Low-voltage cabinet operation regulation method and system based on energy efficiency optimization

Abstract

This invention discloses a method and system for low-voltage switchgear operation control based on energy efficiency optimization, relating to the field of low-voltage switchgear control technology. The method includes: acquiring real-time operating data of equipment within the low-voltage switchgear and determining the target control action for the next control moment; calculating the expected lifespan loss cost of the equipment under the target control action; simulating the expected energy efficiency improvement effect after executing the target control action and calculating the corresponding energy efficiency benefit; assessing the safety risk of executing the target control action and quantifying the safety risk cost; and calculating the net benefit value of executing the target control action based on the energy efficiency benefit, lifespan loss cost, and safety risk cost, comparing it with a preset decision threshold, and making a decision on the target control action. This invention solves the technical problem in existing technologies where low-voltage switchgear control decisions only focus on energy efficiency while ignoring equipment lifespan loss and operational safety risks, achieving the technical effect of optimizing low-voltage switchgear operation energy efficiency while ensuring equipment operation safety and extending equipment lifespan.

Description

Technical Field

[0001] This invention relates to the field of low-voltage switchgear control technology, specifically to a low-voltage switchgear operation control method and system based on energy efficiency optimization. Background Technology

[0002] Low-voltage switchgear plays a crucial role in power distribution, load control, and reactive power compensation. During operation, it typically monitors parameters such as load changes, power factor, and energy efficiency, and executes corresponding control actions to improve energy utilization efficiency. In these control processes, some strategies primarily adjust equipment operating conditions based on energy efficiency indicators, such as frequently switching capacitors or performing switching operations to improve the power factor. However, when making control decisions, insufficient consideration is often given to factors affecting equipment lifespan, such as electrical wear of switchgear contacts and mechanical fatigue of operating mechanisms. Furthermore, a comprehensive assessment is lacking of operational risks such as overvoltage, harmonic amplification, and transient impacts that may result from control actions. This leads to increased equipment operation frequency, accelerated equipment wear, and decreased operational safety. Summary of the Invention

[0003] This application provides a method and system for the operation and control of low-voltage switchgear based on energy efficiency optimization, which is used to address the technical problem that in the prior art, the control decision of low-voltage switchgear only focuses on energy efficiency and ignores the equipment life loss and operational safety risks.

[0004] In view of the above problems, this application provides a method and system for low-voltage switchgear operation control based on energy efficiency optimization.

[0005] The first aspect of this application provides a method for controlling the operation of a low-voltage switchgear based on energy efficiency optimization, the method comprising: The system acquires real-time operating data of the equipment within the low-voltage switchgear to determine the target control action for the next control moment; calculates the expected lifespan loss cost of the equipment under the target control action; simulates the expected energy efficiency improvement effect after executing the target control action and calculates the corresponding energy efficiency benefit; assesses the safety risk of executing the target control action and quantifies the safety risk cost; based on the energy efficiency benefit, the lifespan loss cost, and the safety risk cost, calculates the net benefit value of executing the target control action and compares it with a preset decision threshold to make a decision on the target control action.

[0006] A second aspect of this application provides a low-voltage switchgear operation control system based on energy efficiency optimization, the system comprising: The control action determination module is used to acquire real-time operating data of the equipment in the low-voltage switchgear and determine the target control action at the next control moment; the loss cost calculation module is used to calculate the expected life loss cost of the equipment under the target control action; the energy efficiency benefit calculation module is used to simulate the expected energy efficiency improvement effect after executing the target control action and calculate the corresponding energy efficiency benefit; the safety risk quantification module is used to assess the safety risk of executing the target control action and quantify the safety risk cost; the decision module is used to calculate the net benefit value of executing the target control action based on the energy efficiency benefit, the life loss cost and the safety risk cost, and compare it with a preset decision threshold to make a decision on the target control action.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application acquires real-time operating data of equipment within a low-voltage switchgear to determine the target control action at the next control moment; calculates the expected lifespan loss cost of the equipment under the target control action; simulates the expected energy efficiency improvement effect after executing the target control action and calculates the corresponding energy efficiency benefit; assesses the safety risk of executing the target control action and quantifies the safety risk cost; based on the energy efficiency benefit, the lifespan loss cost, and the safety risk cost, calculates the net benefit value of executing the target control action and compares it with a preset decision threshold to make a decision on the target control action. This invention solves the technical problem in existing low-voltage switchgear control decisions that only focus on energy efficiency while ignoring equipment lifespan loss and operational safety risks. By comprehensively evaluating the energy efficiency benefit, lifespan loss cost, and safety risk cost of the target control action and calculating the net benefit value for decision control, it achieves the technical effect of optimizing the energy efficiency of low-voltage switchgear operation while ensuring equipment operational safety and extending equipment lifespan. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic diagram of the low-voltage switchgear operation control method based on energy efficiency optimization provided in the embodiments of this application; Figure 2 A schematic diagram of the structure of a low-voltage switchgear operation control system based on energy efficiency optimization provided in this application embodiment.

[0010] Figure labeling: Module 11 for determining control action, Module 12 for calculating loss cost, Module 13 for calculating energy efficiency benefit, Module 14 for quantifying safety risk, and Module 15 for decision-making. Detailed Implementation

[0011] This application provides a method and system for the operation and control of low-voltage switchgear based on energy efficiency optimization. It addresses the technical problem in existing technologies where low-voltage switchgear control decisions only focus on energy efficiency while ignoring equipment lifespan loss and operational safety risks. By comprehensively evaluating the energy efficiency benefits, lifespan loss costs, and safety risk costs of the target control actions and calculating the net benefit value for decision control, it achieves the technical effect of optimizing the energy efficiency of low-voltage switchgear operation while ensuring equipment operational safety and extending equipment lifespan.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0014] Example 1, as Figure 1 As shown, this application provides a low-voltage switchgear operation control method based on energy efficiency optimization, the method comprising: Step S100: Obtain real-time operating data of the equipment in the low-voltage switchgear and determine the target control action for the next control moment.

[0015] In this embodiment, a pre-installed intelligent control module in the low-voltage switchgear is connected to acquire real-time operating data and obtain control instructions for the next control moment generated based on the real-time operating data. The control instructions are then parsed to obtain the target control action for the next control moment.

[0016] Furthermore, the method provided in the application embodiment, in order to obtain real-time operating data of the equipment inside the low-voltage switchgear and determine the target control action at the next control moment, further includes: The system connects to the pre-set intelligent control module of the low-voltage switchgear, acquires the real-time operating data and the control command generated for the next control moment based on the real-time operating data, and parses the control command to obtain the target control action.

[0017] In this embodiment, low-voltage switchgear operation information is obtained by establishing a data communication connection with a pre-installed intelligent control module. The intelligent control module is an intelligent control module currently installed inside the low-voltage switchgear, used to monitor and control the operating status of the electrical equipment inside the switchgear. Specifically, the monitoring devices inside the low-voltage switchgear are connected to the intelligent control module via an industrial communication interface, and a data acquisition channel is established based on the Modbus communication protocol. The intelligent control module collects data from each monitoring node according to a set sampling period, thereby forming real-time operating data of the equipment inside the low-voltage switchgear. The real-time operating data is a data set reflecting the current operating status of the low-voltage switchgear, including at least electrical parameters such as the effective value of the bus voltage, the current of each branch, active power, reactive power, power factor, and equipment temperature. The collected real-time operating data is stored through a data caching mechanism to obtain a continuous sequence of equipment operating data.

[0018] After acquiring real-time operating data, the intelligent control module analyzes and processes the data based on a preset control strategy. Specifically, it uses a threshold-based method to identify the power factor parameter's status, comparing the power factor in the real-time operating data with a preset power factor threshold. When the power factor falls below the threshold, the control logic is triggered, and a control instruction for the next control moment is generated according to the preset control strategy. This control instruction is a data structure describing the equipment operation, containing parameters such as the target equipment identifier, operation type, and execution time, representing the equipment operation information that the low-voltage switchgear needs to perform at the next control moment.

[0019] After generating the control command, the specific equipment operation content is determined by structured parsing of the control command. Specifically, the control command is encapsulated in a preset command data format and stored in the intelligent control module. This command data format includes a command header field, a target device identifier field, an operation type field, and an execution time field. The command data frame of the control command is read using a command parsing method. The command header field is identified to determine the command type, and the target device identifier field, operation type field, and execution time field are extracted sequentially according to a preset field order, thus completing the parsing of the control command data structure. After completing the data structure parsing of the control command, the target device identifier field and operation type field are numerically interpreted using a field decoding method. Specifically, the target device identifier field is decoded according to a preset device encoding table to determine the corresponding electrical equipment type, and the equipment operation type is interpreted based on the encoded value of the operation type field, thereby obtaining the specific equipment control operation content. Through the above field decoding process, the coded data in the control command is converted into clear equipment operation information, thereby determining the target control action to be executed at the next control moment. The target control action is the specific control operation that the relevant electrical equipment in the low-voltage cabinet needs to perform at the next control moment, including capacitor bank switching operation, circuit breaker closing and opening operation, and load switching operation.

[0020] Step S200: Calculate the expected lifespan loss cost of the equipment under the target control action.

[0021] In this embodiment of the application, when calculating the expected life loss cost of the equipment under the target control action, the sub-equipment involved in the target control action is identified. Based on real-time operating data, a cumulative contact electrical wear model and a mechanical fatigue model of the operating mechanism are constructed for the sub-equipment. The breaking current value corresponding to the target control action is obtained according to the real-time operating data. The breaking current value is substituted into the cumulative contact electrical wear model to calculate the electrical life percentage. At the same time, the mechanical action corresponding to the target control action is substituted into the mechanical fatigue model of the operating mechanism to calculate the mechanical life percentage. Then, the electrical life percentage and the mechanical life percentage are weighted and calculated to generate the expected life loss cost.

[0022] Furthermore, in the method provided in the application embodiments, calculating the expected lifespan loss cost of the equipment under the target control action further includes: Identify the sub-devices involved in the target control action, and construct a cumulative contact electrical wear model and an operating mechanism mechanical fatigue model for the sub-devices based on the real-time operating data; obtain the expected breaking current value of the target control action based on the real-time operating data, substitute it into the cumulative contact electrical wear model, and calculate the percentage of electrical lifetime that the target control action will consume; based on the mechanical action that the target control action will perform, substitute it into the operating mechanism mechanical fatigue model, and calculate the percentage of mechanical lifetime that will be consumed; weight the percentage of electrical lifetime and the percentage of mechanical lifetime to generate the expected lifetime loss cost.

[0023] In this embodiment of the application, the sub-devices involved in the target control action are first identified, and the model parameters of the sub-devices are retrieved from the local storage. The model parameters include the material electrical wear coefficient and the maximum allowable number of mechanical operations. A cumulative contact electrical wear model is constructed based on the material electrical wear coefficient, and a mechanical fatigue model of the operating mechanism is constructed based on the maximum allowable number of mechanical operations.

[0024] Next, the expected breaking current value for the target control action is obtained based on real-time operating data. Specifically, the operating current of each branch of the low-voltage switchgear is continuously sampled using current sensors to form real-time operating data. The current sampling sequence corresponding to the branch of the target control action is extracted from the real-time operating data, and the root mean square (RMS) calculation is performed on the current sampling sequence to obtain the branch operating current. When the target control action corresponds to a circuit breaker tripping operation or a capacitor bank switching operation, the equipment contacts need to break the current operating current of the current branch at the moment of action. Therefore, the branch operating current is determined as the breaking current value corresponding to the target control action. Subsequently, the breaking current value is substituted into the contact electrical wear accumulation model for calculation. The material electrical wear coefficient corresponding to this sub-equipment is read from the equipment parameter database, and then the breaking current value and the material electrical wear coefficient are multiplied to obtain the contact electrical wear amount generated by this breaking action. Subsequently, the rated electrical life parameter of the sub-device is read from the equipment parameter database. The rated electrical life parameter is used to represent the maximum cumulative wear capacity that the equipment contacts can withstand under rated conditions. Then, the ratio of the current contact electrical wear amount to the total allowable wear amount corresponding to the rated electrical life is calculated, that is, the current contact electrical wear amount is divided by the maximum cumulative wear amount corresponding to the rated electrical life, so as to obtain the electrical life percentage corresponding to the current target control action. The electrical life percentage is used to represent the proportion of contact electrical life consumed by the current switching operation.

[0025] After obtaining the electrical life percentage, the mechanical operation behavior that the equipment needs to perform is determined based on the target control action. Specifically, the mechanical movement that the equipment operating mechanism needs to perform is identified by reading the equipment operation type in the target control action. When the target control action corresponds to a circuit breaker closing operation or a circuit breaker opening operation, the equipment operating mechanism will complete a complete mechanical drive process, and this operation is recorded as one mechanical action. Subsequently, the number of mechanical actions is substituted into the mechanical fatigue model of the operating mechanism for calculation, and the number of mechanical actions is proportionally converted to the maximum allowable number of mechanical operations of the equipment to obtain the mechanical life percentage corresponding to this target control action. The mechanical life percentage is used to represent the proportion of the mechanical life consumed by this mechanical action.

[0026] After obtaining the electrical life percentage and mechanical life percentage, the two types of lifespan consumption are comprehensively calculated to generate the expected lifespan loss cost. Specifically, predetermined weighting coefficients are assigned to the electrical life percentage and mechanical life percentage respectively. The electrical life percentage is multiplied by its corresponding weighting coefficient to obtain the electrical lifespan loss value, and the mechanical life percentage is multiplied by its corresponding weighting coefficient to obtain the mechanical lifespan loss value. Finally, the electrical lifespan loss value and the mechanical lifespan loss value are summed to obtain the expected lifespan loss cost, which reflects the impact of the target control action on the overall lifespan of the equipment.

[0027] Furthermore, in the method provided in the application embodiments, identifying the sub-devices involved in the target control action and constructing a cumulative contact electrical wear model and an operating mechanism mechanical fatigue model for the sub-devices based on the real-time operating data further includes: Identify the sub-devices involved in the target control action, and retrieve the model parameters of the sub-devices from local storage. The model parameters include at least the material electrical wear coefficient and the maximum permissible number of mechanical operations. Based on the material electrical wear coefficient and the maximum permissible number of mechanical operations, construct the cumulative electrical wear model of the contact and the mechanical fatigue model of the operating mechanism.

[0028] In this embodiment, the target control action is first identified by device object recognition to determine the sub-devices involved. Specifically, the target device identification information in the target control action is read and matched with a pre-established device address table for the low-voltage switchgear. The device address table records the device number, device type, and installation location of each electrical device inside the low-voltage switchgear. By retrieving the device number corresponding to the target device identification information from the device address table, the device object corresponding to the target control action is determined, and the sub-devices involved in the target control action are identified based on the device number. The sub-devices are the electrical devices inside the low-voltage switchgear that participate in performing the control action, including circuit breakers, capacitor banks, and load transfer switches.

[0029] After identifying the sub-device, the system accesses the device parameter database in local storage based on the sub-device's device number and retrieves the sub-device's model parameters from the database. Local storage stores the factory parameter information of each electrical device within the low-voltage switchgear, while the device parameter database records the technical parameters corresponding to different device models. The system retrieves the model parameters corresponding to the sub-device by searching the device parameter database using the device number or model number as an index. These model parameters include at least the material electrical abrasion coefficient and the maximum permissible number of mechanical operations. The material electrical abrasion coefficient characterizes the degree of wear on the device's contact material under the action of breaking current, and the maximum permissible number of mechanical operations characterizes the upper limit of the number of mechanical actions the device's operating mechanism can withstand under rated operating conditions.

[0030] After obtaining the model parameters, a cumulative contact electrical wear model is established based on the material electrical wear coefficient. Specifically, the material electrical wear coefficient is used as the basic parameter for calculating contact electrical wear. The corresponding breaking current value is obtained when the equipment performs an opening and closing operation. Then, the breaking current value is multiplied by the material electrical wear coefficient to obtain the contact electrical wear amount generated by that opening and closing operation. The material electrical wear coefficient characterizes the wear sensitivity of the contact material under the action of an electric arc, and the breaking current value characterizes the current amplitude passing through the contact at the moment of opening. After obtaining the contact electrical wear amount generated by a single opening and closing operation through the above calculation, the contact electrical wear amount generated by each opening and closing operation is recorded and accumulated according to the time sequence of the equipment's opening and closing operations. This forms a cumulative contact electrical wear model that describes the gradual accumulation and change of contact wear with the number of operations, reflecting the changes in electrical wear generated by the equipment contacts during multiple opening and closing operations.

[0031] After establishing the cumulative contact wear model, a mechanical fatigue model for the operating mechanism is established based on the maximum permissible number of mechanical operations. Specifically, the maximum permissible number of mechanical operations is used as the upper limit of the equipment's mechanical life. The number of mechanical actions is recorded each time the equipment performs a closing or opening operation. By continuously statistically analyzing the cumulative number of mechanical actions performed by the equipment and correlating the cumulative number of mechanical actions with the maximum permissible number of mechanical operations, a mechanical fatigue model for the operating mechanism is formed to describe the cumulative mechanical fatigue process of the equipment, reflecting the changes in mechanical fatigue of the operating mechanism during multiple mechanical operations.

[0032] Step S300: Simulate the expected energy efficiency improvement effect after executing the target control action, and calculate the corresponding energy efficiency benefit.

[0033] In this embodiment, when simulating the expected energy efficiency improvement effect after executing the target control action and calculating the corresponding energy efficiency benefits, the current active load, real-time electricity price, and current power factor value are obtained based on real-time operating data. On this basis, the target control action is simulated, and twin modeling is performed according to the topology and impedance parameters of the low-voltage switchgear to calculate the expected power factor after control, thereby obtaining the power factor improvement ratio and the expected stable operating time. Subsequently, the expected stable operating time improvement ratio after the target control action takes effect is calculated by combining historical load data and the expected stable operating time, and the power factor improvement ratio and the expected stable operating time improvement ratio are weighted and calculated to generate energy efficiency benefits.

[0034] Furthermore, the method provided in the application embodiments, which simulates the expected energy efficiency improvement effect after executing the target control action and calculates the corresponding energy efficiency gains, further includes: Based on the real-time operating data, the current active load and real-time electricity price are obtained, and the current power factor value is read; the target control action is simulated and executed, and twin modeling is performed based on the topology and impedance parameters of the low-voltage switchgear to calculate the expected power factor after control, obtain the power factor improvement ratio and the expected stable operating time; based on historical load data and the expected stable operating time, the expected stable operating time improvement ratio after the target control action takes effect is calculated; the power factor improvement ratio and the expected stable operating time improvement ratio are weighted to generate the energy efficiency benefit.

[0035] In this embodiment, the current active load and real-time electricity price are first obtained based on real-time operating data, and the current power factor value is read. Specifically, based on the real-time operating data obtained by the intelligent control module, the active power value at the current moment is read from the real-time operating data, and this active power value is determined as the current active load; according to the timestamp information corresponding to the current moment, the electricity price value corresponding to the current time period is retrieved from the pre-stored time-of-use electricity price data table, and this electricity price value is determined as the real-time electricity price; simultaneously, the current active power and current apparent power are read from the real-time operating data, and the current power factor value is obtained by dividing the current active power by the current apparent power.

[0036] Next, the target control action is simulated. A digital twin model is created based on the low-voltage switchgear's topology and impedance parameters to calculate the expected power factor after control. Specifically, the low-voltage switchgear's topology is established based on the connections between the internal busbars, branch feeders, load equipment, and reactive power compensation equipment. The conductor impedance, equivalent load impedance, and compensation branch impedance of each branch are configured into the corresponding branch of the topology, forming a digital twin model. Then, the target control action is simulated within the digital twin model. Specifically, the operating status in the digital twin model is updated according to the equipment operation content corresponding to the target control action. When the target control action is a capacitor bank switching operation, the connection status of the corresponding compensation branch is updated; when the target control action is a circuit breaker closing / opening operation, the conduction status of the corresponding branch is updated; and when the target control action is a load switching operation, the connection status of the corresponding load branch is updated. After completing the above-mentioned operational status update, the node voltage, branch current, active power, reactive power and apparent power after the execution of the target control action are extracted based on the digital twin model. The controlled active power is then divided by the controlled apparent power to obtain the expected power factor after control.

[0037] After obtaining the expected power factor after regulation, the power factor improvement ratio and the expected stable operating time are calculated. Specifically, the expected power factor after regulation is subtracted from the current power factor value to obtain the power factor increment, and then the power factor increment is divided by the current power factor value to obtain the power factor improvement ratio. Subsequently, continuous time-series calculations are performed on the operating state after the execution of the target regulation action in the digital twin model. The corresponding power factor value is obtained at each calculation time, and the power factor difference between two adjacent calculation times is calculated. When the power factor difference at multiple consecutive calculation times is less than the preset fluctuation threshold, the duration of this continuous period is determined as the expected stable operating time.

[0038] Subsequently, based on historical load data and expected stable operating time, the expected stable operating time improvement ratio after the target control action takes effect is calculated. In this process, the load change curve corresponding to the current date type and time period in the historical load data is read, and the average load stabilization time is calculated based on the load change curve; then, the expected stable operating time is proportionally calculated to the average load stabilization time, thereby generating the expected stable operating time improvement ratio.

[0039] Finally, a weighted calculation is performed on the power factor improvement ratio and the expected stable operating time improvement ratio. Specifically, predetermined weight coefficients are set for both the power factor improvement ratio and the expected stable operating time improvement ratio, and the power factor improvement ratio is multiplied by the corresponding weight coefficient to obtain the power factor benefit value; simultaneously, the expected stable operating time improvement ratio is multiplied by the corresponding weight coefficient to obtain the stable operating time benefit value; then, the power factor benefit value and the stable operating time benefit value are summed to obtain the energy efficiency benefit corresponding to the target control action. This energy efficiency benefit is used to characterize the comprehensive energy efficiency improvement effect brought about by the target control action in improving power utilization efficiency and extending stable operating time.

[0040] Furthermore, in the method provided in the application embodiments, calculating the expected stable operating time improvement ratio after the target control action takes effect based on historical load data and the expected stable operating time, further includes: Read the load change curves of the same date type and time period from historical load data, and calculate the average load stabilization time; calculate the increase ratio of the expected stable operating time relative to the average load stabilization time, and generate the expected stable operating time increase ratio.

[0041] In this embodiment, load change curves with the same date type and time period are first read from historical load data. Specifically, historical load data recorded by the low-voltage switchgear during its historical operation is retrieved from the locally stored historical load database, and the historical load data is filtered according to the date type corresponding to the current operating time. The date type is used to distinguish different operating scenarios such as weekdays, weekends, or holidays. Then, the historical load data is matched with the time period to which the current operating time belongs, and multiple load change curves with the same date type and within the same time period are extracted. The load change curves are load change data recorded in chronological order and are used to characterize the load change of the low-voltage switchgear within that time period.

[0042] After obtaining the load change curves, the stabilization time is calculated for each curve. Specifically, the load change data in each curve is traversed chronologically, the load change rate between adjacent sampling times is calculated, and this rate is compared with a preset load fluctuation threshold. When the load change rate at multiple consecutive sampling times is less than the load fluctuation threshold, this continuous time interval is defined as the load stabilization interval, and the duration of this interval is recorded, thus obtaining the load stabilization time for the corresponding load change curve. Subsequently, the load stabilization times for all load change curves are statistically calculated. The average load stabilization time is obtained by summing the stabilization times and dividing by the number of load change curves.

[0043] After obtaining the average load stabilization time, the expected stable operating time is proportionally calculated to the average load stabilization time to generate the expected stable operating time improvement ratio. Specifically, the expected stable operating time is subtracted from the average load stabilization time to obtain the stable operating time increment. This increment is then divided by the average load stabilization time to obtain the expected stable operating time improvement ratio. This ratio characterizes the degree of improvement in the low-voltage switchgear's stable operating time relative to the historical average stable operating time after the execution of the target control action.

[0044] Step S400: Assess the safety risks of performing the target control action and quantify the safety risk costs.

[0045] In this embodiment, when assessing the safety risks and quantifying the safety risk costs of executing target control actions, the current operating status parameters of the low-voltage switchgear are obtained based on real-time operating data. These parameters include the effective value of the bus voltage, total harmonic distortion rate, current of each branch, and equipment temperature. Based on this, the target control action is simulated, and the voltage change trend and harmonic distribution characteristics after the operation are predicted according to the current operating status parameters. Subsequently, the corresponding safety risk types are identified based on a preset safety risk identification rule base. These risk types include overvoltage risk, harmonic amplification risk, and transient impact risk. Then, the levels of various safety risks are assessed based on the voltage change trend and harmonic distribution characteristics, and the corresponding risk cost coefficients are determined according to the risk levels. Finally, the risk cost coefficients of various safety risks are comprehensively calculated to obtain the safety risk cost.

[0046] Furthermore, in the method provided in the application embodiments, assessing the security risks of performing the target control action and quantifying the security risk costs further includes: Based on the real-time operating data, the current operating status parameters of the low-voltage switchgear are obtained, including the effective value of the bus voltage, the total harmonic distortion rate, the current of each branch, and the equipment temperature. Based on the current operating status parameters, the impact of the target control action on the system electrical parameters is simulated, and the voltage change trend and harmonic distribution characteristics after the operation are predicted. Based on a preset safety risk identification rule base, the types of safety risks are identified, including at least overvoltage risk, harmonic amplification risk, and transient impact risk. Based on the voltage change trend and the harmonic distribution characteristics, the various safety risks are graded, and corresponding risk cost coefficients are assigned according to the risk level. The safety risk cost is calculated by combining the risk cost coefficients of various safety risks.

[0047] In this embodiment, the current operating status parameters of the low-voltage switchgear are first obtained based on real-time operating data. Specifically, based on the real-time operating data obtained by the intelligent control module, the electrical monitoring data in the real-time operating data is parsed. The bus voltage sampling value is read from the real-time operating data, and the instantaneous bus voltage value within a continuous sampling period is squared, summed, and divided by the number of sampling points to obtain the average value. Then, the square root of the average value is performed to obtain the effective value of the bus voltage. Subsequently, harmonic monitoring data is read from the real-time operating data. The harmonic monitoring data includes the fundamental voltage component and each harmonic voltage component. The effective value of the harmonic voltage is obtained by summing the squares of each harmonic voltage component and taking the square root. The total harmonic distortion rate (THD) is obtained by dividing the effective value of the harmonic voltage by the effective value of the fundamental voltage. Simultaneously, the sampled values ​​of the current in each branch are extracted from the real-time operating data, and the instantaneous sampled values ​​of the current in each branch are squared, summed, and divided by the number of sampling points. The square root of the result is then performed to obtain the current in each branch. In addition, the sampled values ​​of the equipment temperature collected by the temperature sensor are read from the real-time operating data, and the average of the sampled values ​​of the equipment temperature at multiple consecutive sampling times is calculated to obtain the equipment temperature. Thus, the current operating status parameters, including the effective value of the bus voltage, the total harmonic distortion rate, the current in each branch, and the equipment temperature, are obtained.

[0048] Next, based on the current operating state parameters, the impact of the target control action on the system's electrical parameters is simulated, and the voltage change trend and harmonic distribution characteristics after the operation are predicted. In this process, historical operation record datasets are collected using the current operating state parameters as constraints. Based on these datasets, the influence relationship between the control action and the system's electrical parameters is identified, and the voltage change trend and harmonic distribution characteristics after the target control action are predicted according to this relationship.

[0049] After obtaining the voltage change trend and harmonic distribution characteristics, the safety risk type is identified based on a pre-set safety risk identification rule library. Specifically, the pre-established safety risk identification rule library is read, which stores risk judgment rules corresponding to different electrical operating states, and the predicted voltage change trend is compared with the voltage threshold rules in the rule library. When the predicted effective value of the bus voltage exceeds 1.1 times the rated voltage, an overvoltage risk is determined according to the voltage threshold rule; simultaneously, the predicted harmonic distribution characteristics are read, and the total harmonic distortion rate in the harmonic distribution characteristics is compared with the harmonic limit standard in the rule library. When the total harmonic distortion rate is greater than 5%, it is identified as a harmonic amplification risk according to the harmonic judgment rule; furthermore, the transient voltage change amplitude in the voltage change trend is used for judgment. When the voltage change amplitude in two adjacent sampling periods exceeds 10% of the rated voltage, it is identified as a transient impact risk according to the transient impact judgment rule, thus obtaining the corresponding safety risk type, which includes overvoltage risk, harmonic amplification risk, and transient impact risk.

[0050] After identifying the types of safety risks, a risk level assessment is performed based on voltage variation trends and harmonic distribution characteristics. Specifically, the risk level is determined according to the deviation between the effective value of the bus voltage and the rated voltage in the voltage variation trend. When the effective value of the bus voltage is between 1.1 and 1.2 times the rated voltage, the overvoltage risk is assessed as Level 1; when it is between 1.2 and 1.3 times the rated voltage, it is assessed as Level 2; and when it exceeds 1.3 times the rated voltage, it is assessed as Level 3. Simultaneously, the harmonic amplification risk level is determined based on the total harmonic distortion (THD) in the harmonic distribution characteristics. When the THD is between 5% and 8%, the harmonic amplification risk is assessed as Level 1. The risk level is categorized into three levels: Level 1 (total harmonic distortion) risk, Level 2 risk, and Level 3 risk, based on the transient voltage change amplitude. Level 1 risk is defined as a transient voltage change amplitude between 10% and 15% of the rated voltage; Level 2 risk is defined as a transient voltage change amplitude between 15% and 20% of the rated voltage; and Level 3 risk is defined as a transient voltage change amplitude exceeding 20% ​​of the rated voltage. The corresponding risk cost coefficient is then retrieved from a preset risk cost coefficient table based on the risk level. Level 1 risk corresponds to a risk cost coefficient of 0.3, Level 2 risk to 0.6, and Level 3 risk to 1.0.

[0051] After obtaining the risk cost coefficients corresponding to various safety risks, a comprehensive calculation is performed on these coefficients to obtain the safety risk cost. Specifically, the risk cost coefficients corresponding to overvoltage risk, harmonic amplification risk, and transient impact risk are summed to obtain the safety risk cost corresponding to the target control action.

[0052] Furthermore, in the method provided in the application embodiments, based on the current operating state parameters, simulating the impact of executing the target control action on the system electrical parameters, and predicting the voltage change trend and harmonic distribution characteristics after the operation, it further includes: The historical operation record dataset is collected under the constraints of the current operating status parameters; based on the historical operation record dataset, the influence relationship between the control action and the system electrical parameters is identified, and the voltage change trend and harmonic distribution characteristics after the operation are predicted.

[0053] In this embodiment, historical operation record datasets are first collected, constrained by the current operating status parameters. Specifically, historical operation record data is read from the low-voltage switchgear operation database, and the current operating status parameters are read, including the effective value of the bus voltage, total harmonic distortion (THD), current of each branch, and equipment temperature. Then, for each historical operation record, the absolute difference between the effective value of the bus voltage in that historical operation record and the current effective value of the bus voltage is calculated. This is done by subtracting the current effective value from the historical effective value of the bus voltage and taking the absolute value, resulting in the bus voltage deviation value. The absolute difference between the THD in that historical operation record and the current THD is also calculated, resulting in the THD deviation value. Finally, for each branch current... The system calculates the absolute difference between the current of each branch in the historical operation record and the current of the corresponding branch. It then sums all the absolute differences of the branch currents and divides the sum by the number of branches to obtain the average deviation value of the current of each branch. The system then divides the bus voltage deviation value by the rated voltage to obtain the bus voltage deviation ratio. The system compares the total harmonic distortion rate deviation value with the preset harmonic deviation threshold and divides the average deviation value of the current of each branch by the rated current to obtain the branch current deviation ratio. When the bus voltage deviation ratio is less than 5%, the total harmonic distortion rate deviation value is less than 1%, and the branch current deviation ratio is less than 10%, the historical operation record is retained, thus forming a historical operation record dataset.

[0054] Next, the influence relationship between control actions and system electrical parameters is identified based on the historical operation record dataset. Specifically, the control action type, the effective value of the bus voltage before the control action, the effective value of the bus voltage after the control action, the harmonic voltage components before the control action, and the harmonic voltage components after the control action are read from the historical operation record dataset for each historical operation record. Then, for each historical operation record, the effective value of the bus voltage after the control action is calculated minus the effective value of the bus voltage before the control action to obtain the voltage change corresponding to that historical operation record. For each harmonic voltage component, the difference between the effective value of the harmonic voltage after the control action and the corresponding harmonic voltage before the control action is calculated. The voltage component is analyzed to obtain the harmonic variations. Then, the historical operation records are categorized according to the type of control action. All voltage variations belonging to the same control action type are summed, and the sum is divided by the number of historical operation records corresponding to that control action type to obtain the average voltage variation for that control action type. Simultaneously, each harmonic variation belonging to the same control action type is summed, and the sum of each harmonic variation is divided by the number of historical operation records corresponding to that control action type to obtain the average harmonic variation for that control action type. This yields the relationship between the control action and the system's electrical parameters.

[0055] After obtaining the influencing relationships, the voltage change trend and harmonic distribution characteristics after the operation are predicted. In this process, the current effective value of the bus voltage in the current operating status parameters is read, and the current effective value of the bus voltage is added to the average voltage change corresponding to the target control action to obtain the predicted value of the bus voltage after the target control action is executed. When the historical operation record dataset contains voltage changes at multiple consecutive sampling times, the average voltage change corresponding to each sampling time is calculated, and the current effective value of the bus voltage is added to the average voltage change corresponding to each sampling time one by one to obtain the predicted value of the bus voltage at each sampling time. The predicted values ​​of the bus voltage are then arranged according to the sampling time order to determine the voltage change trend. Simultaneously, the harmonic voltage components corresponding to the current operating status parameters are read, and each current harmonic voltage component is added to the average harmonic change corresponding to the target control action to obtain the predicted harmonic voltage components after the target control action is executed. Then, the predicted harmonic voltage components are arranged according to the harmonic order, and the proportion of each harmonic in the total harmonics is determined based on the magnitude of each predicted harmonic voltage component, thereby determining the harmonic distribution characteristics.

[0056] Step S500: Based on the energy efficiency benefits, the lifespan loss costs, and the safety risk costs, calculate the net benefit value of executing the target control action, compare it with a preset decision threshold, and make a decision on the target control action.

[0057] In this embodiment, the net benefit of executing the target control action is first calculated based on energy efficiency benefits, lifetime loss costs, and safety risk costs. In this process, the energy efficiency benefits, lifetime loss costs, and safety risk costs are first normalized to convert them into dimensionless values ​​within a uniform range. Specifically, the maximum and minimum energy efficiency benefits recorded in historical operating data are read separately. The current energy efficiency benefit is then normalized by subtracting the minimum energy efficiency benefit from the current energy efficiency benefit and dividing by the difference between the maximum and minimum energy efficiency benefits. Simultaneously, the maximum and minimum lifetime loss costs recorded in historical operating data are read separately. The current lifetime loss cost is then normalized by subtracting the minimum lifetime loss cost from the current lifetime loss cost and dividing by the difference between the maximum and minimum lifetime loss costs. Finally, the maximum and minimum safety risk costs recorded in historical operating data are read separately. The current safety risk cost is then normalized by subtracting the minimum safety risk cost from the current safety risk cost and dividing by the difference between the maximum and minimum safety risk costs. After normalization, the normalized energy efficiency benefit is subtracted from the normalized lifetime loss cost, and then the normalized safety risk cost is subtracted from the normalized safety risk cost to obtain the net benefit value corresponding to the target control action.

[0058] The net profit value is then compared with a preset decision threshold to determine the target control action. If the net profit value is greater than zero, the target control action is allowed to be executed; if the net profit value is less than or equal to zero, the target control action is rejected.

[0059] Furthermore, in the method provided in the application embodiments, the decision-making process for the target control action further includes: If the net profit value is greater than zero, the target control action is allowed to be executed; if the net profit value is less than or equal to zero, the target control action is rejected.

[0060] In this embodiment of the application, the net profit value is compared with a preset decision threshold, wherein the preset decision threshold is set to zero and is used as a criterion for determining whether to perform the target control action.

[0061] When the net benefit value is greater than zero, it means that the energy efficiency benefit brought about by the execution of the target control action is greater than the sum of the equipment life loss cost and the safety risk cost. At this time, the target control action is determined to be executable, and an execution command is sent to the relevant control device in the low-voltage cabinet, thereby allowing the execution of the target control action.

[0062] When the net benefit value is less than or equal to zero, it means that the energy efficiency benefits brought by the execution of the target control action are insufficient to offset the cost of equipment life loss and safety risk. At this time, the target control action is determined to be in an unexecutable state, and the current low-voltage switchgear operation status remains unchanged, and the target control action is refused to be executed.

[0063] In summary, the embodiments of this application have at least the following technical effects: This application acquires real-time operating data of equipment within a low-voltage switchgear to determine the target control action at the next control moment; calculates the expected lifespan loss cost of the equipment under the target control action; simulates the expected energy efficiency improvement effect after executing the target control action and calculates the corresponding energy efficiency benefit; assesses the safety risk of executing the target control action and quantifies the safety risk cost; based on the energy efficiency benefit, the lifespan loss cost, and the safety risk cost, calculates the net benefit value of executing the target control action and compares it with a preset decision threshold to make a decision on the target control action. This invention solves the technical problem in existing low-voltage switchgear control decisions that only focus on energy efficiency while ignoring equipment lifespan loss and operational safety risks. By comprehensively evaluating the energy efficiency benefit, lifespan loss cost, and safety risk cost of the target control action and calculating the net benefit value for decision control, it achieves the technical effect of optimizing the energy efficiency of low-voltage switchgear operation while ensuring equipment operational safety and extending equipment lifespan.

[0064] Example 2 is based on the same inventive concept as the low-voltage switchgear operation control method based on energy efficiency optimization in the previous examples, such as... Figure 2 As shown, this application provides a low-voltage switchgear operation control system based on energy efficiency optimization. The system and method embodiments in this application are based on the same inventive concept. The system includes: The control action determination module 11 is used to acquire real-time operating data of the equipment in the low-voltage switchgear and determine the target control action at the next control moment; the loss cost calculation module 12 is used to calculate the expected life loss cost of the equipment under the target control action; the energy efficiency benefit calculation module 13 is used to simulate the expected energy efficiency improvement effect after executing the target control action and calculate the corresponding energy efficiency benefit; the safety risk quantification module 14 is used to assess the safety risk of executing the target control action and quantify the safety risk cost; the decision module 15 is used to calculate the net benefit value of executing the target control action based on the energy efficiency benefit, the life loss cost and the safety risk cost, and compare it with a preset decision threshold to make a decision on the target control action.

[0065] Furthermore, the system is also used to implement the following functions: Identify the sub-devices involved in the target control action, and construct a cumulative contact electrical wear model and an operating mechanism mechanical fatigue model for the sub-devices based on the real-time operating data; obtain the expected breaking current value of the target control action based on the real-time operating data, substitute it into the cumulative contact electrical wear model, and calculate the percentage of electrical lifetime that the target control action will consume; based on the mechanical action that the target control action will perform, substitute it into the operating mechanism mechanical fatigue model, and calculate the percentage of mechanical lifetime that will be consumed; weight the percentage of electrical lifetime and the percentage of mechanical lifetime to generate the expected lifetime loss cost.

[0066] Furthermore, the system is also used to implement the following functions: Identify the sub-devices involved in the target control action, and retrieve the model parameters of the sub-devices from local storage. The model parameters include at least the material electrical wear coefficient and the maximum permissible number of mechanical operations. Based on the material electrical wear coefficient and the maximum permissible number of mechanical operations, construct the cumulative electrical wear model of the contact and the mechanical fatigue model of the operating mechanism.

[0067] Furthermore, the system is also used to implement the following functions: Based on the real-time operating data, the current active load and real-time electricity price are obtained, and the current power factor value is read; the target control action is simulated and executed, and twin modeling is performed based on the topology and impedance parameters of the low-voltage switchgear to calculate the expected power factor after control, obtain the power factor improvement ratio and the expected stable operating time; based on historical load data and the expected stable operating time, the expected stable operating time improvement ratio after the target control action takes effect is calculated; the power factor improvement ratio and the expected stable operating time improvement ratio are weighted to generate the energy efficiency benefit.

[0068] Furthermore, the system is also used to implement the following functions: Read the load change curves of the same date type and time period from historical load data, and calculate the average load stabilization time; calculate the increase ratio of the expected stable operating time relative to the average load stabilization time, and generate the expected stable operating time increase ratio.

[0069] Furthermore, the system is also used to implement the following functions: Based on the real-time operating data, the current operating status parameters of the low-voltage switchgear are obtained, including the effective value of the bus voltage, the total harmonic distortion rate, the current of each branch, and the equipment temperature. Based on the current operating status parameters, the impact of the target control action on the system electrical parameters is simulated, and the voltage change trend and harmonic distribution characteristics after the operation are predicted. Based on a preset safety risk identification rule base, the types of safety risks are identified, including at least overvoltage risk, harmonic amplification risk, and transient impact risk. Based on the voltage change trend and the harmonic distribution characteristics, the various safety risks are graded, and corresponding risk cost coefficients are assigned according to the risk level. The safety risk cost is calculated by combining the risk cost coefficients of various safety risks.

[0070] Furthermore, the system is also used to implement the following functions: The historical operation record dataset is collected under the constraints of the current operating status parameters; based on the historical operation record dataset, the influence relationship between the control action and the system electrical parameters is identified, and the voltage change trend and harmonic distribution characteristics after the operation are predicted.

[0071] Furthermore, the system is also used to implement the following functions: If the net profit value is greater than zero, the target control action is allowed to be executed; if the net profit value is less than or equal to zero, the target control action is rejected.

[0072] Furthermore, the system is also used to implement the following functions: The system connects to the pre-set intelligent control module of the low-voltage switchgear, acquires the real-time operating data and the control command generated for the next control moment based on the real-time operating data, and parses the control command to obtain the target control action.

[0073] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0074] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A low-voltage switchgear operation control method based on energy efficiency optimization, characterized in that, include: Acquire real-time operating data of equipment inside the low-voltage switchgear to determine the target control action at the next control moment; Calculate the expected lifespan loss cost of the equipment under the target control action; Simulate the expected energy efficiency improvement effect after executing the target control action, and calculate the corresponding energy efficiency benefit; Assess the safety risks of performing the target control actions and quantify the costs of those safety risks. Based on the energy efficiency benefits, the lifespan loss costs, and the safety risk costs, the net benefit value of executing the target control action is calculated and compared with a preset decision threshold to make a decision on the target control action.

2. The low-voltage switchgear operation control method based on energy efficiency optimization as described in claim 1, characterized in that, Calculate the expected lifespan loss cost of the equipment under the target control action, including: Identify the sub-devices involved in the target control action, and construct a cumulative contact electrical wear model and an operating mechanism mechanical fatigue model for the sub-devices based on the real-time operating data; Based on the real-time operating data, the expected breaking current value of the target control action is obtained, and then substituted into the contact electrical wear cumulative model to calculate the percentage of electrical lifetime that the target control action will consume. Based on the mechanical action to be performed by the target control action, the mechanical fatigue model of the operating mechanism is substituted into the model to calculate the percentage of mechanical life that will be consumed. The expected lifespan cost is generated by weighting the electrical lifespan percentage with the mechanical lifespan percentage.

3. The low-voltage switchgear operation control method based on energy efficiency optimization as described in claim 2, characterized in that, Identify the sub-devices involved in the target control action, and construct a cumulative contact electrical wear model and a mechanical fatigue model of the operating mechanism for the sub-devices based on the real-time operating data, including: Identify the sub-devices involved in the target control action and retrieve the model parameters of the sub-devices from local storage. The model parameters include at least the material electrical wear coefficient and the maximum allowable number of mechanical operations. Based on the material's electrical wear coefficient and the maximum permissible number of mechanical operations, a cumulative electrical wear model for the contact and a mechanical fatigue model for the operating mechanism are constructed.

4. The low-voltage switchgear operation control method based on energy efficiency optimization as described in claim 1, characterized in that, Simulate the expected energy efficiency improvement effect after executing the target control action, and calculate the corresponding energy efficiency gain, including: Based on the real-time operating data, the current active load and real-time electricity price are obtained, and the current power factor value is read. The target control action is simulated and executed. Based on the topology and impedance parameters of the low-voltage switchgear, a twin model is performed to calculate the expected power factor after control, and the power factor improvement ratio and expected stable operation time are obtained. Based on historical load data and the expected stable operating time, calculate the percentage increase in expected stable operating time after the target control action takes effect; The energy efficiency gain is generated by weighting the power factor improvement ratio with the expected stable operating time improvement ratio.

5. The low-voltage switchgear operation control method based on energy efficiency optimization as described in claim 4, characterized in that, Based on historical load data and the expected stable operating time, calculate the percentage increase in expected stable operating time after the target control action takes effect, including: Read the load change curves of the same date type and time period from historical load data, and calculate the average load stability time. Calculate the percentage increase in the expected stable operating time relative to the average load stabilization time, and generate the percentage increase in the expected stable operating time.

6. The low-voltage switchgear operation control method based on energy efficiency optimization as described in claim 1, characterized in that, Assess the safety risks of performing the aforementioned target control actions and quantify the costs of these safety risks, including: Based on the real-time operating data, the current operating status parameters of the low-voltage switchgear are obtained, including the effective value of the bus voltage, the total harmonic distortion rate, the current of each branch, and the equipment temperature. Based on the current operating status parameters, simulate the impact of the target control action on the system electrical parameters, and predict the voltage change trend and harmonic distribution characteristics after the operation. Based on a preset safety risk identification rule base, the types of safety risks are identified, including at least overvoltage risk, harmonic amplification risk, and transient impact risk. Based on the voltage change trend and the harmonic distribution characteristics, various safety risks are assessed and assigned corresponding risk cost coefficients according to the risk level. The cost of the security risk is calculated by taking into account the risk cost coefficients of various security risks.

7. The low-voltage switchgear operation control method based on energy efficiency optimization as described in claim 6, characterized in that, Based on the current operating state parameters, simulate the impact of the target control action on the system's electrical parameters, and predict the voltage change trend and harmonic distribution characteristics after the operation, including: Collect a dataset of historical operation records using the current operating status parameters as constraints; Based on the historical operation record dataset, the influence relationship between control actions and system electrical parameters is identified, and the voltage change trend and harmonic distribution characteristics after the operation are predicted.

8. The low-voltage switchgear operation control method based on energy efficiency optimization as described in claim 1, characterized in that, Making decisions on the target control actions includes: If the net profit value is greater than zero, the target adjustment action is permitted. If the net return is less than or equal to zero, the target control action will not be executed.

9. The low-voltage switchgear operation control method based on energy efficiency optimization as described in claim 1, characterized in that, Acquire real-time operating data of equipment within the low-voltage switchgear to determine the target control action for the next control moment, including: Connect to the intelligent control module preset in the low-voltage switchgear to obtain the real-time operating data and the control command for the next control moment generated based on the real-time operating data; The target control action is obtained by parsing the control command.

10. A low-voltage switchgear operation control system based on energy efficiency optimization, characterized in that, The system is used to execute the low-voltage switchgear operation control method based on energy efficiency optimization as described in any one of claims 1-9, and the system includes: The control action determination module is used to acquire real-time operating data of the equipment in the low-voltage cabinet and determine the target control action at the next control moment. The loss cost calculation module is used to calculate the expected lifespan loss cost of the equipment under the target control action; The energy efficiency benefit calculation module is used to simulate the expected energy efficiency improvement effect after the target control action is executed, and to calculate the corresponding energy efficiency benefit. The safety risk quantification module is used to assess the safety risks of performing the target control action and quantify the safety risk costs. The decision module is used to calculate the net benefit value of executing the target control action based on the energy efficiency benefit, the life loss cost and the safety risk cost, and compare it with the preset decision threshold to make a decision on the target control action.

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