Early warning method and system of electricity marketing system based on artificial intelligence

Abstract

The invention relates to the technical field of electric power marketing early warning data analysis, and particularly discloses an early warning method and system of an electric power marketing system based on artificial intelligence. Reference data basis is provided for power utilization early warning of power demand equipment, the power operation efficiency is improved, the power demand operation cost is reduced, meanwhile, potential faults and abnormalities can be found in time by monitoring the power supply equipment, an electric power early warning mechanism carries out early warning reminding in time, and the power utilization early warning efficiency is improved. An alarm is provided before a potential problem becomes an actual problem, thereby reducing accidental power failure caused by insufficient power supply, and improving the supply efficiency of power supply equipment.

Description

Technical Field

[0001] The present invention relates to the technical field of power marketing early warning data analysis, and in particular to an early warning method and system for a power marketing system based on artificial intelligence. Background Art

[0002] At present, with the continuous expansion of the scale of the power system, the power marketing system needs to be further developed to adapt to the growing power demand and complexity. However, with the expansion of various equipment in the power marketing system, if the supply and demand of equipment in the power marketing are not determined in time by the equipment, the early warning function of the power marketing system will be inaccurate, affecting the reliability and efficiency of the equipment operation in the power marketing system.

[0003] For example, the invention patent with announcement number CN116401895B announces an information processing method and system for a power marketing management system, which determines the preset equipment image of the power equipment corresponding to the construction scanning information as the completed image, and configures the completed image at the corresponding equipment slot in the initial progress twin space to generate a construction progress twin space; when it is judged that the current time point is equal to the preset construction period point, the standard progress twin space and the construction progress twin space are compared to generate progress reminder information; the power equipment is classified to obtain fixed equipment and line equipment, and image collection is performed at a fixed geographical location to obtain the fixed equipment image corresponding to the fixed equipment, and image collection is performed at the midpoint of the line to obtain the midpoint image of the line corresponding to the line equipment; based on the equipment attributes of each power equipment, the first verification strategy of the fixed equipment and the second verification strategy of the line equipment are determined, and the fixed equipment image is verified based on the first verification strategy to obtain a first verification result.

[0004] For example, the invention patent with announcement number CN115378143B announces an intelligent power marketing audit system, including a power information input module, a power information update module, a power loss estimation module, a power information statistics module and a server. The power information input module, the power information update module, the power loss estimation module and the power information statistics module are respectively connected to the server data; during the power audit process, images of power consumption information and line inspection are uploaded, the received power values ​​are counted, and circuit abnormalities are analyzed and judged based on the power information. Then, effective processing is performed based on the judgment information, thereby improving the audit effect.

[0005] Based on the above solution, it is found that the current power marketing system management solution not only uses relatively intuitive and single data, but also cannot guarantee the accuracy of the data, which will cause a large difference between the final judgment result and the actual result, affecting the data processing behavior of the power marketing system. At the same time, it will cause incorrect operation adjustments of equipment related to the power marketing system. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides an early warning method and system for an electric power marketing system based on artificial intelligence, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides an early warning method for an electric power marketing system based on artificial intelligence, including: electric power demand analysis: obtaining electric power demand data in the electric power marketing system, and performing preprocessing operations on the electric power demand data, recording the electric power demand data after the preprocessing operation as the electric power consumption data of each electric power demand device, and obtaining the electric power consumption indicators of each electric power demand device by analyzing the electric power consumption data of each electric power demand device.

[0008] Power equipment operation monitoring: monitor the power operation status of each power supply equipment in the power marketing system, obtain the operation data of each power supply equipment, and obtain the power supply indicators of each power supply equipment by analyzing the operation data of each power supply equipment.

[0009] Intelligent power early warning: compare the power consumption index of each power demand device with the power consumption index threshold value preset in the power database, and obtain the power consumption index of each power demand device greater than the preset power consumption index threshold value, and issue an intelligent early warning to each power demand device through the power early warning mechanism in the power marketing system. At the same time, compare the power supply index of each power supply device with the power supply index boundary value preset in the power database, and obtain the power supply index of each power supply device greater than the preset power supply index boundary value, and issue an intelligent early warning to each power supply device through the power early warning mechanism in the power marketing system.

[0010] As a further method, the power demand data is preprocessed, and the specific preprocessing operations include removing erroneous values, removing outliers, processing missing values, smoothing noise data and data standardization, thereby completing the preprocessing operation of the power demand data, and the power demand data after the preprocessing operation is completed is recorded as power demand data.

[0011] As a further method, the power consumption data of each power demand device specifically includes the number of times each power demand device is used within the power operation cycle, and the useful power and apparent power corresponding to each power demand device at each time point within the power operation cycle.

[0012] As a further method, the specific analysis process of the power demand indicator is as follows:

[0013] The usage frequency and maximum power factor of each power demand equipment during the power operation cycle are comprehensively analyzed to obtain the power demand index.

[0014] As a further method, the power supply index of each power supply device is specifically analyzed as follows:

[0015] The operating voltage and operating current corresponding to each power supply equipment at each time point in the power operation cycle are analyzed through extreme value analysis to obtain the maximum operating voltage and maximum operating current of each power supply equipment in the power operation cycle.

[0016] The maximum operating power of each power supply device during the power operation cycle is obtained by multiplying the maximum operating voltage of each power supply device during the power operation cycle by the maximum operating current.

[0017] The maximum electric power of each power supply device during the power operation cycle is processed with the rated power of each power supply device preset in the power database through ratio analysis to obtain the operating load rate of each power supply device during the power operation cycle.

[0018] The operating temperature corresponding to each power supply equipment at each time point in the power operation cycle is analyzed through extreme value analysis to obtain the maximum operating temperature of each power supply equipment in the power operation cycle.

[0019] The operating load rate, the maximum operating temperature and the total operating time period of each power supply equipment in the power operation cycle are comprehensively analyzed to obtain the power supply index of each power supply equipment. The specific analysis formula is:

[0020]

[0021] In the formula, H i is the power supply index of the i-th power supply device, i is the number of each power supply device, i=1, 2, 3...y, y is the total number of power devices, u i is the operating load rate of the ith power equipment in the power operation cycle, b1 is the influencing factor of the unit value of the operating load rate preset in the power database, and w i is the maximum operating temperature of the ith power equipment during the power operation cycle, Δw is the power equipment operating reference temperature preset in the power database, b2 is the correction factor corresponding to the maximum operating temperature preset in the power database, ty i is the total operation time period of the ith power equipment in the power operation cycle, and b3 is the influencing factor of the unit value of the total operation time period preset in the power database.

[0022] As a further method, the intelligent early warning is performed on each power demand device, and the specific early warning process is:

[0023] The power consumption index of each power demand device is compared with the power consumption index threshold preset in the power database. If the power consumption index of a power demand device is greater than the preset power consumption index threshold, the power demand device is recorded as a demand device requiring early warning. Thus, the power demand devices corresponding to the power consumption index greater than the preset power consumption index threshold are statistically obtained and recorded as demand devices requiring early warning. Intelligent early warning is performed on each demand device requiring early warning through the power early warning mechanism in the power marketing system.

[0024] If the power consumption index of a certain power demand device is less than or equal to the power consumption demand index threshold preset in the power database, there is no need to provide intelligent early warning for the power demand device.

[0025] As a further method, the intelligent early warning is performed on each power supply device, and the specific early warning process is:

[0026] The power supply index of each power supply device is compared with the power supply index limit value preset in the power database. If the power supply index of a certain power supply device is greater than the preset power supply index limit value, the power supply device is recorded as a supply device requiring early warning. The power supply devices whose power supply index is greater than the preset power supply index limit value are statistically recorded as supply devices requiring early warning. Intelligent early warning is performed on each supply device requiring early warning through the power early warning mechanism in the power marketing system.

[0027] If the power supply index of a certain power supply equipment is less than or equal to the power supply index limit value preset in the power database, there is no need to provide intelligent early warning for the power supply equipment.

[0028] The second aspect of the present invention provides a system for an early warning method of an electric power marketing system based on artificial intelligence, including: an electric power demand analysis module, used to obtain electric power demand data in the electric power marketing system, and perform preprocessing operations on the electric power demand data, record the electric power demand data after the preprocessing operation as the electric power consumption data of each electric power demand device, and obtain the electric power consumption index of each electric power demand device by analyzing the electric power consumption data of each electric power demand device.

[0029] The power equipment operation monitoring module is used to monitor the power operation status of each power supply equipment in the power marketing system, obtain the operation data of each power supply equipment, and obtain the power supply indicators of each power supply equipment by analyzing the operation data of each power supply equipment.

[0030] The power intelligent early warning module is used to compare the power consumption index of each power demand device with the power consumption index threshold value preset in the power database, and statistically obtain the power demand devices whose power consumption index is greater than the preset power consumption index threshold value, and perform intelligent early warning on each power demand device through the power early warning mechanism in the power marketing system. At the same time, the power supply index of each power supply device is compared with the power supply index boundary value preset in the power database, and statistically obtain the power supply index of each power supply device that is greater than the preset power supply index boundary value, and perform intelligent early warning on each power supply device through the power early warning mechanism in the power marketing system.

[0031] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0032] (1) The present invention can ensure the quality of power demand data by preprocessing the power demand data, thereby improving the accuracy and reliability of subsequent power data analysis. By analyzing the power demand data, it can help identify the operating conditions in the power demand equipment, optimize the resource allocation of the power demand equipment, improve the operating efficiency of the power demand equipment, reduce unnecessary power waste, and improve the operating efficiency of each power demand equipment.

[0033] (2) By monitoring the power operation status of each power supply device, the present invention can timely discover potential faults and abnormalities, so as to take measures to prevent the occurrence of faults, optimize the workload of the equipment, reduce unnecessary wear, and help the power marketing system to more accurately warn the power supply equipment.

[0034] (3) The present invention uses an electric power early warning mechanism to warn the electric power demand equipment and the electric power supply equipment in the electric power marketing system. The early warning mechanism can provide warnings before potential problems become actual problems, thereby reducing unexpected power outages caused by insufficient power supply and unexpected power outages caused by excessive power demand. The early warning mechanism can adjust the resource allocation of the electric power equipment according to the power demand of the electric power demand equipment and the supply situation of the electric power supply equipment, so that the resources of the electric power equipment can be more effectively utilized, thereby improving the operating efficiency of the electric power equipment in the electric power marketing system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0036] Figure 1 The figure is a schematic flow chart of the method steps of the present invention.

[0037] Figure 2It is a schematic diagram of system module connection of the present invention.

[0038] Figure 3 It is a simulation curve diagram between the operating time and the operating temperature of the equipment involved in the present invention. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only 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 ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0040] Reference Figure 1 As shown, the first aspect of the present invention provides an early warning method for an electric power marketing system based on artificial intelligence, including: electric power demand analysis: acquiring electric power demand data in the electric power marketing system, and performing preprocessing operations on the electric power demand data, recording the electric power demand data after the preprocessing operation as the electric power consumption data of each electric power demand device, and obtaining the electric power consumption index of each electric power demand device by analyzing the electric power consumption data of each electric power demand device.

[0041] In this embodiment, the above-mentioned electricity demand data can be analyzed by artificial intelligence technology, which can deeply explore the hidden rules and characteristics in the electricity demand data. Based on these rules, artificial intelligence technology can predict future electricity demand trends and issue early warning signals in time when demand changes exceed the normal range, which helps power companies to prepare in advance and deal with potential supply and demand imbalances.

[0042] Power equipment operation monitoring: monitor the power operation status of each power supply equipment in the power marketing system, obtain the operation data of each power supply equipment, and obtain the power supply indicators of each power supply equipment by analyzing the operation data of each power supply equipment.

[0043] In this embodiment, the operating data of the above-mentioned power supply equipment can be analyzed through artificial intelligence technology, and the potential failure risks in the equipment can be identified through in-depth analysis of the power equipment operating data (such as temperature, current, voltage data, etc.). At the same time, artificial intelligence technology can process and analyze massive amounts of power equipment operating data and complete complex data processing tasks without human intervention, which greatly improves the efficiency and accuracy of data processing and reduces the risk of human errors.

[0044] Intelligent power early warning: compare the power consumption index of each power demand device with the power consumption index threshold value preset in the power database, and obtain the power consumption index of each power demand device greater than the preset power consumption index threshold value, and issue an intelligent early warning to each power demand device through the power early warning mechanism in the power marketing system. At the same time, compare the power supply index of each power supply device with the power supply index boundary value preset in the power database, and obtain the power supply index of each power supply device greater than the preset power supply index boundary value, and issue an intelligent early warning to each power supply device through the power early warning mechanism in the power marketing system.

[0045] It needs to be explained that the above-mentioned power marketing system is an information management system for the power industry, and its functions include multiple aspects, including but not limited to the equipment information management subsystem, which is used to process the registration of new equipment, equipment power consumption record query, equipment power supply record query, equipment service request processing, etc.; the user information management subsystem, including generating user electricity bills, generating equipment power supply bills, sending reminders, sending equipment power consumption warning notifications, sending equipment power supply warning notifications, etc.; the present invention only describes the equipment power consumption warning notification and equipment power supply warning notification parts in the power marketing system. In this embodiment, according to the built-in power warning mechanism in the power marketing system, by issuing a warning signal, the power warning mechanism performs intelligent warning on each power demand device or each power supply device.

[0046] Specifically, a preprocessing operation is performed on the power demand data, and the specific preprocessing operation includes removing erroneous values, removing outliers, processing missing values, smoothing noise data and data standardization, thereby completing the preprocessing operation of the power demand data, and the power demand data after the preprocessing operation is completed is recorded as power demand data.

[0047] It should be explained that the above-mentioned preprocessing operation on the power demand data, in which the removal of erroneous values ​​can be done by using statistical methods to identify possible erroneous values, for example, calculating the mean, standard deviation and other statistics of the data, and then identifying the extreme values ​​far away from these statistics, which may be erroneous values. After identifying the erroneous values, the erroneous values ​​are deleted to remove them, so as to prevent the erroneous values ​​from misleading the data analysis results and ensure the accuracy of the analysis results. Statistical methods (such as box plots, Z-score method, four-point difference method, etc.) can be used to remove outliers in the data, delete records containing outliers, reduce the interference of outliers on the data analysis results, and improve the stability and reliability of the analysis results. Missing values ​​can be processed by interpolation techniques (such as mean and median filling), prediction models (such as regression, K nearest neighbor Algorithms), or delete records with missing values. Missing values ​​may lead to incomplete data analysis. Appropriate processing can maintain the integrity and consistency of the data. Noise data may mask real signals. Smoothing helps to highlight trends and patterns. Smoothing noisy data can be done by using filters (such as moving average, sliding window average), or more advanced techniques such as Fourier transform or wavelet transform to reduce random fluctuations in the data. Data standardization scales the data to a standard range, usually 0 to 1 (minimum-maximum standardization) or converts it to a distribution with unit variance (Z-score standardization). The data is standardized so that data of different dimensions and scales can be compared together. The above series of preprocessing operations can ensure the quality of power demand data, thereby improving the accuracy and reliability of subsequent power data analysis.

[0048] Specifically, the power consumption data of each power demand device includes the number of times each power demand device is used within the power operation cycle, and the useful power and apparent power corresponding to each power demand device at each time point within the power operation cycle.

[0049] It needs to be explained that the above-mentioned power operation cycle refers to the period used to observe, measure and analyze the power operation conditions in the power demand area over a period of time. It is randomly arranged according to the power operation conditions, and each time point within the power operation cycle is randomly set within the power operation cycle.

[0050] As mentioned above, the number of times each power demand device is used during the power operation cycle can be obtained by the actual number of times each power demand device is used recorded by the smart terminal device, the useful power corresponding to each power demand device at each time point during the power operation cycle can be monitored by the smart meter to obtain the useful power, and the apparent power corresponding to each power demand device at each time point during the power operation cycle can be measured by a voltmeter and an ammeter, the effective values ​​of voltage and current are measured, and then multiplied to obtain the apparent power.

[0051] Furthermore, the power consumption data of each power demand device is analyzed. The specific analysis process is as follows:

[0052] The number of times each power demand device is used during the power operation cycle is divided by the duration corresponding to the power operation cycle to obtain the usage frequency of each power demand device during the power operation cycle.

[0053] The useful power and apparent power corresponding to each power demand device at each time point in the power operation cycle are ratio-processed to obtain the power factor corresponding to each power demand device at each time point in the power operation cycle.

[0054] The power factor corresponding to each power demand device at each time point in the power operation cycle is analyzed to obtain the maximum power factor of each power demand device in the power operation cycle.

[0055] Specifically, the power consumption indicators of each power demand equipment are analyzed as follows:

[0056] The usage frequency and maximum power factor of each power demand equipment in the power operation cycle are comprehensively analyzed to obtain the power consumption index of each power demand equipment. The specific analysis formula is:

[0057]

[0058] Where D i is the power consumption index of the ith power demand device. When the usage frequency of the power demand device is higher than the reference usage frequency of the power demand device preset in the power database, it means that the power demand device will operate more frequently, which directly leads to an increase in the overall load of the power system. High load may cause overload of the power grid, thereby affecting the stability and safety of power supply. At the same time, when the usage frequency of the power demand device increases, the instantaneous power at each startup will increase, thereby affecting the maximum power factor. Frequent start and stop may make the equipment unable to operate in the best state, resulting in a decrease in the power factor. When the maximum power factor is lower than the reference power factor of the power demand device, it means that the proportion of reactive power of the power demand device increases. The increase in reactive power will cause the total current of the power system to increase, thereby increasing line loss and equipment loss. Therefore, by analyzing the various data in the power consumption index of each power demand device, it is possible to identify the power demand status of the power demand device, improve the operating efficiency of the power demand device, and adjust the resource allocation of the power demand device according to the operating status.

[0059] In this embodiment, the power consumption index of each power demand device is obtained by nonlinearly transforming the usage frequency and maximum power factor of each power demand device during the power operation cycle. The nonlinear transformation can capture the complex nonlinear relationship between the usage frequency and maximum power factor of each power demand device during the power operation cycle. At the same time, the nonlinear transformation can reduce the impact of extremely large or extremely small data points, thereby obtaining a more accurate and robust power consumption index for each power demand device.

[0060] i is the serial number of each power supply device, i=1, 2, 3...y, y is the total number of power devices.

[0061] q i is the usage frequency of the ith power demand device in the power operation cycle, which refers to the number of times the device is activated or operated in a specific time period.

[0062] Δq is the reference usage frequency of the power demand equipment preset in the power database, which refers to the standard or average usage frequency set for various power demand equipment in the set power database.

[0063] a1 is the correction factor corresponding to the usage frequency of the power demand equipment preset in the power database, which refers to the degree of influence of the change in the usage frequency of the power demand equipment on the power demand index. The fitting curve of the usage frequency of the power demand equipment can be obtained by fitting the corresponding relationship between the historical usage times of the power demand equipment and the usage frequency of the power demand equipment. The real-time usage times of the power demand equipment are brought into the fitting curve of the usage frequency of the power demand equipment, thereby obtaining the correction factor corresponding to the usage frequency of the power demand equipment in this embodiment, and the value range is (0, 1).

[0064] t i It is the maximum power factor of each power demand device during the power operation cycle, which refers to the maximum efficiency index that each power demand device can achieve during the power operation cycle.

[0065] Δt is a reference power factor of the power demand equipment preset in the power database, which refers to a standard or expected power factor value set for various power demand equipment.

[0066] a2 is the correction factor corresponding to the power factor preset in the power database, which refers to the degree of influence of the power factor change on the power demand index. The fitting curve corresponding to the power factor can be fitted through the correspondence between the historical useful power, the historical apparent power and the power factor. The real-time useful power and apparent power are brought into the fitting curve corresponding to the power factor, thereby obtaining the correction factor corresponding to the power factor in this embodiment, and the value range is (0, 1).

[0067] In this embodiment, the reference usage frequency of the power demand equipment preset in the power database is set to 60%, the reference power factor of the power demand equipment is set to 0.6, the correction factor corresponding to the usage frequency of the power demand equipment is set to 0.4, and the correction factor corresponding to the power factor is set to 0.6. Thus, a corresponding table of the relationship between the power demand index and the usage frequency and the maximum power factor of each power demand equipment in the power operation cycle in this embodiment can be obtained, as shown in Table 1:

[0068] Table 1 Relationship between power demand index and various parameters

[0069] serial number Frequency of use % Maximum power factor % Electricity demand index% 1 80 90 36 2 50 80 23.64 3 90 50 26.23

[0070] By observing the data in Table 1, which shows the relationship between the power demand index and various parameters, it is found that the power demand index will change according to the changes in the frequency of use and the maximum power factor. As the frequency of use and the maximum power factor change, the power demand index continues to increase.

[0071] Specifically, the operating data of each power supply equipment includes the operating voltage and operating current of each power supply equipment at each time point in the power operation cycle, the total operating time period of each power supply equipment in the power operation cycle, and the operating temperature of each power supply equipment at each time point in the power operation cycle.

[0072] It needs to be explained that the operating voltage and operating current of the above-mentioned power supply equipment during the power operation cycle can be obtained through online monitoring technology and intelligent sensors, the total operating time period of each power supply equipment during the power operation cycle can be obtained by recording the operating time of the equipment in the power system's operation log, and the operating temperature of each power supply equipment during the power operation cycle can be obtained by detecting infrared rays radiated from the surface of the equipment.

[0073] Furthermore, the specific analysis process of the power supply indicators of each power supply device is as follows:

[0074] The operating voltage and operating current corresponding to each power supply equipment at each time point in the power operation cycle are analyzed through extreme value analysis to obtain the maximum operating voltage and maximum operating current of each power supply equipment in the power operation cycle.

[0075] The maximum operating power of each power supply device during the power operation cycle is obtained by multiplying the maximum operating voltage of each power supply device during the power operation cycle by the maximum operating current.

[0076] The maximum electric power of each power supply device during the power operation cycle is processed with the rated power of each power supply device preset in the power database through ratio analysis to obtain the operating load rate of each power supply device during the power operation cycle.

[0077] It should be explained that the rated power of each power supply device preset in the above-mentioned power database refers to the maximum power level at which the equipment can continue to operate safely under normal operating conditions, and the power of each device can be directly measured by professional power measuring instruments.

[0078] The operating temperature corresponding to each power supply equipment at each time point in the power operation cycle is analyzed through extreme value analysis to obtain the maximum operating temperature of each power supply equipment in the power operation cycle.

[0079] The operating load rate, the maximum operating temperature and the total operating time period of each power supply equipment in the power operation cycle are comprehensively analyzed to obtain the power supply index of each power supply equipment. The specific analysis formula is:

[0080]

[0081] In the formula, H i is the power supply index of the ith power supply equipment. With the increase of the load rate, power equipment such as generators and transformers will be in a high-load operation state for a long time, which will lead to increased wear of mechanical parts and electrical components inside the equipment and shorten the service time period of the equipment. High-load operation will also cause the equipment to generate more heat. If the heat dissipation system cannot dissipate the heat in time, the equipment temperature will continue to rise, thereby increasing heat loss and possibly causing equipment overheating failure, further shortening the total operation time period of the power equipment in the power operation cycle. Therefore, by analyzing various data of the power supply indicators of the power supply equipment, identifying abnormal operation modes and potential risk points, when the operating load rate of the power equipment is abnormal, the maximum temperature is too high, or the operation time exceeds the normal range, the power system can issue an early warning to help power companies take timely measures to avoid the occurrence or expansion of failures, ensure the load balance and stable operation of the power grid, and help reduce grid losses and improve energy efficiency.

[0082] In this embodiment, there is a certain nonlinear relationship between the operating load rate, the maximum operating temperature and the total operating time period of each power supply equipment within the power operation cycle. The operating indicators of the power supply equipment are obtained by nonlinearly transforming the operating load rate, the maximum operating temperature and the total operating time period of each power supply equipment within the power operation cycle. This can effectively shorten the calculation time, reduce the complexity and dimension of the data to a certain extent, and make data analysis more intuitive. Smaller operating indicator values ​​may be masked by larger values. The use of nonlinear transformation can enhance the sensitivity and weight of these small values, reduce the relative differences between data, and help to compare power equipment of different sizes more fairly.

[0083] i is the serial number of each power supply device, i=1, 2, 3...y, y is the total number of power devices.

[0084] u i is the operating load rate of the ith power equipment during the power operation cycle, which refers to the ratio between the actual load borne by the equipment and its rated load.

[0085] b1 is the influencing factor of the unit value of the operating load rate preset in the power database, which refers to the degree of influence of the change in the operating load rate on the power supply index of the power supply equipment. The corresponding relationship between the historical electric power of the power equipment and the load rate can be used to fit the fitting curve corresponding to the load rate. The real-time electric power of the power equipment is brought into the fitting curve corresponding to the load rate, thereby obtaining the influencing factor of the unit value of the operating load rate in this embodiment, and the value range is (0, 1).

[0086] w i is the maximum operating temperature of the ith power equipment during the power operation cycle, which refers to the highest operating temperature reached by the equipment during the entire power operation cycle (which may include various links such as power generation, transmission, and distribution).

[0087] Δw is the operating reference temperature of the power equipment preset in the power database, which refers to the ideal operating temperature of the power equipment.

[0088] b2 is the correction factor corresponding to the maximum operating temperature preset in the power database, which refers to the degree of influence of the change in the maximum operating temperature on the power supply index of the power supply equipment. The fitting curve corresponding to the operating temperature can be fitted through the correspondence between the historical ambient temperature of the equipment and the operating temperature. The real-time ambient temperature of the equipment is brought into the fitting curve corresponding to the operating temperature to obtain the correction factor corresponding to the maximum operating temperature in this embodiment, and the value range is (0, 1).

[0089] ty iis the total operating time period of the ith power equipment in the power operation cycle, which refers to the total length of time from the start of operation to the current operation of the equipment.

[0090] b3 is the influencing factor of the unit value of the total operating time period preset in the power database, which refers to the degree of influence of the change of the total operating time period on the power supply index of the power supply equipment. The fitting curve of the total operating time period can be obtained by fitting the correspondence between the historical operating temperature of the equipment and the total operating time period. The real-time operating temperature of the equipment is brought into the fitting curve of the total operating time period to obtain the influencing factor of the unit value of the total operating time period in this embodiment, and the value range is (0, 1).

[0091] In this embodiment, the influence factor of the above-mentioned operation load rate unit value is set to 0.3, the power equipment operation reference temperature is 35 degrees Celsius, the correction factor corresponding to the maximum operation temperature is 0.6, and the influence factor of the unit value of the total operation time period is 0.4. This can obtain the relationship between the power supply index of each power supply equipment and the operation load rate, the maximum operation temperature and the total operation time period of each power supply equipment in the power operation cycle in this embodiment. The relationship between the power supply index and each parameter of each power supply equipment is shown in Table 2:

[0092] As shown in Table 2, the relationship between the power supply indicators and parameters of each power supply equipment

[0093]

[0094] By observing the data in the power supply indicators of each power supply equipment and the relationship table of each parameter in Table 2, it is found that the power supply equipment operation indicators of the power supply equipment will change according to the changes in the operation load rate, the maximum operation temperature and the total operation time period of each power supply equipment during the power operation cycle.

[0095] In this embodiment, generally, as the total operating time period of the power supply device increases, the maximum operating temperature of the power supply device will also increase accordingly, thereby constructing a simulation curve diagram between the operating time and the operating temperature, such as Figure 3 The simulation curve between equipment operation time and operating temperature is shown in the figure. Figure 3 The horizontal axis is the operating time in hours, and the vertical axis is the operating temperature in degrees Celsius.

[0096] By observing Figure 3The simulation curve between the equipment operating time and the operating temperature shows that the overall operating temperature of the equipment increases with the increase of the equipment operating time, but when the temperature of the equipment reaches 50 degrees Celsius, there is a downward trend, which means that when the temperature of the equipment reaches 50 degrees Celsius, the equipment accelerates heat dissipation and protects the equipment from overheating.

[0097] Specifically, intelligent early warning is carried out for each power demand device, and the specific early warning process is as follows:

[0098] The power consumption index of each power demand device is compared with the power consumption index threshold preset in the power database. If the power consumption index of a power demand device is greater than the preset power consumption index threshold, the power demand device is recorded as a demand device requiring early warning. Thus, the power demand devices corresponding to the power consumption index greater than the preset power consumption index threshold are statistically obtained and recorded as demand devices requiring early warning. Intelligent early warning is performed on each demand device requiring early warning through the power early warning mechanism in the power marketing system.

[0099] If the power consumption index of a certain power demand device is less than or equal to the power consumption demand index threshold preset in the power database, there is no need to provide intelligent early warning for the power demand device.

[0100] It should be explained that the above-mentioned electricity demand index threshold refers to a series of preset electricity usage critical values ​​used to monitor and manage the operating status of the power system. The electricity demand index threshold can be obtained by consulting experts for advice on electricity demand.

[0101] In the present embodiment, the above-mentioned power early warning mechanism in the power marketing system is used to perform intelligent early warning on each power demand device. The intelligent early warning refers to the early warning of artificial intelligence. The early warning technology in artificial intelligence technology is used to perform early warning. The specific early warning mechanism is that the early warning mechanism will generate an obvious and urgent orange early warning signal to warn each power demand device of serious excessive and insufficient power demand problems. Once a warning signal of this level is issued, the power marketing system must immediately initiate pre-set emergency response measures, which may include but are not limited to: quickly launching an emergency response plan and taking measures such as starting backup power generation facilities and adjusting power distribution strategies.

[0102] Specifically, intelligent early warning is carried out for each power supply equipment, and the specific early warning process is as follows:

[0103] The power supply index of each power supply device is compared with the power supply index limit value preset in the power database. If the power supply index of a certain power supply device is greater than the preset power supply index limit value, the power supply device is recorded as a supply device requiring early warning. The power supply devices whose power supply index is greater than the preset power supply index limit value are statistically recorded as supply devices requiring early warning. Intelligent early warning is performed on each supply device requiring early warning through the power early warning mechanism in the power marketing system.

[0104] If the power supply index of a certain power supply equipment is less than or equal to the power supply index limit value preset in the power database, there is no need to provide intelligent early warning for the power supply equipment.

[0105] It should be explained that the above-mentioned power supply equipment operation indicator definition values ​​refer to a series of preset critical values ​​used to monitor and manage the operating status of power equipment, which can be obtained by monitoring power supply equipment data and consulting power supply equipment experts.

[0106] In this embodiment, intelligent early warning is performed on each power supply device through the power early warning mechanism in the power marketing system. The intelligent early warning refers to the early warning of artificial intelligence. The early warning technology in artificial intelligence technology is used to perform early warning. The specific power early warning mechanism is that the early warning mechanism will generate an obvious and urgent red early warning signal to warn the power supply equipment in the power marketing system of serious fault problems. Once a high-level early warning signal is issued, the power supply equipment must immediately initiate pre-set emergency response measures, which may include but are not limited to: accelerating the operation of the power supply equipment or increasing the load response mechanism to increase the power supply, starting the backup power supply, such as generator sets, energy storage systems and other backup power supply equipment to cope with possible power shortages.

[0107] Reference Figure 2 As shown, the second aspect of the present invention provides a system of early warning method for power marketing system based on artificial intelligence, including: power demand analysis module, power equipment operation monitoring module, power intelligent early warning module and power database.

[0108] The second aspect of the present invention provides a system for an early warning method of an electric power marketing system based on artificial intelligence, wherein the electric power database is used to store a preset electric power demand index threshold, a preset electric power supply equipment operation index boundary value, a preset electric power demand equipment reference usage frequency, a preset correction factor corresponding to the preset electric power demand equipment usage frequency, a preset reference power factor of the electric power demand equipment, a correction factor corresponding to the preset power factor, a preset rated power of each electric power supply equipment, an influencing factor of a preset unit value of an operating load rate, a preset electric power equipment operation reference temperature, a correction factor corresponding to a preset maximum operating temperature, an influencing factor of a preset unit value of an operating total time period, an electric power demand level corresponding to each preset electric power demand index interval, and an electric power supply level corresponding to each preset electric power supply equipment operation index interval.

[0109] The power demand analysis module is connected to the power equipment operation monitoring module, the power equipment operation monitoring module is connected to the power intelligent early warning module, and the power demand analysis module, the power equipment operation monitoring module and the power intelligent early warning module are all connected to the power database.

[0110] The power demand analysis module is used to obtain the power demand data in the power marketing system and pre-process the power demand data, record the power demand data after the pre-processing operation as the power demand data of each power demand device, and obtain the power demand index of each power demand device by analyzing the power demand data of each power demand device.

[0111] The power equipment operation monitoring module is used to monitor the power operation status of each power supply equipment in the power marketing system, obtain the operation data of each power supply equipment, and obtain the power supply indicators of each power supply equipment by analyzing the operation data of each power supply equipment.

[0112] The power intelligent early warning module is used to compare the power consumption index of each power demand device with the power consumption index threshold value preset in the power database, and statistically obtain the power demand devices whose power consumption index is greater than the preset power consumption index threshold value, and perform intelligent early warning on each power demand device through the power early warning mechanism in the power marketing system. At the same time, the power supply index of each power supply device is compared with the power supply index boundary value preset in the power database, and statistically obtain the power supply index of each power supply device that is greater than the preset power supply index boundary value, and perform intelligent early warning on each power supply device through the power early warning mechanism in the power marketing system.

[0113] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. An early warning method for an electric power marketing system based on artificial intelligence, characterized in that: include: Power demand analysis: Obtain power demand data from the power marketing system, perform preprocessing on the power demand data, record the preprocessed power demand data as power consumption data of each power demand device, and obtain the power consumption index of each power demand device by analyzing the power consumption data of each power demand device; Power equipment operation monitoring: monitor the power operation status of each power supply equipment in the power marketing system, obtain the operation data of each power supply equipment, and obtain the power supply indicators of each power supply equipment by analyzing the operation data of each power supply equipment; Intelligent power early warning: compare the power consumption index of each power demand device with the power consumption index threshold value preset in the power database, and obtain statistics on each power demand device whose power consumption index is greater than the preset power consumption index threshold value. Through the power early warning mechanism in the power marketing system, intelligent early warning is given to each power demand device. At the same time, compare the power supply index of each power supply device with the power supply index boundary value preset in the power database, and obtain statistics on each power supply device whose power supply index is greater than the preset power supply index boundary value. Through the power early warning mechanism in the power marketing system, intelligent early warning is given to each power supply device.

2. The early warning method of the power marketing system based on artificial intelligence according to claim 1 is characterized by: The preprocessing operation is performed on the power demand data, and the specific preprocessing operation includes removing erroneous values, removing abnormal values, processing missing values, smoothing noise data and data standardization, thereby completing the preprocessing operation of the power demand data, and the power demand data after the preprocessing operation is completed is recorded as power demand data.

3. The early warning method of the power marketing system based on artificial intelligence according to claim 2 is characterized by: The power consumption data of each power demand device specifically includes the number of times each power demand device is used within the power operation cycle, and the useful power and apparent power corresponding to each power demand device at each time point within the power operation cycle.

4. The early warning method of the power marketing system based on artificial intelligence according to claim 3 is characterized by: The power consumption data of each power demand device is analyzed, and the specific analysis process is as follows: The number of times each power demand device is used in the power operation cycle is divided by the duration corresponding to the power operation cycle to obtain the usage frequency of each power demand device in the power operation cycle; Perform ratio processing on the useful power and apparent power corresponding to each power demand device at each time point in the power operation cycle to obtain the power factor corresponding to each power demand device at each time point in the power operation cycle; The power factor corresponding to each power demand device at each time point in the power operation cycle is analyzed to obtain the maximum power factor of each power demand device in the power operation cycle.

5. The early warning method of the power marketing system based on artificial intelligence according to claim 4 is characterized by: The specific analysis process of the power consumption index of each power demand device is as follows: The usage frequency and maximum power factor of each power demand equipment during the power operation cycle are comprehensively analyzed to obtain the power consumption index of each power demand equipment.

6. The early warning method of the power marketing system based on artificial intelligence according to claim 1 is characterized by: The operating data of each power supply device specifically include the operating voltage and operating current of each power supply device at each time point within the power operation cycle, the total operating time period of each power supply device within the power operation cycle, and the operating temperature of each power supply device at each time point within the power operation cycle.

7. The early warning method of the power marketing system based on artificial intelligence according to claim 6 is characterized by: The specific analysis process of the power supply indicators of each power supply device is as follows: The operating voltage and operating current corresponding to each power supply device at each time point in the power operation cycle are analyzed by extreme value to obtain the maximum operating voltage and maximum operating current of each power supply device in the power operation cycle; The maximum operating power of each power supply device during the power operation cycle is obtained by multiplying the maximum operating voltage of each power supply device during the power operation cycle by the maximum operating current; The maximum electric power of each power supply device in the power operation cycle is processed by ratio analysis with the rated power of each power supply device preset in the power database to obtain the operating load rate of each power supply device in the power operation cycle; The operating temperature corresponding to each power supply equipment at each time point in the power operation cycle is analyzed by the maximum value to obtain the maximum operating temperature of each power supply equipment in the power operation cycle; The operating load rate, the maximum operating temperature and the total operating time period of each power supply equipment in the power operation cycle are comprehensively analyzed to obtain the power supply index of each power supply equipment. The specific analysis formula is: In the formula, H i is the power supply index of the i-th power supply device, i is the number of each power supply device, i=1, 2, 3...y, y is the total number of power devices, u i is the operating load rate of the ith power equipment in the power operation cycle, b1 is the influencing factor of the unit value of the operating load rate preset in the power database, and w i is the maximum operating temperature of the ith power equipment during the power operation cycle, Δw is the power equipment operating reference temperature preset in the power database, b2 is the correction factor corresponding to the maximum operating temperature preset in the power database, ty i is the total operation time period of the ith power equipment in the power operation cycle, and b3 is the influencing factor of the unit value of the total operation time period preset in the power database.

8. The early warning method of the power marketing system based on artificial intelligence according to claim 1 is characterized by: The specific early warning process of each power demand device is as follows: Compare the power consumption index of each power demand device with the power consumption index threshold preset in the power database. If the power consumption index of a power demand device is greater than the preset power consumption index threshold, the power demand device is recorded as a demand device requiring early warning. Thus, the power demand devices corresponding to the power consumption index greater than the preset power consumption index threshold are obtained by statistics and recorded as demand devices requiring early warning. Intelligent early warning is performed on each demand device requiring early warning through the power early warning mechanism in the power marketing system. If the power consumption index of a certain power demand device is less than or equal to the power consumption demand index threshold preset in the power database, there is no need to provide intelligent early warning for the power demand device.

9. The early warning method of the power marketing system based on artificial intelligence according to claim 8 is characterized by: The specific early warning process of each power supply device is as follows: Compare the power supply index of each power supply device with the power supply index limit value preset in the power database. If the power supply index of a power supply device is greater than the preset power supply index limit value, the power supply device is recorded as a power supply device requiring early warning. Statistically obtain each power supply device whose power supply index is greater than the preset power supply index limit value and record it as each power supply device requiring early warning. Perform intelligent early warning on each power supply device requiring early warning through the power early warning mechanism in the power marketing system. If the power supply index of a certain power supply equipment is less than or equal to the power supply index limit value preset in the power database, there is no need to provide intelligent early warning for the power supply equipment.

10. A system using an early warning method for an electric power marketing system based on artificial intelligence as claimed in any one of claims 1 to 9, characterized in that: include: The power demand analysis module is used to obtain the power demand data in the power marketing system, perform preprocessing operations on the power demand data, record the preprocessed power demand data as the power demand data of each power demand device, and obtain the power demand index of each power demand device by analyzing the power demand data of each power demand device; The power equipment operation monitoring module is used to monitor the power operation status of each power supply equipment in the power marketing system, obtain the operation data of each power supply equipment, and obtain the power supply index of each power supply equipment by analyzing the operation data of each power supply equipment; The power intelligent early warning module is used to compare the power consumption index of each power demand device with the power consumption index threshold value preset in the power database, and statistically obtain the power demand devices whose power consumption index is greater than the preset power consumption index threshold value, and perform intelligent early warning on each power demand device through the power early warning mechanism in the power marketing system. At the same time, the power supply index of each power supply device is compared with the power supply index boundary value preset in the power database, and statistically obtain the power supply index of each power supply device that is greater than the preset power supply index boundary value, and perform intelligent early warning on each power supply device through the power early warning mechanism in the power marketing system.

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