each electrical cabinet to obtain the safety analysis results of each electrical cabinet, the safety analysis results including safety abnormality and safety normality;

[0028] The security processing module is used to process when a security anomaly is identified, identify the security anomaly problem, and warn the user, generate a security impact curve based on the security anomaly problem, perform processing evaluation based on the security impact curve, and determine the processing time point; obtain the security processing measures corresponding to the security anomaly problem, and when the processing time point is reached, process it according to the security processing measures.

[0029] Furthermore, the method for generating the safety impact curve includes:

[0030] Obtain each potential problem of the electrical cabinet, obtain the historical record data of each potential problem, the historical record data includes impact status data, monitoring data, and condition data, perform impact assessment based on the historical record data, and generate a material impact curve, the horizontal axis of the material impact curve is time, and the vertical axis is the impact value; the value range of the impact value is [0, 100];

[0031] Set training data based on historical record data and corresponding material impact curves; establish a curve generation model through training data;

[0032] The curve condition data corresponding to the safety anomaly problem is collected in real time, and the safety anomaly model and curve condition data are analyzed through the curve generation model to obtain the safety impact curve.

[0033] Furthermore, the method of processing assessment based on the safety impact curve includes:

[0034] Obtain electrical cabinet information and safety handling measures corresponding to safety anomalies; estimate losses based on safety handling measures and electrical cabinet information to obtain a loss prediction curve, where the horizontal axis of the loss prediction curve is time and the vertical axis is predicted loss;

[0035] Obtain the safety impact curve, fit the loss prediction curve and the safety impact curve respectively, obtain the loss prediction function and the safety impact function, determine the critical point according to the loss prediction function and the safety impact function, and determine the processing time point according to the critical point.

[0036] Furthermore, the method for determining the critical point includes:

[0037] Set the critical standard; establish the critical identification model according to the critical standard, and the expression of the critical identification model is:

[0038]

[0039] Where: [SH(T), AH(t)] is the input data, SH(T), AH(t) are the loss prediction function and the safety impact function respectively, and t is the time; the output data is the critical judgment value PL[SH(T), AH(t)]; the critical judgment value is 1 or 0;

[0040] The loss prediction function and the safety impact function are analyzed through the critical identification model to obtain the critical judgment value corresponding to each time; the critical point is determined according to the critical judgment value.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] Through the cooperation between the information management module, strategy module, monitoring module, control module, safety analysis module and safety processing module, the differences between electrical cabinets of different manufacturers and models are eliminated, so that these devices can follow the same management strategy, greatly improving the compatibility and interoperability of the system; improving the production management efficiency of the enterprise, shortening the adjustment cycle, and reducing the risk of human error; when an abnormal state is detected, the emergency processing process is triggered first, providing operators with valuable reaction time for preliminary diagnosis and intervention. At the same time, the abnormal data is analyzed through intelligent algorithms to determine the best processing time, and the operator is automatically or assisted to perform the corresponding processing measures to minimize production interruption time and losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] 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.

[0044] Figure 1 This is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0045] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only 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.

[0046] like Figure 1 As shown, an electrical cabinet operation automation control system includes an information management module, a strategy module, a monitoring module, a control module, a safety analysis module and a safety processing module;

[0047] The information management module is used to manage the information of each electrical cabinet, establish an information database, and obtain the information of each electrical cabinet that needs to be managed in a unified manner in real time. The electrical cabinet information includes various related information such as brand, model, component information, working mode, and scope of responsibility. The scope of responsibility refers to the various equipment it controls and protects; the information of each electrical cabinet is sent to the information database for storage;

[0048] Identify the information of each electrical cabinet in the information library, and establish a control conversion unit for each electrical cabinet based on the information of each electrical cabinet, wherein the control conversion unit is used to convert the received control strategy into the control strategy applied by the electrical cabinet, and mark the applied control strategy as the control implementation strategy; set each control conversion unit in the corresponding electrical cabinet; that is, make corresponding equipment adjustments so that the control conversion unit can work normally in the electrical cabinet.

[0049] In one embodiment, a method for establishing a control conversion unit according to electrical cabinet information includes:

[0050] Based on the existing control strategy recognition and understanding technology, a corresponding strategy recognition node is set, that is, the strategy recognition node is used to identify the control strategy and understand its real control requirements; subsequently, the control strategy is received and recognized through the strategy recognition node;

[0051] According to the information of the electrical cabinet, a large amount of historical control data of the electrical cabinet is obtained and marked as control material data; several possible control strategies are set and marked as simulation strategies, and control simulation is performed through each simulation strategy and each control material data, that is, the simulation strategy and each control material data are used to analyze what kind of control method the simulation strategy corresponds to in real time on the electrical cabinet, and the control simulation data are integrated, that is, the control simulation data is used to represent the actual corresponding control method of the corresponding simulation strategy on the electrical cabinet; a control conversion model is established according to each control simulation data, and the control conversion model can be established based on a neural network such as a CNN network or a DNN network, and trained through control simulation data, and analyzed through the control conversion model after successful training, so as to realize subsequent conversion according to the received and identified control strategy to form a control implementation strategy corresponding to the electrical cabinet; the control conversion model can also be established based on other intelligent technologies;

[0052] A control conversion unit is established based on the strategy identification node and the control conversion model; it is established specifically in combination with existing technologies and electrical cabinet information to achieve adaptation with the electrical cabinet.

[0053] The strategy module is used for the user to set the corresponding control strategy according to the management requirements, and sends the set control strategy to the control conversion unit in each electrical cabinet.

[0054] In one embodiment, due to problems such as user professionalism and incomplete consideration, the control strategy set by the user may not meet the corresponding management requirements in actual application. Therefore, in order to ensure safe and normal control of the electrical cabinet, the control strategy set by the user is checked and sent to each control conversion unit after passing the check.

[0055] In one embodiment, the control strategy is verified by using existing technology.

[0056] In one embodiment, a method for verifying a control strategy includes:

[0057] Identify the control strategy, simulate and transform the control strategy, and obtain the simulated implementation strategy of each electrical cabinet, that is, transform the control strategy using the corresponding transformation method to form the corresponding control implementation strategy, which is marked as the simulated implementation strategy;

[0058] According to the historical operation data of each electrical cabinet, corresponding verification materials are set for simulation verification; according to each verification material, a corresponding verification model is established. The verification model is used to simulate the corresponding simulation implementation strategy according to the verification material to determine whether there are any situations that do not meet the preset management requirements during the simulation process. If so, it is considered that the verification is unqualified; if the whole process meets the requirements, it is considered that the verification is qualified; that is, the verification material and the simulation implementation strategy are used for simulation management to determine whether the management requirements are met; the expression of the verification model is Where: (SC, MC) is the input data, which are the calibration material and the simulation implementation strategy respectively; the output data is the calibration evaluation value HM (SC, MC), which is 1 or 0;

[0059] By analyzing the calibration materials and simulation implementation strategies of each electrical cabinet in the calibration and evaluation model, the corresponding calibration and evaluation values ​​are obtained; the calibration results are determined based on each calibration and evaluation value.

[0060] The monitoring module is used to monitor each electrical cabinet, establish a display model according to user needs, obtain monitoring data of each electrical cabinet in real time, and input the monitoring data into the display model for real-time display.

[0061] The control module is used to control each electrical cabinet, identify the monitoring data of each electrical cabinet in the display model in real time, obtain the control implementation strategy of each electrical cabinet, and control and process the corresponding monitoring data according to the obtained control implementation strategy.

[0062] The safety analysis module is used to perform real-time safety analysis on each electrical cabinet to obtain safety analysis results of each electrical cabinet. The safety analysis results include safety abnormality and safety normality.

[0063] In one embodiment, a safety analysis may be performed on each electrical cabinet based on existing technologies.

[0064] The security processing module is used to process when a security anomaly is identified, identify the security anomaly problem, and warn the user, generate a security impact curve based on the security anomaly problem, perform processing evaluation based on the security impact curve, and determine the processing time point; obtain the preset security processing measures corresponding to the security anomaly problem, or the user directly adjusts and sets the security processing measures, and when the processing time point is reached, the processing is performed according to the security processing measures.

[0065] The methods for generating safety impact curves include:

[0066] Obtain various safety anomalies that the electrical cabinet may have, mark them as potential problems, and obtain historical record data of each potential problem, mainly about the impact status data, monitoring data and corresponding condition data after the potential problem occurs, such as the change in the impact status on the operation of the electrical cabinet over time from the beginning of the occurrence; the condition data is the corresponding working condition data of the electrical cabinet, because the impact status of the same safety problem under different conditions is different; perform an impact assessment based on the historical record data, obtain the impact value that changes with time under the corresponding condition data, and generate a material impact curve, with the horizontal axis being time and the vertical axis being the impact value; the impact value is set according to the degree of impact on the work of the electrical cabinet, and the value range is [0, 100], 0 means no impact, and 100 means that the electrical cabinet is damaged and cannot be used, which is the most serious situation; set each impact value manually The corresponding reference impact is subsequently matched according to the reference impact to obtain the corresponding impact value; the corresponding impact value can also be determined by intelligent analysis based on existing intelligent technology; the corresponding training data is set according to the historical record data and the corresponding material impact curve; the curve generation model is established through the training data, which is used to estimate the corresponding safety impact curve according to the safety anomaly problem and the curve condition data. The curve condition data is the various data corresponding to the safety anomaly problem, such as monitoring data and condition data; that is, the corresponding curve is generated according to the actual data that has occurred, and then the above curve is combined for subsequent prediction to finally form a safety impact curve; specifically, the curve generation model can be established based on CNN network or DNN network, and the training set is set based on the training data for training. After the training is successful, analysis is performed, and the curve generation model can also be established through other intelligent technologies;

[0067] The curve condition data corresponding to the safety anomaly problem is collected in real time, and the safety anomaly model and curve condition data are analyzed through the curve generation model to obtain the safety impact curve.

[0068] Methods for treatment assessment based on safety impact curves include:

[0069] Obtain electrical cabinet information and safety handling measures corresponding to safety anomalies; make loss predictions based on safety handling measures and electrical cabinet information, and obtain a loss prediction curve with time on the horizontal axis and predicted loss on the vertical axis; that is, estimate the economic losses that may be incurred if safety handling measures are followed at subsequent times, and consider losses from the user's perspective. For example, if a certain production equipment cannot be shut down suddenly, and if the electrical cabinet stops running directly, corresponding losses will be incurred. The corresponding loss conversion will be carried out based on the economic losses; and after the early warning notification, make loss predictions based on time, because when the early warning is received, corresponding emergency measures will be taken to gradually reduce the losses; specifically, a corresponding loss prediction model can be established based on a neural network such as a CNN network or a DNN network, and a corresponding training set can be established manually for training, and the loss prediction model after successful training can be analyzed; other existing technologies can also be used for estimation.

[0070] The loss prediction curve and the safety impact curve are fitted respectively to obtain the corresponding loss prediction function and safety impact function. The critical point is determined according to the loss prediction function and the safety impact function. In one case, the loss is low and the loss can be ignored. The impact value is directly used as the standard. When the impact value reaches a certain preset value, it is regarded as reaching the critical point, that is, the time corresponding to the corresponding preset value. In another case, the loss is high and the loss needs to be considered. It is determined according to the proportion of the impact value and the loss. If the loss is particularly high, after exceeding the preset value, the loss still exceeds the corresponding standard, the critical point can be set after the preset value. When the loss is reduced to the requirement, it is directly regarded as reaching the critical point. In special cases, it can be directly handled in the way of damaging the electrical cabinet. In order to free up emergency time as much as possible, the loss can be reduced to a level higher than the loss caused by the damage to the electrical cabinet, but it is generally handled before the damage. In summary, the preset values ​​corresponding to the loss and the impact value are set according to user needs, and then compared and judged according to the corresponding preset values ​​and integrated into the critical standard. In one embodiment, a critical identification model is established according to the critical standard. The critical identification model is used to identify and analyze the loss prediction function and the safety impact function according to the critical standard to determine the critical point. The expression of the critical identification model is Where: [SH(T), AH(t)] is the input data, SH(T), AH(t) are the loss prediction function and the safety impact function respectively, and t is the time; the output data is the critical judgment value PL[SH(T), AH(t)]; t satisfies the critical standard, that is, it satisfies the critical standard at time t; the critical point is determined according to the critical judgment value.

[0071] Determine the processing time point based on the critical point.

[0072] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0073] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. An electrical cabinet operation automation control system, characterized in that: It includes information management module, strategy module, monitoring module and control module; The information management module is used to manage the information of each electrical cabinet, establish an information base, obtain the information of each electrical cabinet in real time, and send the information of each electrical cabinet to the information base for storage; Identify the information of each electrical cabinet in the information library, establish a control conversion unit for each electrical cabinet according to the information of each electrical cabinet, and the control conversion unit is used to convert the received control strategy into the control implementation strategy of the electrical cabinet; and set each control conversion unit in the electrical cabinet; The strategy module is used for the user to set the control strategy according to the management requirements, and send the control strategy to the control conversion unit in each electrical cabinet; The monitoring module is used to monitor each electrical cabinet, establish a display model, obtain monitoring data of each electrical cabinet in real time, and input the monitoring data into the display model for real-time display; The control module is used to control each electrical cabinet, identify the monitoring data of each electrical cabinet in the display model in real time, obtain the control implementation strategy of each electrical cabinet, and control the electrical cabinet according to the control implementation strategy and the monitoring data.

2. The electrical cabinet operation automation control system according to claim 1 is characterized in that: The method for establishing a control conversion unit of an electrical cabinet according to electrical cabinet information includes: Setting a strategy identification node, wherein the strategy identification node is used to receive and identify the control strategy; The control material data of the electrical cabinet is obtained according to the electrical cabinet information; several simulation strategies are set, and control simulation is performed through each simulation strategy and each control material data to obtain control simulation data; a control conversion model is established according to each control simulation data, and the control conversion model is used to analyze the control strategy and obtain the corresponding control implementation strategy; a control conversion unit is established according to the strategy identification node and the control conversion model.

3. The electrical cabinet operation automation control system according to claim 2 is characterized in that: The method for controlling the operation of the conversion unit includes: The control strategy is received and identified in real time through the strategy identification node, and the identified control strategy is sent to the control conversion model. The control conversion model analyzes the control strategy to obtain the corresponding control implementation strategy; and the control implementation strategy is output.

4. The electrical cabinet operation automation control system according to claim 1, characterized in that: The control strategy set by the user is checked, and when the check passes, the control strategy is sent to the control conversion unit.

5. The electrical cabinet operation automation control system according to claim 4 is characterized in that: Methods for verifying the control strategy include: Identify control strategies, simulate and transform control strategies, and obtain simulated implementation strategies for each electrical cabinet; Set the calibration materials for each electrical cabinet; establish a calibration model based on each calibration material. The expression of the calibration model is: Where: (SC, MC) is the input data, which are the calibration material and the simulation implementation strategy respectively; the output data is the calibration evaluation value HM (SC, MC), which is 1 or 0; By analyzing the calibration materials and simulation implementation strategies of each electrical cabinet in the calibration and evaluation model, corresponding calibration and evaluation values ​​are obtained; and the calibration results are determined based on the calibration and evaluation values.

6. The electrical cabinet operation automation control system according to claim 1, characterized in that: It also includes a security analysis module and a security processing module; The safety analysis module is used to perform real-time safety analysis on each electrical cabinet to obtain the safety analysis results of each electrical cabinet, the safety analysis results including safety abnormality and safety normality; The security processing module is used to process when a security anomaly is identified, identify the security anomaly problem, and warn the user, generate a security impact curve according to the security anomaly problem, perform processing evaluation according to the security impact curve, and determine the processing time point; Obtain the security handling measures corresponding to the security anomaly problem, and when the processing time point is reached, handle it according to the security handling measures.

7. An electrical cabinet operation automation control system according to claim 6, characterized in that: The methods for generating safety impact curves include: Obtain each potential problem of the electrical cabinet, obtain the historical record data of each potential problem, the historical record data includes impact status data, monitoring data, and condition data, perform impact assessment based on the historical record data, and generate a material impact curve, the horizontal axis of the material impact curve is time, and the vertical axis is the impact value; the value range of the impact value is [0, 100]; Set training data based on historical record data and corresponding material impact curves; establish a curve generation model through training data; The curve condition data corresponding to the safety anomaly problem is collected in real time, and the safety anomaly model and curve condition data are analyzed through the curve generation model to obtain the safety impact curve.

8. An electrical cabinet operation automation control system according to claim 7, characterized in that: Methods for treatment assessment based on safety impact curves include: Obtain electrical cabinet information and safety handling measures corresponding to safety anomalies; estimate losses based on safety handling measures and electrical cabinet information to obtain a loss prediction curve, where the horizontal axis of the loss prediction curve is time and the vertical axis is predicted loss; Obtain the safety impact curve, fit the loss prediction curve and the safety impact curve respectively, obtain the loss prediction function and the safety impact function, determine the critical point according to the loss prediction function and the safety impact function, and determine the processing time point according to the critical point.

9. An electrical cabinet operation automation control system according to claim 8, characterized in that: Methods for determining critical points include: Set the critical standard; establish the critical identification model according to the critical standard, and the expression of the critical identification model is: Where: [SH(T), AH(t)] is the input data, SH(T), AH(t) are the loss prediction function and safety impact function respectively, t is the time; the output data is the critical judgment value PL[SH(T), AH(t)]; The critical judgment value is 1 or 0; The loss prediction function and the safety impact function are analyzed through the critical identification model to obtain the critical judgment value corresponding to each time; the critical point is determined according to the critical judgment value.

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