Power grid operation risk identification method based on space-time multi-modal information fusion

Abstract

The invention relates to the technical field of information fusion risk identification, and particularly discloses a power grid operation risk identification method based on space-time multi-modal information fusion, and the method comprises the steps: obtaining the historical risk feature data of a power grid, and determining the level of a power grid operation risk monitoring threshold value; analyzing power grid operation environment equipment appearance image data, equipment sound and vibration data and meteorological condition data, determining a power grid operation risk monitoring initial evaluation grade, analyzing a power grid operation environment communication signal state, determining a power grid operation risk monitoring grade, and determining whether the power grid operation has a risk; according to the method, the problems that a traditional risk identification method often depends on single-mode data or artificial experience judgment, data sources are not rich enough, the data integration capacity is insufficient, and the dynamic adaptive capacity is insufficient are solved, the power grid operation risk can be evaluated more accurately, the situation of missed judgment or misjudgment is reduced, and the risk change is dynamically adjusted and adapted.

Description

Technical Field

[0001] The present invention relates to the technical field of information fusion risk identification, and in particular to a method for identifying power grid operation risks based on spatiotemporal multimodal information fusion. Background Art

[0002] The power grid operation environment is complex and dangerous, involving multiple risk factors such as high altitude, high voltage, strong electromagnetic, equipment failure, etc. With the rapid development of the Internet of Things, sensor technology and artificial intelligence, intelligent identification of power grid operation risks by integrating data from multiple sensing modalities has become a key research direction for improving the safety and reliability of power grid operations. This helps to discover potential risks in advance, prevent accidents, ensure the stable operation of the power grid and the life safety of operators, and also improve the intelligence level and efficiency of power grid operation and maintenance.

[0003] Nowadays, there are still some deficiencies in the research on the identification of power grid operation risks by integrating spatiotemporal multimodal information. Specifically, traditional risk identification methods often rely on single-modal data or manual experience judgment, and have problems such as low accuracy and untimely warning. Traditional data collection methods can often only obtain a limited number of data types. For some multi-dimensional data that can more comprehensively reflect the operation risks, the collection is insufficient or there is a lack of effective collection methods, resulting in insufficient data sources, inability to fully explore potential risk information, insufficient data integration capabilities, and insufficient dynamic adaptability. Insufficient data integration capabilities make it impossible to quickly obtain comprehensive information, thereby affecting the timeliness and effectiveness of emergency response and expanding the scope of risk impact. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a method for identifying power grid operation risks based on spatiotemporal multimodal information fusion, which can effectively solve the problems involved in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for identifying power grid operation risks based on spatiotemporal multimodal information fusion, comprising the following steps: obtaining historical risk characteristic data of the power grid, and based on the obtained historical risk characteristic data of the power grid, comprehensively analyzing to obtain historical risk characteristic constraint factors of the power grid; determining the threshold level of power grid operation risk monitoring based on the historical risk characteristic constraint factors of the power grid; analyzing the appearance image data of the power grid operation environment equipment to obtain the equipment appearance image analysis signal; analyzing the sound and vibration data of the power grid operation environment equipment to obtain the equipment sound and vibration analysis signal; obtaining meteorological condition data of the power grid operation environment, and determining the initial assessment level of power grid operation risk monitoring in combination with the equipment appearance image analysis signal and the equipment sound and vibration analysis signal; analyzing the communication signal status of the power grid operation environment, and determining the power grid operation risk monitoring level in combination with the initial assessment level of power grid operation risk monitoring; judging whether the power grid operation is at risk based on the power grid operation risk monitoring level and the power grid operation risk monitoring threshold level.

[0006] As a further method, a comprehensive analysis is performed to obtain the constraint factors of the historical risk characteristics of the power grid. The specific analysis process is: obtaining the historical risk characteristic data of the power grid, which specifically includes the historical equipment failure frequency of the power grid, the historical current overload frequency of the power grid, and the historical electromagnetic interference frequency of the power grid; based on the acquired historical risk characteristic data of the power grid, a comprehensive analysis is performed to obtain the constraint factors of the historical risk characteristics of the power grid, and the historical risk characteristic constraint factors of the power grid are used as the analysis basis for determining the threshold level of power grid operation risk monitoring.

[0007] As a further method, the threshold level of power grid operation risk monitoring is determined. The specific analysis process is: storing the historical risk characteristic constraint factor of the power grid as a specified label, comparing the specified label with each set label stored in the database, and obtaining the set label corresponding to the specified label; obtaining the power grid operation risk monitoring threshold level corresponding to the set label stored in the database.

[0008] As a further method, the appearance image data of the equipment in the power grid operating environment is analyzed to obtain the equipment appearance image analysis signal. The specific analysis process is: obtaining the appearance image data of the equipment in the power grid operating environment, the appearance image data of the equipment in the power grid operating environment specifically includes the number of cracks on the equipment casing, the corrosion ratio of the equipment casing, and the integrity ratio of the equipment nameplate; based on the obtained appearance image data of the equipment in the power grid operating environment, a comprehensive analysis is performed to obtain the equipment appearance image analysis signal, and the equipment appearance image analysis signal is used as the analysis basis for determining the initial assessment level of the power grid operation risk monitoring.

[0009] As a further method, the device appearance image analysis signal, the specific analysis process is:

[0010]

[0011] Wherein, α is the equipment appearance image analysis signal, lwt is the number of cracks on the equipment shell, fsb is the corrosion ratio of the equipment shell, wzb is the integrity ratio of the equipment nameplate, ε1 is the compensation factor of the set lwt, ε2 is the compensation factor of the set fsb, and ε3 is the compensation factor of the set wzb.

[0012] As a further method, the sound and vibration data of the power grid operating environment equipment are analyzed to obtain equipment sound and vibration analysis signals. The specific analysis process is: obtaining the sound and vibration data of the power grid operating environment equipment, the sound and vibration data of the power grid operating environment equipment specifically include the equipment operation decibel value, equipment average vibration frequency, equipment vibration amplitude; based on the obtained power grid operating environment equipment sound and vibration data, comprehensive analysis is performed to obtain equipment sound and vibration analysis signals, and the equipment sound and vibration analysis signals are used as the analysis basis for determining the initial evaluation level of power grid operation risk monitoring.

[0013] As a further method, the equipment sound and vibration analysis signals, the specific analysis process is as follows:

[0014]

[0015] Wherein, β is the equipment sound and vibration analysis signal, yfb is the equipment operation decibel value, zdp is the equipment average vibration frequency, zdf is the equipment vibration amplitude, σ1 is the compensation factor of the set yfb, σ2 is the compensation factor of the set zdp, σ3 is the compensation factor of the set zdf, and e is a natural constant.

[0016] As a further method, the meteorological condition data of the power grid operation environment is obtained, and the preliminary evaluation level of the power grid operation risk monitoring is determined by combining the equipment appearance image analysis signal and the equipment sound and vibration analysis signal. The specific analysis process is: obtaining the meteorological condition data of the power grid operation environment, and the obtained meteorological condition data of the power grid operation environment specifically includes the temperature deviation rate of the power grid operation environment, the humidity deviation rate of the power grid operation environment, and the air pressure deviation rate of the power grid operation environment; based on the obtained meteorological condition data of the power grid operation environment, combined with the equipment appearance image analysis signal and the equipment sound and vibration analysis signal, a comprehensive analysis is performed to obtain the power grid operation monitoring factor, and the power grid operation monitoring factor is used as the analysis basis for determining the preliminary evaluation level of the power grid operation risk monitoring; the power grid operation monitoring factor is compared with the power grid operation monitoring threshold stored in the database; if the power grid operation monitoring factor is not lower than the power grid operation monitoring threshold, the preliminary evaluation level of the power grid operation risk monitoring corresponding to the power grid operation monitoring factor is level one; if the power grid operation monitoring factor is lower than the power grid operation monitoring threshold, the preliminary evaluation level of the power grid operation risk monitoring corresponding to the power grid operation monitoring factor is level two.

[0017] As a further method, the power grid operation environment communication signal status is analyzed, and the power grid operation risk monitoring level is determined in combination with the initial evaluation level of the power grid operation risk monitoring. The specific analysis process is: obtaining the power grid operation environment communication signal status data, the power grid operation environment communication signal status data specifically includes the operation environment communication signal transmission bandwidth, the operation environment communication signal transmission delay, and the operation environment communication signal signal-to-noise ratio; based on the obtained power grid operation environment communication signal status data, a comprehensive analysis is performed to obtain the environmental communication signal constraint factor, and the environmental communication signal constraint factor is used as an analysis basis for determining the power grid operation risk monitoring level; the environmental communication signal constraint factor is stored as a specified label, and the specified label is compared with the set label stored in the database to obtain the set label corresponding to the specified label; the power grid operation risk correction level corresponding to the set label stored in the database is obtained; the power grid operation risk correction level and the power grid operation risk monitoring initial evaluation level are accumulated to obtain the power grid operation risk monitoring level.

[0018] As a further method, based on the grid operation risk monitoring level and combined with the grid operation risk monitoring threshold level, it is judged whether the grid operation has risks. The specific analysis process is: compare the grid operation risk monitoring level with the grid operation risk monitoring threshold level; if the grid operation risk monitoring level is not lower than the grid operation risk monitoring threshold level, then the grid operation has risks, and a risk warning is issued for the grid operation; if the grid operation risk monitoring level is lower than the grid operation risk monitoring threshold level, then the grid operation has no risks.

[0019] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0020] (1) The present invention provides a method for identifying power grid operation risks based on spatiotemporal multimodal information fusion. By analyzing the historical risk characteristic data of the power grid to obtain constraint factors, the present invention takes into account various complex conditions of power grid operation, thereby making the determination of risk monitoring threshold levels more scientific, facilitating the accurate definition of different risk levels, and avoiding the evaluation errors caused by setting thresholds based solely on subjective judgment or simple rules.

[0021] (2) The present invention integrates equipment appearance image analysis signals, sound and vibration analysis signals, meteorological condition data and communication signal status to determine the risk monitoring level. The multi-source data fusion method can comprehensively consider various factors that affect the risk of power grid operation, and can more accurately assess the risk of power grid operation, reduce missed or misjudgment situations, and dynamically adjust to adapt to risk changes.

[0022] (3) The present invention obtains historical risk characteristic data of the power grid and determines the threshold level of power grid operation risk monitoring, which can more accurately define the scope of risk based on the historical risk characteristics of the power grid. Once the monitoring data reaches or exceeds the threshold, the early warning mechanism can be triggered to timely detect and issue an alarm when the risk has just appeared or is still at a low level. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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.

[0024] Figure 1 It is a schematic diagram of the method flow of the present invention.

[0025] Figure 2 Flowchart of steps for determining risk monitoring level of power grid operations. DETAILED DESCRIPTION

[0026] 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.

[0027] Reference Figure 1 As shown, the present invention provides a method for identifying power grid operation risks based on spatiotemporal multimodal information fusion, including: acquiring historical risk characteristic data of the power grid, and obtaining historical risk characteristic constraint factors of the power grid based on the acquired historical risk characteristic data of the power grid through comprehensive analysis.

[0028] The specific analysis process is as follows: obtaining historical risk characteristic data of the power grid, which specifically includes historical equipment failure frequency of the power grid, historical current overload frequency of the power grid, and historical electromagnetic interference frequency of the power grid; based on the acquired historical risk characteristic data of the power grid, a comprehensive analysis is performed to obtain the historical risk characteristic constraint factors of the power grid, and the historical risk characteristic constraint factors of the power grid are used as the analysis basis for determining the threshold level of power grid operation risk monitoring.

[0029] The historical equipment failure frequency of the power grid refers to the ratio of the number of failures of various types of equipment (including transformers, circuit breakers, transmission lines, switchgear, etc.) in the power grid system to the total operating time within a period of time in the past (such as the past year, five years or a specific statistical period). It is an indicator used to measure the frequency of equipment failures in the historical operation process.

[0030] The historical current overload frequency of the power grid refers to the ratio of the number of times the current exceeded the rated current (overload) of the equipment in the power grid system within a specific period of time in the past (such as the past year, five years or a specific statistical period) to the total operating time, reflecting the frequency of current overload in the power grid during its historical operation.

[0031] The historical electromagnetic interference frequency of the power grid refers to the ratio of the number of times the power grid system has been subjected to external or internal electromagnetic interference to the total operating time in the past period of time. Electromagnetic interference comes from many sources, such as nearby radio transmission towers, lightning strikes, and switching operations of power electronic equipment.

[0032] By obtaining specific data such as the frequency of historical equipment failures, current overload frequencies, and electromagnetic interference frequencies in the power grid, it is possible to conduct an analysis based on what actually happened in the past, making the resulting power grid historical risk characteristic constraint factors more objective. The power grid historical risk characteristic constraint factors are derived based on the historical data of a specific power grid. The operating environment, equipment composition, and load characteristics of each power grid are different. The constraint factors can tailor the risk monitoring threshold level for the power grid to make it more in line with the actual operation of the power grid. For example, for a power grid area with a large number of old equipment, the historical data of its equipment failure frequency is high. When determining the risk monitoring threshold level, a more sensitive threshold can be set accordingly to detect potential risks earlier.

[0033] In this embodiment, all calculation formulas may perform a homogenization process on corresponding parameters before calculation.

[0034] The specific analysis process of the power grid historical risk characteristic constraint factor is as follows:

[0035]

[0036] Wherein, ω is the historical risk characteristic constraint factor of the power grid, gzp is the historical equipment failure frequency of the power grid, lzp is the historical current overload frequency of the power grid, grp is the historical electromagnetic interference frequency of the power grid, θ1 is the compensation factor of the set gzp, θ2 is the compensation factor of the set lzp, and θ3 is the compensation factor of the set grp.

[0037] Including a variety of key factors that affect the operation of the power grid, including equipment failure, current overload, and electromagnetic interference, avoids the limitations of single-factor assessment, can more comprehensively reflect the risk status faced by the power grid in historical operation, and make the risk assessment results closer to reality. Through in-depth analysis and summary of the historical risks of the power grid, we can better grasp the laws and characteristics of the power grid operation, calculate through specific frequency data, quantify the historical risk characteristics of the power grid, and provide a clear numerical basis for risk assessment.

[0038] It needs to be explained that the compensation factors of gzp, lzp, and grp set above are obtained from the database. According to historical data, a mapping set of historically measured power grid historical equipment failure frequencies, power grid historical current overload frequencies, power grid historical electromagnetic interference frequencies and compensation factors of gzp, lzp, and grp is established to obtain the compensation factors of gzp, lzp, and grp corresponding to the current gzp, lzp, and grp.

[0039] It should be noted that ε1, ε2, ε3, σ1, σ2, σ3, τ1, τ2, τ3, μ1, μ2, and μ3 mentioned below are also obtained through a mapping set of historical data and compensation factors established in a database, that is, the corresponding compensation factors are obtained according to the current data.

[0040] Based on the historical risk characteristic constraint factors of the power grid, the threshold level of power grid operation risk monitoring is determined.

[0041] The specific analysis process is as follows: the historical risk characteristic constraint factor of the power grid is stored as a specified label, the specified label is compared with each set label stored in the database to obtain the set label corresponding to the specified label; and the power grid operation risk monitoring threshold level corresponding to the set label stored in the database is obtained.

[0042] By storing the historical risk characteristic constraint factors of the power grid as specified tags and comparing them with the set tags in the database, a standardized risk assessment process is established to improve the efficiency of risk assessment. As the power grid develops and the operating environment changes, the set tags and corresponding threshold levels in the database enable the risk assessment system to dynamically adapt to these changes. The determined risk monitoring threshold level provides a clear boundary for risk warning. When the power grid operation data reaches or exceeds this threshold level, a warning signal can be issued in time to more accurately reflect the actual risk situation.

[0043] The appearance image data of the equipment in the power grid operation environment is analyzed to obtain an equipment appearance image analysis signal.

[0044] The specific analysis process is as follows: obtaining the appearance image data of the equipment in the power grid operating environment, which specifically includes the number of cracks on the equipment casing, the corrosion ratio of the equipment casing, and the integrity ratio of the equipment nameplate; based on the obtained appearance image data of the equipment in the power grid operating environment, a comprehensive analysis is performed to obtain the equipment appearance image analysis signal, which is used as the analysis basis for determining the initial assessment level of the power grid operation risk monitoring.

[0045] The number of cracks on the equipment shell refers to the number of visible cracks on the surface of the equipment shell. The corrosion ratio of the equipment shell refers to the ratio of the area of ​​the corroded part of the equipment shell to the total area of ​​the equipment shell. The equipment nameplate is a metal plate or label installed on the surface of the equipment to identify the basic information of the equipment (such as equipment name, model, specification, production date, manufacturer, etc.). The complete ratio of the equipment nameplate refers to the ratio of the clearly identifiable information on the nameplate to the total information on the nameplate.

[0046] The number of cracks on the equipment shell is a direct reflection of the structural integrity of the equipment. The presence of cracks exposes the internal components of the equipment to the external environment, increasing the risk of moisture and contamination, and also weakening the mechanical strength of the equipment shell. The shell corrosion ratio reflects the degree of chemical erosion of the equipment. Corrosion will gradually damage the equipment shell material and reduce its protective and mechanical properties. The equipment nameplate contains key information about the equipment, such as model, parameters, production date, etc. The completeness ratio of the nameplate can reflect the clarity and integrity of the equipment identification. If the nameplate is damaged or the information is missing, it will cause many inconveniences to the maintenance, inspection and management of the equipment.

[0047] By analyzing various appearance data such as the number of cracks on the equipment shell, the corrosion ratio, and the integrity ratio of the nameplate, we can avoid the limitation of relying on a single appearance factor for risk assessment. The equipment appearance image analysis signal is used as the basis for determining the initial evaluation level of power grid operation risk monitoring, which can help identify and warn risks in the early stages. Through early risk warning and timely intervention, the service life of the equipment can be extended and the reliability of the equipment can be improved.

[0048] Furthermore, the device appearance image analysis signal, the specific analysis process is:

[0049]

[0050] Wherein, α is the equipment appearance image analysis signal, lwt is the number of cracks on the equipment shell, fsb is the corrosion ratio of the equipment shell, wzb is the integrity ratio of the equipment nameplate, ε1 is the compensation factor of the set lwt, ε2 is the compensation factor of the set fsb, and ε3 is the compensation factor of the set wzb.

[0051] Different aspects of equipment appearance, such as shell cracks, corrosion, and nameplate integrity, can reflect the physical condition of the equipment from different angles. By combining these three factors to calculate the equipment appearance image analysis signal, the overall appearance of the equipment can be comprehensively evaluated. Equipment shell cracks and corrosion are potential precursors to equipment damage, and the integrity of the nameplate is also related to the maintenance and management of the equipment. By calculating and analyzing the signal, potential risks can be discovered when these problems are still in the early stages.

[0052] Analyze the sound and vibration data of equipment in the power grid operating environment to obtain equipment sound and vibration analysis signals.

[0053] The specific analysis process is as follows: obtaining the sound and vibration data of the power grid operating environment equipment, which specifically includes the equipment operation decibel value, equipment average vibration frequency, and equipment vibration amplitude; based on the acquired power grid operating environment equipment sound and vibration data, a comprehensive analysis is performed to obtain the equipment sound and vibration analysis signal, which is used as the analysis basis for determining the initial assessment level of power grid operation risk monitoring.

[0054] The decibel value of equipment operation is a physical quantity used to measure the intensity of sound generated by the equipment during operation. It is measured in decibels (dB). The average vibration frequency of the equipment refers to the average number of vibration cycles per unit time during the vibration process of the equipment, and the unit is Hertz (Hz). The vibration amplitude of the equipment refers to the maximum displacement of the equipment from its equilibrium position during vibration. It is a physical quantity that measures the amplitude of the vibration of the equipment.

[0055] The decibel value of equipment operation is an indicator to measure the volume of sound produced during the operation of the equipment. Normally operating equipment usually has its own specific sound range. When the decibel value changes abnormally, it is often a signal that there is a problem with the equipment. Wear, looseness or electrical failure of mechanical parts can cause the equipment to produce abnormal noise. The average vibration frequency of the equipment reflects the speed of the equipment vibration. Once the vibration frequency increases, it is the mechanical structure or moving parts of the equipment that are abnormal. The vibration amplitude of the equipment indicates the amplitude of the vibration of the equipment. A larger vibration amplitude usually means that the equipment is subjected to a larger dynamic force, which is caused by reasons such as loose foundation of the equipment, damaged components or external interference.

[0056] By analyzing the data of equipment operation decibel value, average vibration frequency and vibration amplitude in combination, we can have a more comprehensive understanding of the equipment's operating status. These data are interrelated and influence each other. Single data can only reflect part of the problem, while comprehensive analysis can dig out more complex potential risks of equipment. Equipment sound and vibration analysis signals serve as the basis for determining the initial evaluation level of power grid operation risk monitoring, which can help to warn in the early stages of equipment failure. Changes in equipment sound and vibration usually show obvious signs before a failure occurs. By capturing these changes in time, measures can be taken in advance.

[0057] Furthermore, the equipment sound and vibration analysis signals, the specific analysis process is as follows:

[0058]

[0059] Wherein, β is the equipment sound and vibration analysis signal, yfb is the equipment operation decibel value, zdp is the equipment average vibration frequency, zdf is the equipment vibration amplitude, σ1 is the compensation factor of the set yfb, σ2 is the compensation factor of the set zdp, σ3 is the compensation factor of the set zdf, and e is a natural constant.

[0060] The sound and vibration conditions during the operation of the equipment are important manifestations of the mechanical performance of the equipment. The equipment sound and vibration analysis signals are obtained, and the sound and vibration characteristics of the equipment are quantified into a unified indicator. This indicator can be used to intuitively judge the degree of risk of equipment operation. Before a device fails, its sound and vibration characteristics usually change first. Problems such as wear, looseness or electrical failure of mechanical parts often lead to an increase in the decibel value of the equipment operation and a change in the vibration frequency or amplitude. By calculating the sound and vibration analysis signals of the equipment, these early signs can be captured in time. When the analysis signal exceeds the normal range or shows an abnormal change trend, it can warn in advance that the equipment may have a risk of failure, effectively avoiding the occurrence of sudden equipment failures.

[0061] Obtain data on meteorological conditions of the power grid operating environment, combine equipment appearance image analysis signals with equipment sound and vibration analysis signals to determine the initial assessment level of power grid operation risk monitoring.

[0062] The specific analysis process is as follows: obtaining meteorological condition data of the power grid operation environment, including the temperature deviation rate of the power grid operation environment, the humidity deviation rate of the power grid operation environment, and the air pressure deviation rate of the power grid operation environment; based on the obtained meteorological condition data of the power grid operation environment, combined with the equipment appearance image analysis signal and the equipment sound and vibration analysis signal, a comprehensive analysis is performed to obtain the power grid operation monitoring factor, and the power grid operation monitoring factor is used as the analysis basis for determining the initial assessment level of the power grid operation risk monitoring; the power grid operation monitoring factor is compared with the power grid operation monitoring threshold stored in the database; if the power grid operation monitoring factor is not lower than the power grid operation monitoring threshold, the initial assessment level of the power grid operation risk monitoring corresponding to the power grid operation monitoring factor is level one; if the power grid operation monitoring factor is lower than the power grid operation monitoring threshold, the initial assessment level of the power grid operation risk monitoring corresponding to the power grid operation monitoring factor is level two.

[0063] The temperature deviation rate of the power grid operating environment reflects the degree of deviation between the actual temperature and the temperature suitable for normal operation of the equipment. High temperature makes it difficult for the equipment to dissipate heat, causing the temperature of the internal components of the equipment to be too high, thereby accelerating the aging of insulation materials and reducing the performance of electronic components; low temperature makes some materials in the equipment brittle and affects mechanical properties. By considering the temperature deviation rate, this environmental factor can be quantified and its potential risks to the equipment can be more accurately assessed.

[0064] The humidity deviation rate reflects the difference between the ambient humidity and the ideal humidity. A high humidity environment easily causes the surface of the equipment to get damp, which reduces the insulation performance and causes electrical faults such as leakage and short circuit. A humid environment also promotes corrosion of metal parts and affects the mechanical integrity of the equipment. A low humidity environment generates static electricity, which damages electronic equipment. Risk assessment combined with the humidity deviation rate can more comprehensively consider the impact of humidity factors on power grid equipment.

[0065] The air pressure deviation rate of the power grid operating environment can reflect the changes in air pressure. Air pressure changes affect the sealing performance of equipment, especially for some equipment that is sensitive to air pressure, such as switch cabinets with air pressure protection devices. Air pressure changes are also related to weather changes. For example, a sudden drop in air pressure indicates the arrival of bad weather (such as heavy rain and strong winds). These weather conditions will cause various risks to outdoor power grid equipment, such as tower collapse and line swaying. Considering the air pressure deviation rate can help predict these risks in advance.

[0066] The deviation rate is the ratio of the absolute value of the difference between the actual value and the reference value to the reference value. The grid operation monitoring factor is obtained by combining the meteorological condition data of the grid operation environment with the equipment appearance image analysis signal, equipment sound and vibration analysis signal, realizing a comprehensive evaluation from two dimensions: the equipment's own status and the external environment. This multi-factor fusion method can more comprehensively capture the various factors that lead to grid operation risks. The grid operation monitoring factor obtained by comprehensive analysis is a quantitative indicator that can be used as a unified standard to measure the size of grid operation risks. The initial risk monitoring assessment level is divided by comparing the grid operation monitoring factor with the grid operation monitoring threshold stored in the database, which provides a clear and operational grading method for risk assessment. The higher the level, the greater the risk.

[0067] Power grid operation monitoring factors, the specific analysis process is as follows:

[0068]

[0069] In the formula, γ is the power grid operation monitoring factor, wdp is the power grid operation environment temperature deviation rate, sdp is the power grid operation environment humidity deviation rate, qyp is the power grid operation environment air pressure deviation rate, τ1 is the compensation factor of the set wdp, τ2 is the compensation factor of the set sdp, and τ3 is the compensation factor of the set qyp.

[0070] It should be explained that the above-mentioned power grid operation monitoring factor is calculated by combining the power grid operation environment temperature deviation rate, power grid operation environment humidity deviation rate, power grid operation environment air pressure deviation rate with the equipment appearance image analysis signal and the equipment sound and vibration analysis signal, and normalizing WDP, SDP, and QYP. The risk of power grid operation is jointly affected by environmental conditions and equipment status. By combining the temperature, humidity, and air pressure deviation rate of the power grid operation environment with the equipment appearance image analysis signal, equipment sound and vibration analysis signal to calculate the power grid operation monitoring factor, a variety of risk factors can be fully considered. It covers factors in the meteorological environment field and the equipment operation status field, which is helpful to analyze the interaction and correlation between risk factors in different fields. The calculated power grid operation monitoring factor is a quantitative indicator that can express the complex power grid operation risk status with a numerical value. When the monitoring factor approaches or exceeds the warning threshold, a risk warning can be issued in advance, thereby effectively preventing the occurrence of power grid accidents and ensuring the reliability and stability of power grid operations.

[0071] Analyze the communication signal status of the power grid operation environment, and determine the power grid operation risk monitoring level based on the initial assessment level of the power grid operation risk monitoring.

[0072] like Figure 2 As shown, the specific analysis process is: obtaining the power grid operation environment communication signal status data, the power grid operation environment communication signal status data specifically includes the operation environment communication signal transmission bandwidth, the operation environment communication signal transmission delay, and the operation environment communication signal signal-to-noise ratio; based on the obtained power grid operation environment communication signal status data, a comprehensive analysis is performed to obtain the environmental communication signal constraint factor, and the environmental communication signal constraint factor is used as the analysis basis for determining the power grid operation risk monitoring level; the environmental communication signal constraint factor is stored as a specified label, and the specified label is compared with the set label stored in the database to obtain the set label corresponding to the specified label; the power grid operation risk correction level corresponding to the set label stored in the database is obtained; the power grid operation risk correction level and the power grid operation risk monitoring initial evaluation level are accumulated to obtain the power grid operation risk monitoring level.

[0073] The transmission bandwidth of the operating environment communication signal reflects the amount of data that the communication channel can transmit per unit time. In power grid operations, sufficient bandwidth is essential for transmitting equipment status monitoring data, control instructions and other information. The transmission delay of the operating environment communication signal refers to the time delay experienced by the signal from the sender to the receiver. In the automation control and real-time monitoring system of the power grid, low latency is the key. The signal-to-noise ratio of the operating environment communication signal is the ratio of signal power to noise power, which reflects the quality of the communication signal. A high signal-to-noise ratio means clear signals and strong anti-interference ability, which is conducive to accurate data transmission.

[0074] Based on the comprehensive analysis of communication signal status data such as transmission bandwidth, transmission delay and signal-to-noise ratio, the environmental communication signal constraint factor is obtained, which realizes the quantification of communication signal risk factors and can integrate multiple communication signal-related factors to measure the overall impact of communication signals on power grid operation risks. The environmental communication signal constraint factor is used as the analysis basis for determining the power grid operation risk monitoring level, so that the communication signal status occupies a reasonable weight in the power grid operation risk assessment system, which helps to more accurately incorporate the communication signal risk factor into the assessment scope of the entire power grid operation risk and avoid inaccurate risk assessment caused by ignoring or underestimating communication signal problems. The power grid operation risk correction level and the power grid operation risk monitoring initial assessment level are accumulated to obtain the power grid operation risk monitoring level, so that the risk monitoring level can be dynamically adjusted according to changes in the communication signal status.

[0075] Environmental communication signal constraint factors, the specific analysis process is:

[0076]

[0077] Wherein, δ is the constraint factor of the environmental communication signal, dk is the transmission bandwidth of the operating environment communication signal, sy is the transmission delay of the operating environment communication signal, xz is the signal-to-noise ratio of the operating environment communication signal, μ1 is the compensation factor of the set dk, μ2 is the compensation factor of the set sy, and μ3 is the compensation factor of the set xz.

[0078] The transmission bandwidth, transmission delay and signal-to-noise ratio of communication signals are the three core elements for measuring communication quality. The transmission bandwidth determines the amount of data that can be transmitted per unit time, the transmission delay affects the timeliness of information transmission, and the signal-to-noise ratio reflects the signal's anti-interference ability during transmission. By calculating the environmental communication signal constraint factor through these three indicators, the quality of the communication signal in the power grid operating environment can be comprehensively and comprehensively evaluated. Based on the dynamic monitoring of the environmental communication signal constraint factor, power grid accidents caused by communication failures can be effectively prevented.

[0079] Based on the grid operation risk monitoring level and combined with the grid operation risk monitoring threshold level, determine whether there is a risk in the grid operation.

[0080] The specific analysis process is: compare the power grid operation risk monitoring level with the power grid operation risk monitoring threshold level; if the power grid operation risk monitoring level is not lower than the power grid operation risk monitoring threshold level, there is a risk in the power grid operation, and a risk warning is issued for the power grid operation; if the power grid operation risk monitoring level is lower than the power grid operation risk monitoring threshold level, there is no risk in the power grid operation.

[0081] By comparing the risk monitoring level of power grid operations with the threshold level, a clear and definite risk determination boundary is established. This comparison mechanism makes risk determination no longer ambiguous. As long as the risk monitoring level reaches or exceeds the threshold level, it is determined to be a risk, avoiding subjective assumptions and uncertainties. If there is no clear risk determination standard, excessive maintenance may occur. By comparing the risk monitoring level with the threshold level, it is determined to be a risk only when the risk reaches a certain level, which helps to avoid unnecessary frequent inspections and maintenance of power grid operations.

[0082] The comparison mechanism can comprehensively consider various risk factors in the power grid operation process and conduct comprehensive risk control from multiple aspects such as equipment status, environmental conditions and communication signals. By timely discovering and handling risks, the frequency of power grid equipment failures and accidents can be effectively reduced, ensuring the safe and stable operation of the power grid.

[0083] In a specific embodiment, in the past five years, the equipment failure conditions in a certain power grid area are as follows: the historical equipment failure frequency (gzp) of the power grid: a total of 30 equipment failures occurred, so the annual failure frequency is 30÷5=6 (times / year), the historical current overload frequency (lzp) of the power grid: 50 current overloads were detected, and the annual current overload frequency is 50÷5=10 (times / year), the historical electromagnetic interference frequency (grp) of the power grid: 40 electromagnetic interferences were received, and the annual electromagnetic interference frequency is 40÷5=8 (times / year), the compensation factor of gzp is 0.8, the compensation factor of lzp is 1.2, and the compensation factor of grp is 1.0, the calculated historical risk characteristic constraint factor of the power grid is 443.19, and the power grid operation risk monitoring threshold level corresponding to the historical risk characteristic constraint factor (443.19) of the power grid is level four.

[0084] The number of cracks on the equipment casing (lwt): 3, the corrosion ratio of the equipment casing (fsb): 0.2 (i.e. 20%), the integrity ratio of the equipment nameplate (wzb): 0.6 (i.e. 60%), the compensation factor of lwt is 0.5, the compensation factor of fsb is 0.6, the compensation factor of wzb is 0.7, and the calculated equipment appearance image analysis signal is 0.778.

[0085] Equipment operating decibel value (yfb): 80dB, equipment average vibration frequency (zdp): 20Hz, equipment vibration amplitude (zdf): 0.5mm, the compensation factor of yfb is 0.9, the compensation factor of zdp is 1.1, the compensation factor of zdf is 1.3, and the calculated equipment sound and vibration analysis signal is 0.747.

[0086] The power grid operation environment temperature deviation rate (wdp): 0.1 (the actual temperature is 10% higher than the appropriate temperature), the power grid operation environment humidity deviation rate (sdp): 0.2 (the actual humidity is 20% higher than the ideal humidity), the power grid operation environment air pressure deviation rate (qyp): 0.05 (the actual air pressure is 5% higher than the normal air pressure), the compensation factor of wdp is 1.5, the compensation factor of sdp is 1.3, the compensation factor of qyp is 1.2, and the calculated power grid operation monitoring factor is 3.063. The power grid operation monitoring factor (3.063) is compared with the power grid operation monitoring threshold (3) stored in the database. Because 3.063>3, the initial assessment level of power grid operation risk monitoring is level one.

[0087] The transmission bandwidth of the operating environment communication signal (dk): 100Mbps, the transmission delay of the operating environment communication signal (sy): 0.05s, the signal-to-noise ratio of the operating environment communication signal (xz): 20dB, the compensation factor of dk is 0.7, the compensation factor of sy is 0.8, and the compensation factor of xz is 1.5. The calculated environmental communication signal constraint factor is 70.541. The power grid operation risk correction level corresponding to the environmental communication signal constraint factor (70.541) is level two. The power grid operation risk correction level (level two) and the power grid operation risk monitoring initial assessment level (level one) are added together to obtain a power grid operation risk monitoring level of level three. The power grid operation risk monitoring level (level three) is compared with the power grid operation risk monitoring threshold level (level four). Because level three is lower than level four, it is determined that there is no risk in the power grid operation at this time.

[0088] 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 by the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for identifying power grid operation risks based on spatiotemporal multimodal information fusion, characterized in that: The following steps are involved: Obtain historical risk characteristic data of the power grid, and obtain historical risk characteristic constraint factors of the power grid through comprehensive analysis based on the acquired historical risk characteristic data of the power grid; Determine the threshold level of grid operation risk monitoring based on the grid historical risk characteristic constraint factors; Analyze the appearance image data of the equipment in the power grid operation environment to obtain the equipment appearance image analysis signal; Analyze the sound and vibration data of equipment in the power grid operating environment to obtain equipment sound and vibration analysis signals; Obtain the meteorological condition data of the power grid operation environment, combine the equipment appearance image analysis signal and the equipment sound and vibration analysis signal to determine the initial assessment level of the power grid operation risk monitoring; Analyze the communication signal status of the power grid operation environment, and determine the power grid operation risk monitoring level in combination with the initial evaluation level of the power grid operation risk monitoring; Based on the grid operation risk monitoring level and combined with the grid operation risk monitoring threshold level, determine whether there is a risk in the grid operation.

2. The method for identifying power grid operation risks based on spatiotemporal multimodal information fusion according to claim 1, characterized in that: The comprehensive analysis obtains the historical risk characteristic constraint factors of the power grid. The specific analysis process is as follows: Obtain historical risk characteristic data of the power grid, which specifically includes historical equipment failure frequency of the power grid, historical current overload frequency of the power grid, and historical electromagnetic interference frequency of the power grid; Based on the acquired historical risk characteristic data of the power grid, a comprehensive analysis is conducted to obtain the historical risk characteristic constraint factors of the power grid. The historical risk characteristic constraint factors of the power grid serve as the analysis basis for determining the threshold level of power grid operation risk monitoring.

3. The method for identifying power grid operation risks based on spatiotemporal multimodal information fusion according to claim 2, characterized in that: The specific analysis process of determining the threshold level of power grid operation risk monitoring is as follows: The historical risk characteristic constraint factor of the power grid is stored as a designated tag, and the designated tag is compared with each set tag stored in the database to obtain the set tag corresponding to the designated tag; The power grid operation risk monitoring threshold level corresponding to the set tag stored in the database is obtained.

4. The method for identifying power grid operation risks based on spatiotemporal multimodal information fusion according to claim 1, characterized in that: The power grid operation environment equipment appearance image data is analyzed to obtain equipment appearance image analysis signals. The specific analysis process is as follows: Obtaining appearance image data of equipment in a power grid operating environment, wherein the appearance image data of equipment in a power grid operating environment specifically includes the number of cracks on the equipment shell, the corrosion ratio of the equipment shell, and the integrity ratio of the equipment nameplate; Based on the acquired equipment appearance image data of the power grid operation environment, a comprehensive analysis is performed to obtain the equipment appearance image analysis signal, which is used as the analysis basis for determining the initial assessment level of power grid operation risk monitoring.

5. The method for identifying power grid operation risks based on spatiotemporal multimodal information fusion according to claim 4 is characterized by: The device appearance image analysis signal, the specific analysis process is: Wherein, α is the equipment appearance image analysis signal, lwt is the number of cracks on the equipment shell, fsb is the corrosion ratio of the equipment shell, wzb is the integrity ratio of the equipment nameplate, ε1 is the compensation factor of the set lwt, ε2 is the compensation factor of the set fsb, and ε3 is the compensation factor of the set wzb.

6. The method for identifying power grid operation risks based on spatiotemporal multimodal information fusion according to claim 1 is characterized by: The sound and vibration data of the equipment in the power grid operation environment are analyzed to obtain equipment sound and vibration analysis signals. The specific analysis process is as follows: Acquire the sound and vibration data of the equipment in the power grid operation environment, which specifically includes the equipment operation decibel value, the average vibration frequency of the equipment, and the vibration amplitude of the equipment; Based on the acquired sound and vibration data of the equipment in the power grid operation environment, a comprehensive analysis is performed to obtain the equipment sound and vibration analysis signals, which are used as the analysis basis for determining the initial assessment level of the power grid operation risk monitoring.

7. The method for identifying power grid operation risks based on spatiotemporal multimodal information fusion according to claim 6, characterized in that: The equipment sound and vibration analysis signal, the specific analysis process is: Wherein, β is the equipment sound and vibration analysis signal, yfb is the equipment operation decibel value, zdp is the equipment average vibration frequency, zdf is the equipment vibration amplitude, σ1 is the compensation factor of the set yfb, σ2 is the compensation factor of the set zdp, σ3 is the compensation factor of the set zdf, and e is a natural constant.

8. The method for identifying power grid operation risks based on spatiotemporal multimodal information fusion according to claim 1, characterized in that: The meteorological condition data of the power grid operation environment is obtained, and the initial evaluation level of the power grid operation risk monitoring is determined by combining the equipment appearance image analysis signal and the equipment sound and vibration analysis signal. The specific analysis process is as follows: Obtaining meteorological condition data of the power grid operation environment, the meteorological condition data of the power grid operation environment specifically includes the power grid operation environment temperature deviation rate, the power grid operation environment humidity deviation rate, and the power grid operation environment air pressure deviation rate; Based on the acquired data on meteorological conditions of the power grid operation environment, combined with the equipment appearance image analysis signal and the equipment sound and vibration analysis signal, a comprehensive analysis is conducted to obtain the power grid operation monitoring factor, which is used as the analysis basis for determining the initial assessment level of the power grid operation risk monitoring; comparing the power grid operation monitoring factor with the power grid operation monitoring threshold value stored in the database; If the power grid operation monitoring factor is not lower than the power grid operation monitoring threshold, the initial evaluation level of the power grid operation risk monitoring corresponding to the power grid operation monitoring factor is level one; If the power grid operation monitoring factor is lower than the power grid operation monitoring threshold, the initial assessment level of the power grid operation risk monitoring corresponding to the power grid operation monitoring factor is level two.

9. The method for identifying power grid operation risks based on spatiotemporal multimodal information fusion according to claim 1, characterized in that: The communication signal status of the power grid operation environment is analyzed, and the power grid operation risk monitoring level is determined in combination with the initial evaluation level of the power grid operation risk monitoring. The specific analysis process is as follows: Acquire power grid operation environment communication signal status data, the power grid operation environment communication signal status data specifically includes the operation environment communication signal transmission bandwidth, the operation environment communication signal transmission delay, and the operation environment communication signal signal-to-noise ratio; Based on the acquired power grid operation environment communication signal status data, the environmental communication signal constraint factor is obtained through comprehensive analysis. The environmental communication signal constraint factor is used as the analysis basis for determining the power grid operation risk monitoring level. The environmental communication signal constraint factor is stored as a specified label, and the specified label is compared with the set label stored in the database to obtain the set label corresponding to the specified label; Obtaining the power grid operation risk correction level corresponding to the set tag stored in the database; The grid operation risk correction level and the grid operation risk monitoring initial assessment level are added together to obtain the grid operation risk monitoring level.

10. The method for identifying power grid operation risks based on spatiotemporal multimodal information fusion according to claim 1, characterized in that: Based on the grid operation risk monitoring level, combined with the grid operation risk monitoring threshold level, it is judged whether the grid operation has risks. The specific analysis process is as follows: comparing the power grid operation risk monitoring level with the power grid operation risk monitoring threshold level; If the grid operation risk monitoring level is not lower than the grid operation risk monitoring threshold level, the grid operation is at risk, and a risk warning is issued for the grid operation; If the grid operation risk monitoring level is lower than the grid operation risk monitoring threshold level, there is no risk in the grid operation.

Images