Electric power operation remote monitoring and early warning system based on artificial intelligence

Abstract

The invention belongs to the technical field of electric power operation supervision, and particularly relates to an electric power operation remote monitoring and early warning system based on artificial intelligence, which comprises an electric power operation comprehensive analysis module, a time period risk decision module, a planning executive judgment module and a remote early warning terminal, according to the invention, all power equipment and power lines are monitored and analyzed through the power operation comprehensive analysis module, so that effective monitoring and rapid early warning and feedback of power operation are realized, safe and stable operation of a power system is ensured, and a dangerous time period and an easy-to-manage time period are determined through time period risk decision analysis. According to the method, adaptive management schemes and supervision intensities can be adopted for power operation in different time periods, power operation management is more scientific, the management plan execution performance condition for power operation is accurately judged in real time through the plan execution judgment module, management personnel optimization adjustment and personnel supervision can be carried out in time, and the management efficiency is improved. The safety of power operation is further ensured, and the intelligent degree is high.

Description

Technical Field

[0001] The present invention relates to the technical field of power operation supervision, and specifically to a remote monitoring and early warning system for power operation based on artificial intelligence. Background Art

[0002] Power operation is a crucial part of the power system. Through scientific management and scheduling, it ensures the normal supply of electric energy and the stable operation of the power grid, providing a solid power guarantee for the economic development and life of society. With the continuous development of the power system and the complexity of power equipment, power monitoring and early warning technologies have become the key to ensuring the stable operation of the power system; Currently, it is difficult to effectively monitor and analyze corresponding power equipment and power lines during power operation supervision and give early warnings for power operation in a timely manner. Moreover, it is impossible to reasonably judge the power operation risks in each period and accurately evaluate the implementation performance of power management plans in real time. The difficulty of power operation supervision is large, which is not conducive to ensuring the safe and stable operation of the power system; In view of the above technical defects, a solution is proposed now. Summary of the Invention

[0003] The purpose of the present invention is to provide a remote monitoring and early warning system for power operation based on artificial intelligence, which solves the problems that it is difficult to effectively monitor and analyze corresponding power equipment and power lines and give early warnings for power operation in a timely manner in the prior art, and it is impossible to reasonably judge the power operation risks in each period and accurately evaluate the implementation performance of power management plans in real time, with low intelligence and great difficulty in power operation supervision.

[0004] To achieve the above purpose, the present invention provides the following technical solutions: A remote monitoring and early warning system for power operation based on artificial intelligence includes a power operation comprehensive analysis module, a time period risk decision module, a planning execution judgment module, and a remote early warning terminal; The power operation comprehensive analysis module obtains the equipment symbols of all power equipment to be monitored and the line symbols of all power lines to be monitored. If there is a power equipment corresponding to the equipment symbol SK-1 or a power line corresponding to the line symbol XK-1, it generates a power operation early warning message and sends the power operation early warning message to the remote early warning terminal; The time period risk decision module is used to set eight time periods every day, with a duration of three hours for each time period. Through time period risk decision analysis, the corresponding time period is marked as a high-risk time period or an easy-to-manage time period, and the marking information of all time periods is sent to the remote early warning terminal; The planning execution judgment module is used to determine the current time period, analyze and judge the current personnel planning execution status, generate an execution exception signal or an execution qualified signal accordingly, and send the execution exception signal to the remote warning terminal. When the remote warning terminal receives the execution exception signal, it issues a corresponding warning.

[0005] Furthermore, the power operation comprehensive analysis module is communicatively connected to the power equipment monitoring module and the power line monitoring module. The power equipment monitoring module obtains all the power equipment to be monitored, monitors the corresponding power equipment, analyzes and assigns the equipment symbol SK-1 or SK-2 to the corresponding monitoring equipment, and sends the equipment symbol of the corresponding power equipment to the power operation comprehensive analysis module; The power line monitoring module obtains all the power lines to be monitored, monitors the corresponding power lines, analyzes and assigns the line symbol XK-1 or XK-2 to the corresponding power lines, and sends the line symbol of the corresponding power lines to the power operation comprehensive analysis module.

[0006] Furthermore, the analysis process of the power equipment monitoring module is as follows: Collect the smoke concentration data and internal temperature data of the environment where the corresponding power equipment is located, compare the smoke concentration data and internal temperature data with the preset smoke concentration data threshold and the preset internal temperature data threshold respectively. If the smoke concentration data or the internal temperature data exceeds the corresponding preset threshold, assign SK-1 to the corresponding power equipment; If both the smoke concentration data and the internal temperature data do not exceed the corresponding preset thresholds, obtain the decibel value of the abnormal sound generated by the corresponding power equipment and the amplitude value of the vibration generated, and mark them as the abnormal sound detection value and the abnormal vibration detection value respectively. Compare the abnormal sound detection value and the abnormal vibration detection value with the preset abnormal sound detection threshold and the preset abnormal vibration detection threshold respectively. If the abnormal sound detection value or the abnormal vibration detection value exceeds the corresponding preset threshold, assign SK-1 to the corresponding power equipment.

[0007] Furthermore, if both the abnormal sound detection value and the abnormal vibration detection value do not exceed the corresponding preset thresholds, obtain the operating parameters that the power equipment needs to be monitored, collect the real-time detection data of the corresponding operating parameters, compare the real-time detection data of the corresponding operating parameters with the corresponding preset data requirements. If there are operating parameters that do not meet the corresponding preset data requirements, assign SK-1 to the corresponding power equipment; if the real-time detection data of all the operating parameters of the corresponding power equipment meet the corresponding preset data requirements, assign SK-2 to the corresponding power equipment.

[0008] Furthermore, the analysis process of the power line monitoring module is as follows: Voltage and current at several positions on the corresponding power line are obtained. The variances of the voltages at all positions are calculated to obtain a voltage stability judgment value, and the variances of the currents at all positions are calculated to obtain a current stability judgment value. The voltage stability judgment value and the current stability judgment value are respectively compared numerically with the corresponding preset voltage stability judgment threshold and preset current stability judgment threshold. If the voltage stability judgment value or the current stability judgment value exceeds the corresponding preset threshold, the line symbol XK-1 is assigned to the corresponding power line; If both the voltage stability judgment value and the current stability judgment value do not exceed the corresponding preset threshold, the deviation value of the mean value of the voltages at all positions compared with the corresponding preset appropriate voltage standard value is marked as the voltage deviation judgment value, and the deviation value of the mean value of the currents at all positions compared with the corresponding preset appropriate current standard value is marked as the current deviation judgment value. The voltage deviation judgment value and the current deviation judgment value are respectively compared numerically with the corresponding preset voltage deviation judgment threshold and preset current deviation judgment threshold. If the voltage deviation judgment value or the current deviation judgment value exceeds the corresponding preset threshold, the line symbol XK-1 is assigned to the corresponding power line.

[0009] Furthermore, if both the voltage deviation judgment value and the current deviation judgment value do not exceed the corresponding preset threshold, the ratio of the power output from the corresponding power line within a unit time to the power input to the corresponding power line is calculated to obtain a power output inspection value, and the real-time temperatures at all positions on the corresponding power line are collected. The positions where the real-time temperature exceeds the preset real-time temperature threshold are marked as abnormal positions. The ratio of the number of abnormal positions on the corresponding power line is obtained and marked as the abnormal position detection value, and the mean value of the real-time temperatures at all positions is calculated to obtain the line temperature detection value; By numerically calculating the power output inspection value, the abnormal position detection value, and the line temperature detection value, a power line evaluation value is obtained. The power line evaluation value is compared numerically with the corresponding preset power line evaluation threshold. If the power line evaluation value exceeds the preset power line evaluation threshold, the line symbol XK-1 is assigned to the corresponding power line; if the power line evaluation value does not exceed the preset power line evaluation threshold, the line symbol XK-2 is assigned to the corresponding power line.

[0010] Furthermore, the specific analysis process of the time period risk decision-making analysis is as follows: Taking the current date as the end date and tracing back L1 days, mark the interval duration between the current date and the previous L1 days as the tracing period; collect the number of times of generating power operation warning information in the corresponding time period of the corresponding date within the tracing period and mark it as the power operation alarm value, calculate the average value of all power operation alarm values in the corresponding time period within the tracing period to obtain the initial judgment value of the time period riskiness, compare the initial judgment value of the time period riskiness with the preset initial judgment threshold of the time period riskiness. If the initial judgment value of the time period riskiness exceeds the preset initial judgment threshold of the time period riskiness, mark the corresponding time period as a vulnerable time period; If the initial judgment value of the time period riskiness does not exceed the preset initial judgment threshold of the time period riskiness, compare the power operation warning value in the corresponding time period of the corresponding date with the preset power operation warning threshold. If the power operation warning value exceeds the preset power operation warning threshold, mark the corresponding date as a vulnerable date; obtain the number of vulnerable dates corresponding to the corresponding time period within the tracing period and mark it as the vulnerable date table value, and mark the maximum power operation warning value corresponding to the corresponding time period within the tracing period as the power operation risk amplitude value; Calculate the time period risk decision value by numerically calculating the initial judgment value of the time period riskiness, the vulnerable date table value, and the power operation risk amplitude value, compare the time period risk decision value with the preset time period risk decision threshold. If the time period risk decision value exceeds the preset time period risk decision threshold, mark the corresponding time period as a vulnerable time period; if the time period risk decision value does not exceed the preset time period risk decision threshold, mark the corresponding time period as an easy-to-manage time period.

[0011] Furthermore, the specific analysis process of the planning execution judgment module includes: Obtain the current time period and mark it as the decision time period, mark the number of on-duty management personnel currently conducting power operation management as the power management inspection value, determine the excellent management personnel through management personnel evaluation and analysis, mark the number of current on-duty excellent management personnel as the power excellent management inspection value, and calculate the average value of the management evaluation values of all current on-duty management personnel to obtain the management situation value; Calculate the execution detection value by numerically calculating the power management inspection value, the power excellent management inspection value, and the management situation value. If the decision time period is a vulnerable time period, allocate the preset execution detection threshold FY1 to it. If the decision time period is an easy-to-manage time period, allocate the preset execution detection threshold FY2 to it, and FY1 > FY2 > 0; compare the execution detection value with the corresponding preset execution detection threshold. If the execution detection value exceeds the corresponding preset execution detection threshold, generate an execution normal signal; if the execution detection value does not exceed the corresponding preset execution detection threshold, generate an execution abnormal signal.

[0012] Furthermore, the specific analysis process of the management personnel evaluation and analysis is as follows: The total historical on-duty duration of the corresponding management personnel in the power operation management position is collected and marked as the power operation inspection value. The number of times the corresponding management personnel failed to arrive on time to perform power operation and maintenance tasks during the historical period is marked as the power execution delay frequency value. And the number of mistakes made by the corresponding management personnel during the power operation management process in the historical period is marked as the power management mistake frequency value. By performing numerical calculations on the power operation inspection value, the power execution delay frequency value, and the power management mistake frequency value, a management personnel evaluation value is obtained. The management personnel evaluation value is numerically compared with a preset management personnel evaluation threshold. If the management personnel evaluation value exceeds the preset management personnel evaluation threshold, the corresponding management personnel is marked as excellent management personnel.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. In the present invention, the power operation comprehensive analysis module monitors and analyzes all power equipment and power lines to achieve effective monitoring of power operation and rapid early warning feedback. And through time period risk decision-making analysis to determine the easy-risk time period and easy-management time period, which helps to adopt appropriate management plans and supervision intensities for power operation in different time periods. And through the planning execution judgment module to accurately judge the execution performance status of the management plan for power operation in real time, it can timely optimize and adjust management personnel and supervise personnel, effectively ensuring the safety of power operation and having a high degree of intelligence. 2. In the present invention, the power equipment monitoring module reasonably analyzes and accurately feedbacks the abnormal operation conditions of each power equipment to remind timely maintenance and other treatment measures for the corresponding power equipment. The power line monitoring module reasonably analyzes and accurately feedbacks the potential safety hazards of each power line to remind timely inspection and other treatment measures for the corresponding power line. And the power equipment monitoring module and the power line monitoring module can provide information support for the analysis process of the power operation comprehensive analysis module to ensure the accuracy of its analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings. Figure 1 It is the system block diagram of Embodiment 1 in the present invention. Figure 2 It is the system block diagram of Embodiment 2 and Embodiment 3 in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0016] Example 1: As Figure 1 shown, a remote monitoring and early warning system for power operation based on artificial intelligence proposed by the present invention includes a power operation comprehensive analysis module, a time period risk decision-making module, a planning execution judgment module, and a remote early warning terminal; The power operation comprehensive analysis module obtains the device symbols of all power devices to be monitored and the line symbols of all power lines to be monitored. If there is a power device corresponding to the device symbol SK-1 or a power line corresponding to the line symbol XK-1, a power operation early warning message is generated and sent to the remote early warning terminal. The remote early warning terminal displays the power operation early warning message and issues a corresponding warning to remind timely improvement measures to be taken for the power system, so as to realize effective monitoring of power operation and rapid early warning feedback, and ensure the safe and stable operation of the power system; The time period risk decision-making module sets eight time periods every day, and the duration of each time period is three hours. Through time period risk decision-making analysis, the corresponding time period is marked as a high-risk time period or an easy-to-manage time period, and the marking information of all time periods is sent to the remote early warning terminal, which can reasonably analyze the power operation risk of each time period and accurately divide its risk level, so as to help adopt appropriate management plans and supervision intensities for power operation in different time periods, making power operation management more scientific and targeted, with a high degree of intelligence, and further ensuring the safe and stable operation of the power system in each time period. The specific analysis process of time period risk decision-making analysis is as follows: Taking the current date as the end date and tracing back L1 days forward. Preferably, L1 is 25 days; the time interval between the current date and the previous L1 days is marked as the tracing period; the number of times of generating power operation early warning messages in the corresponding time period of the corresponding date within the tracing period is collected and marked as the power operation alarm value. The average value of all power operation alarm values in the corresponding time period within the tracing period is calculated to obtain the initial judgment value of time period risk. The initial judgment value of time period risk is compared with the preset initial judgment threshold of time period risk. If the initial judgment value of time period risk exceeds the preset initial judgment threshold of time period risk, it indicates that the power operation risk of the corresponding time period is relatively large initially, and the corresponding time period is marked as a high-risk time period; If the initial judgment value of time period risk does not exceed the preset initial judgment threshold of time period risk, the power operation early warning value in the corresponding time period of the corresponding date is compared with the preset power operation early warning threshold. If the power operation early warning value exceeds the preset power operation early warning threshold, it indicates that the power operation condition in the corresponding time period of the corresponding date is not good, and the corresponding date is marked as a high-risk date; the number of high-risk dates corresponding to the corresponding time period within the tracing period is obtained and marked as the high-risk date table value, and the maximum power operation early warning value corresponding to the corresponding time period within the tracing period is marked as the power operation risk amplitude value; The initial judgment value of the risk during a period RY, the value of the risk-prone day table RK, and the amplitude value of the power operation risk RN are numerically calculated through the formula P = q2 * RK + (q1 * RY + q3 * RN) / 2 to obtain the decision value P of the risk during the period, where q1, q2, and q3 are preset proportionality coefficients, and q2 > q1 > q3 > 0; moreover, the larger the numerical value of the decision value P of the risk during the period, the greater the power operation risk during the corresponding period in the retrospective period overall. The decision value P of the risk during the period is numerically compared with the preset decision threshold of the risk during the period. If the decision value P of the risk during the period exceeds the preset decision threshold of the risk during the period, it indicates that the power operation risk during the corresponding period in the retrospective period is relatively large and it is difficult to conduct power operation management. Then, the corresponding period is marked as a risk-prone period; if the decision value P of the risk during the period does not exceed the preset decision threshold of the risk during the period, it indicates that the power operation risk during the corresponding period in the retrospective period is relatively small and it is easier to conduct power operation management. Then, the corresponding period is marked as an easy-to-manage period.

[0017] The planning execution judgment module is used to determine the current period. By analyzing and judging the personnel planning execution status of the current period, an execution exception signal or an execution qualified signal is generated accordingly, and the execution exception signal is sent to the remote warning terminal. When the remote warning terminal receives the execution exception signal, it issues a corresponding warning, which can accurately judge the execution performance status of the management plan for power operation in real time, optimize and adjust the management personnel when issuing a corresponding warning, and strengthen the supervision of the management personnel, so as to further ensure the safety of power operation; the specific analysis process of the planning execution judgment module is as follows: The number of on-the-job management personnel currently conducting power operation management is marked as the power management inspection value. The excellent management personnel are determined through the evaluation and analysis of the management personnel. Specifically: the total historical on-the-job duration of the corresponding management personnel in the power operation management position is collected and marked as the power operation management inspection time value, and the number of times that the corresponding management personnel failed to arrive on time to perform power operation and maintenance tasks in the historical stage is marked as the power execution delay frequency value, and the number of mistakes made by the corresponding management personnel in the process of power operation management in the historical stage is marked as the power management mistake frequency value; Through the formula The power operation management inspection time value TY, the power execution delay frequency value TL, and the power management mistake frequency value TS are numerically calculated to obtain the management personnel evaluation value TW, where hm1, hm2, and hm3 are preset proportionality coefficients greater than zero, and moreover, the larger the numerical value of the management personnel evaluation value TW, the richer the power operation management experience and the better the management performance of the corresponding management personnel overall; The manager evaluation value TW is numerically compared with the preset manager evaluation threshold. If the manager evaluation value TW exceeds the preset manager evaluation threshold, it indicates that the corresponding manager has rich experience in power operation management and good management performance, and the corresponding manager is marked as an excellent manager. The number of excellent managers currently on duty is marked as the power excellent manager inspection value, and the manager evaluation values ​​of all managers currently on duty are averaged to obtain the manager status value. By formula The power manager inspection value YN, the power excellent manager inspection value YM and the manager status value YS are numerically calculated to obtain the execution detection value G, where hu1, hu2 and hu3 are preset proportional coefficients, hu2>hu1>hu3>0; and the larger the value of the execution detection value G, the better the execution performance of the current power management plan; The current time period is obtained and marked as a decision period. If the decision period is a risky period, a preset executable detection threshold FY1 is assigned to it. If the decision period is an easy-to-manage period, a preset executable detection threshold FY2 is assigned to it, and FY1>FY2>0. By setting different preset executable detection thresholds, the results of the power management plan execution performance analysis can be made more accurate. The executable detection value G is numerically compared with the corresponding preset executable detection threshold. If the executable detection value G exceeds the corresponding preset executable detection threshold, it indicates that the current power management plan execution performance is good, and a normal executable signal is generated; if the executable detection value G does not exceed the corresponding preset executable detection threshold, it indicates that the current power management plan execution performance is poor, and an abnormal executable signal is generated.

[0018] Embodiment 2: Figure 2 As shown, the difference between this embodiment and embodiment 1 is that the power operation analysis module is connected to the power equipment monitoring module in communication, the power equipment monitoring module obtains all power equipment that needs to be monitored, monitors the corresponding power equipment, and allocates equipment symbol SK-1 or SK-2 to the corresponding monitoring equipment through analysis; The equipment symbol of the corresponding power equipment is sent to the power operation comprehensive analysis module, which can not only reasonably analyze and accurately feedback the abnormal operation status of each power equipment to remind timely operation and maintenance of the corresponding power equipment, but also provide information support for the analysis process of the power operation comprehensive analysis module to ensure the accuracy of its analysis results; the analysis process of the power equipment monitoring module is as follows: Collect smoke density data and internal temperature data of the environment where the corresponding power equipment is located, and compare the smoke density data and internal temperature data with the preset smoke density data threshold and the preset internal temperature data threshold respectively. If the smoke density data or the internal temperature data exceeds the corresponding preset threshold, it indicates that the corresponding power equipment has a fire risk and a large hidden danger in operation safety, then SK-1 is allocated to the corresponding power equipment; If the smoke density data and the internal temperature data do not exceed the corresponding preset thresholds, indicating that there is no fire risk in the corresponding power equipment, the decibel value of the abnormal sound and the amplitude value of the vibration generated by the corresponding power equipment are obtained and marked as the abnormal sound detection value and the abnormal vibration detection value respectively; The abnormal sound detection value and the abnormal vibration detection value are numerically compared with the preset abnormal sound detection threshold and the preset abnormal vibration detection threshold respectively. If the abnormal sound detection value or the abnormal vibration detection value exceeds the corresponding preset threshold, it indicates that the corresponding power equipment has obvious abnormal sound or shaking, and the probability of abnormal operation is high, then SK-1 is assigned to the corresponding power equipment.

[0019] Furthermore, if the abnormal sound detection value and the abnormal vibration detection value do not exceed the corresponding preset threshold value, the operating parameters that need to be monitored by the power equipment are obtained, and the real-time detection data of the corresponding operating parameters (such as current, current, power, energy consumption, etc.) are collected. The real-time detection data of the corresponding operating parameters are compared with the corresponding preset data requirements. If there are operating parameters that do not meet the corresponding preset data requirements, indicating that the power parameters of the corresponding power equipment are relatively abnormal, SK-1 is assigned to the corresponding power equipment; if the real-time detection data of all operating parameters of the corresponding power equipment meet the corresponding preset data requirements, indicating that the operation of the corresponding power equipment is relatively safe, SK-2 is assigned to the corresponding power equipment.

[0020] Embodiment 3: Figure 2 As shown, the difference between this embodiment and embodiment 1 and embodiment 2 is that the power operation analysis module is connected to the power line monitoring module in communication, and the power line monitoring module obtains all power lines that need to be monitored, monitors the corresponding power lines, and allocates line symbols XK-1 or XK-2 to the corresponding power lines through analysis; The line symbol of the corresponding power line is sent to the power operation comprehensive analysis module, which can not only reasonably analyze and accurately feedback the safety hazard status of each power line to remind timely inspection of the corresponding power line and other treatment measures, but also provide information support for the analysis process of the power operation comprehensive analysis module to further ensure the accuracy of its analysis results; the analysis process of the power line monitoring module is as follows: The voltage and current at several positions on the corresponding power line are obtained, the voltage at all positions is calculated by variance to obtain a voltage stability judgment value, and the current at all positions is calculated by variance to obtain a current stability judgment value; The voltage stability judgment value and the current stability judgment value are respectively compared numerically with the corresponding preset voltage stability judgment threshold and the preset current stability judgment threshold. If the voltage stability judgment value or the current stability judgment value exceeds the corresponding preset threshold, it indicates that the voltage or current on the corresponding power line is uneven, and the potential safety hazard existing in the corresponding power line is relatively large. Then, the line symbol XK-1 is assigned to the corresponding power line; If both the voltage stability judgment value and the current stability judgment value do not exceed the corresponding preset threshold, the deviation value of the average value of the voltages at all positions compared with the corresponding preset appropriate voltage standard value is marked as the voltage deviation judgment value, and the deviation value of the average value of the currents at all positions compared with the corresponding preset appropriate current standard value is marked as the current deviation judgment value; The voltage deviation judgment value and the current deviation judgment value are respectively compared numerically with the corresponding preset voltage deviation judgment threshold and the preset current deviation judgment threshold. If the voltage deviation judgment value or the current deviation judgment value exceeds the corresponding preset threshold, it indicates that the deviation degree of the voltage or current on the corresponding power line is relatively large, and the potential safety hazard existing in the corresponding power line is relatively large. Then, the line symbol XK-1 is assigned to the corresponding power line.

[0021] Furthermore, if both the voltage deviation judgment value and the current deviation judgment value do not exceed the corresponding preset threshold, the ratio of the power output from the corresponding power line to the power input to the corresponding power line within a unit time is calculated to obtain the power transmission inspection value. The real-time temperature at all positions on the corresponding power line is collected, the positions where the real-time temperature exceeds the preset real-time temperature threshold are marked as abnormal positions, the ratio of the number of abnormal positions on the corresponding power line is obtained and marked as the abnormal position detection value, and the average value of the real-time temperatures at all positions is calculated to obtain the line temperature detection value; The power transmission inspection value QX, the abnormal position detection value QK, and the line temperature detection value QM are numerically calculated through the formula W = uy1*QX + uy2*QK + uy3*QM / uy2 to obtain the power line evaluation value W; where uy1, uy2, and uy3 are preset proportionality coefficients greater than zero, and the larger the numerical value of the power line evaluation value W, the worse the overall operation performance of the corresponding power line and the greater the operation risk; The power line evaluation value W is numerically compared with the corresponding preset power line evaluation threshold. If the power line evaluation value W exceeds the preset power line evaluation threshold, it indicates that the operation performance of the corresponding power line is poor and the operation risk is relatively large. Then, the line symbol XK-1 is assigned to the corresponding power line; if the power line evaluation value W does not exceed the preset power line evaluation threshold, it indicates that the overall operation performance of the corresponding power line is good. Then, the line symbol XK-2 is assigned to the corresponding power line.

[0022] Working principle of the present invention: During use, the comprehensive analysis module operates through electricity to monitor and analyze all power equipment and power lines to determine whether power operation warning information is generated. When power operation warning information is generated, corresponding improvement measures are taken for the power system in a timely manner, realizing effective monitoring of power operation and rapid warning feedback, ensuring the safe and stable operation of the power system. And through the time period risk decision-making module, time period risk decision-making analysis is carried out to mark the corresponding time period as a high-risk time period or an easy-to-manage time period, which can reasonably analyze the power operation risk of each time period and accurately divide its risk level, helping to adopt appropriate management plans and supervision intensities for power operation in different time periods, making power operation management more scientific. And through the planning execution judgment module to analyze the current personnel planning execution status, it can accurately judge the execution performance status of the management plan for power operation in real time, and optimize the adjustment of management personnel and personnel supervision when an execution abnormality signal is generated, further ensuring the safety of power operation and having a high degree of intelligence.

[0023] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain a formula closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, many modifications and changes can be made according to the content of this specification. These embodiments are selected and specifically described in this specification to better explain the principle and practical application of the present invention, so that those skilled in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. An artificial intelligence-based power operation remote monitoring and early warning system, characterized in that: It includes power operation comprehensive analysis module, time period risk decision module, planning execution judgment module and remote early warning terminal; The power operation analysis module obtains the equipment symbols of all power equipment that need to be monitored and the line symbols of all power lines that need to be monitored. If there is a power equipment corresponding to the equipment symbol SK-1 or a power line corresponding to the line symbol XK-1, it generates power operation warning information and sends the power operation warning information to the remote warning terminal; The time period risk decision module is used to set eight time periods every day, each of which lasts for three hours. Through the time period risk decision analysis, the corresponding time period is marked as a risky time period or an easy-to-manage time period, and the marking information of all time periods is sent to the remote warning terminal; The planning execution judgment module is used to determine the current time period, analyze and judge the current personnel planning execution status, and generate an execution exception signal or an execution qualification signal accordingly, and send the execution exception signal to the remote early warning terminal. When the remote early warning terminal receives the execution exception signal, it will issue a corresponding early warning.

2. According to claim 1, an artificial intelligence-based power operation remote monitoring and early warning system is characterized in that: The power operation analysis module is connected to the power equipment monitoring module and the power line monitoring module in communication. The power equipment monitoring module obtains all the power equipment that needs to be monitored, monitors the corresponding power equipment, allocates equipment symbols SK-1 or SK-2 to the corresponding monitoring equipment through analysis, and sends the equipment symbols of the corresponding power equipment to the power operation analysis module; The power line monitoring module obtains all power lines that need to be monitored, monitors the corresponding power lines, assigns line symbols XK-1 or XK-2 to the corresponding power lines through analysis, and sends the line symbols of the corresponding power lines to the power operation analysis module.

3. According to claim 2, an artificial intelligence-based power operation remote monitoring and early warning system is characterized in that: The analysis process of the power equipment monitoring module is as follows: Collect smoke density data and internal temperature data of the environment in which the corresponding power equipment is located. If the smoke density data or the internal temperature data exceeds the corresponding preset threshold, SK-1 is allocated to the corresponding power equipment; If the smoke concentration data and the internal temperature data do not exceed the corresponding preset thresholds, the decibel value of the abnormal sound and the amplitude value of the vibration generated by the corresponding electrical equipment are obtained and marked as the abnormal sound detection value and the abnormal vibration detection value respectively. If the abnormal sound detection value or the abnormal vibration detection value exceeds the corresponding preset threshold, SK-1 is assigned to the corresponding electrical equipment.

4. According to claim 3, an artificial intelligence-based power operation remote monitoring and early warning system is characterized in that: If both the abnormal sound detection value and the abnormal vibration detection value do not exceed the corresponding preset threshold value, the operating parameters that need to be monitored by the power equipment are obtained, the real-time detection data of the corresponding operating parameters are collected, and the real-time detection data of the corresponding operating parameters are compared with the corresponding preset data requirements. If there are operating parameters that do not meet the corresponding preset data requirements, SK-1 is assigned to the corresponding power equipment; if the real-time detection data of all operating parameters of the corresponding power equipment meet the corresponding preset data requirements, SK-2 is assigned to the corresponding power equipment.

5. According to claim 2, an artificial intelligence-based electric power operation remote monitoring and early warning system is characterized in that: The analysis process of the power line monitoring module is as follows: The voltage and current at several positions on the corresponding power line are obtained, the voltage at all positions are calculated by variance to obtain a voltage stability judgment value, and the current at all positions are calculated by variance to obtain a current stability judgment value. If the voltage stability judgment value or the current stability judgment value exceeds the corresponding preset threshold value, the line symbol XK-1 is assigned to the corresponding power line; If both the voltage stability judgment value and the current stability judgment value do not exceed the corresponding preset threshold value, the deviation value of the mean voltage at all positions compared to the corresponding preset appropriate voltage standard value is marked as the voltage deviation judgment value, and the deviation value of the mean current at all positions compared to the corresponding preset appropriate current standard value is marked as the current deviation judgment value. If the voltage deviation judgment value or the current deviation judgment value exceeds the corresponding preset threshold value, the line symbol XK-1 is assigned to the corresponding power line.

6. According to claim 5, an artificial intelligence-based electric power operation remote monitoring and early warning system is characterized in that: If the voltage deviation judgment value and the current deviation judgment value do not exceed the corresponding preset threshold value, the power line judgment value is obtained by numerically calculating the power transmission detection value, the out-of-position detection value and the line temperature detection value. If the power line judgment value exceeds the preset power line judgment threshold value, the line symbol XK-1 is allocated to the corresponding power line; If the power line evaluation value does not exceed the preset power line evaluation threshold, the line symbol XK-2 is allocated to the corresponding power line.

7. According to claim 1, an artificial intelligence-based power operation remote monitoring and early warning system is characterized in that: The specific analysis process of time period risk decision analysis is as follows: Take the current date as the end date and trace back L1 days, and mark the interval between the current date and the previous L1 day as the tracing period; collect the number of times the power operation warning information is generated in the corresponding time period of the corresponding date in the tracing period and mark it as the power operation alarm value, calculate the average of all power operation alarm values ​​of the corresponding time period in the tracing period to obtain the initial risk judgment value of the time period, and if the initial risk judgment value of the time period exceeds the preset initial risk judgment threshold of the time period, mark the corresponding time period as a risky time period; If the initial risk judgment value of the time period does not exceed the preset initial risk judgment threshold of the time period, the power operation warning value of the corresponding time period in the corresponding date is compared with the preset power operation warning threshold. If the power operation warning value exceeds the preset power operation warning threshold, the corresponding date is marked as a risky date; the number of risky dates corresponding to the corresponding time period in the retrospective period is obtained and marked as the risky date table value, and the power operation warning value with the largest value corresponding to the corresponding time period in the retrospective period is marked as the power operation risk amplitude; The period risk decision value is obtained by numerically calculating the initial judgment value of the period risk, the easy-risk daily meter value and the power operation risk amplitude. If the period risk decision value exceeds the preset period risk decision threshold, the corresponding period is marked as an easy-risk period. If the risk decision value of a time period does not exceed the preset risk decision threshold of the time period, the corresponding time period will be marked as an easy-to-manage time period.

8. The artificial intelligence-based electric power operation remote monitoring and early warning system according to claim 1 is characterized in that: The specific analysis process of the planning execution judgment module includes: The current time period is obtained and marked as a decision-making period. The optimal manager is determined through evaluation and analysis by management personnel. The executable detection value is obtained by numerically calculating the power manager inspection value, the optimal power manager inspection value and the manager status value. If the decision period is a risky period, a preset executable detection threshold FY1 is assigned to it. If the decision period is an easy-to-manage period, a preset executable detection threshold FY2 is assigned to it, and FY1>FY2>0; if the executable detection value exceeds the corresponding preset executable detection threshold, a normal executable signal is generated; if the executable detection value does not exceed the corresponding preset executable detection threshold, an abnormal executable signal is generated.

9. The artificial intelligence-based electric power operation remote monitoring and early warning system according to claim 8 is characterized in that: The specific analysis process of management personnel evaluation analysis is as follows: The manager evaluation is obtained by numerically calculating the power operation and management time inspection value, the power execution delay value and the power management error frequency value. If the manager evaluation exceeds the preset manager evaluation threshold, the corresponding manager will be marked as an excellent manager.

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