Multi-modal knowledge enhancement analysis system based on complex power production operation scene

Abstract

The invention discloses a multi-modal knowledge enhancement analysis system based on a complex power production operation scene. The multi-modal knowledge enhancement analysis system comprises a data acquisition module (100), a multi-modal fusion analysis module (200), a visual decision platform module (300), a safe closed-loop execution module (400) and a knowledge management engine module (500). The multi-modal fusion analysis module (200) generates an equipment health assessment report and a fault early warning signal, and transmits an analysis result to the safety closed-loop execution module (400); the safety closed-loop execution module (400) is embedded into a power equipment controller to realize state adjustment, and after safety verification, the safety closed-loop execution module (400) drives and executes verification operation. The system has remarkable advantages and effects, the operation state of the power equipment is sensed in all directions through multi-source sensing and heterogeneous data fusion, the man-machine cooperation efficiency is enhanced, and a safe closed-loop execution mechanism ensures the reliability of a control instruction through double verification.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power data analysis, and in particular to a multi-modal knowledge enhancement analysis system based on a complex power production operation scenario. BACKGROUND

[0002] In the field of power production, with the increasing intelligence of equipment and the complexity of operation scenarios, there is an increasing demand for comprehensive monitoring and accurate analysis of the running state of equipment. In the prior art, most monitoring systems rely on single sensor data or local operation and maintenance information to carry out equipment evaluation, such as monitoring the heating state of equipment through temperature sensors or judging running abnormalities according to equipment log records. These systems can achieve basic state monitoring, but they are difficult to integrate multi-dimensional information to form a comprehensive understanding, resulting in limited warning capabilities for complex faults. At the same time, traditional analysis methods rely mainly on human experience for decision-making, lack effective reuse of historical cases and expert knowledge, and are difficult to adapt to the dynamic management needs of large-scale power production networks. The deficiencies of the prior art mainly manifest in three aspects: first, the data collection dimension is single, only focusing on certain physical signals or part of the running parameters of the equipment, and unable to associate multi-modal information such as acoustics, optics, and electromagnetism for comprehensive research and judgment, which is likely to miss potential fault signs; second, the analysis model lacks self-optimization capability and is difficult to update the algorithm in combination with newly emerging fault cases, resulting in a decrease in accuracy over time; third, there is a disconnection between decision-making and execution, and there is a lack of effective verification mechanism for manual intervention instructions and system automatic analysis results, which may cause misoperation risks and affect the safety and stability of power production. SUMMARY

[0003] This section is intended to summarize some aspects of the embodiments of the present application and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of the specification to avoid obscuring the purpose of this section, the abstract and the title, and such simplifications or omissions cannot be used to limit the scope of the present application.

[0004] In view of the above and / or existing problems in the multi-modal knowledge enhancement analysis system based on a complex power production operation scenario, the present application is proposed.

[0005] Therefore, the problem to be solved by the present application is how to provide a remote sewage treatment that can be realized.

[0006] To solve the above technical problems, the present application provides the following technical solutions: a multi-modal knowledge enhancement analysis system based on a complex power production operation scenario, comprising, a data acquisition module, a multi-modal fusion analysis module, a visual decision platform module, a safety closed-loop execution module, and a knowledge management engine module; The data acquisition module includes a multi-source sensing unit and a heterogeneous data analysis unit, the multi-source sensing unit is used for real-time acquisition of acoustic, optical, thermodynamic and electromagnetic signals in the power production site, and the heterogeneous data analysis unit is used for structured processing of device logs, operation and maintenance records and environmental monitoring data; The multi-modal fusion analysis module associates the power equipment operation state through a knowledge graph reasoning unit and a deep learning prediction unit, generates a device health evaluation report and a fault warning signal, and transmits the analysis result to the safety closed-loop execution module; The visual decision platform module supports the operator to adjust the system strategy through a three-dimensional digital twin interface or a natural language instruction, and the generated control instruction is synchronized to the safety closed-loop execution module in real time; The safety closed-loop execution module includes a control verification unit and an instruction distribution unit, the control verification unit is embedded in the power equipment controller to realize state adjustment, and the instruction distribution unit receives the output instruction of the multi-modal fusion analysis module and the visual decision platform module, and drives the control verification unit to perform verification operation after safety verification; The knowledge management engine module integrates historical fault cases and expert experience library, continuously optimizes the algorithm model of the multi-modal fusion analysis module through the incremental learning unit, and feeds back the operation and maintenance decision data to the visual decision platform module.

[0007] As a preferred scheme of the multi-modal knowledge enhanced analysis system based on complex power production operation scene, wherein: the multi-source sensing unit includes a distributed microphone array, an infrared thermal imager, a camera, a laser radar and a three-phase current transformer, wherein: The distributed microphone array is used for collecting device operation abnormal sound signals, and the infrared thermal imager is used for capturing device surface temperature field distribution; The camera is used for collecting device appearance and operation scene video stream; The laser radar is used for generating three-dimensional point cloud data of the operation area; The three-phase current transformer is used for collecting device incoming line end current signal.

[0008] As a preferred scheme of the multi-modal knowledge enhanced analysis system based on complex power production operation scene, wherein: the structured processing of the heterogeneous data analysis unit includes: Field extraction is performed on the device log, and the timestamp, device ID, operation parameter and abnormal code are retained; Entity recognition is performed on the operation and maintenance record to generate three-tuple data including fault phenomenon, treatment measure and executor; The environmental monitoring data is spatio-temporally aligned to establish a multi-dimensional database indexed by latitude and longitude coordinates and time axis.

[0009] As a preferred scheme of the multi-modal knowledge enhanced analysis system based on a complex power production operation scene, the knowledge graph reasoning unit is constructed with a power equipment topology knowledge graph, the topology knowledge graph contains equipment entity nodes, component correlation edges and operation rule attributes, and the following reasoning is realized through a graph neural network algorithm: Fault tracing reasoning based on space-time correlation rules, locating the initial abnormal point causing the cascading failure; Influence range reasoning based on component dependency, predicting the impact of a single equipment failure on related systems.

[0010] As a preferred scheme of the multi-modal knowledge enhanced analysis system based on a complex power production operation scene, the deep learning prediction unit adopts a multi-modal feature fusion model based on an attention mechanism, and the input layer of the multi-modal feature fusion model includes: The multi-source sensing unit collects acoustic signal spectrum features, optical image texture features, thermodynamic temperature distribution features and electromagnetic signal waveform features; The heterogeneous data analysis unit outputs structured data time series features; The equipment health score output by the deep learning prediction unit is calculated by a weighted sum formula: H=α1S1+α2S2+α3S3+α4S4+α5S5 Wherein: H is the total score of the equipment health; S1 is the acoustic signal evaluation score, reflecting the abnormality of the equipment operation abnormal sound, calculated by the signal-to-noise ratio of the acoustic signal spectrum features; S2 is the optical feature evaluation score, determined based on the defect detection results of the equipment appearance image; S3 is the thermodynamic state score, calculated according to the deviation rate of the equipment surface temperature field distribution and the standard temperature threshold; S4 is the electromagnetic signal stability score, determined by the fluctuation amplitude of the current and voltage signals; S5 is the historical operation and maintenance matching degree score, obtained by comparing the similarity between the current state and the historical health state; α1 to α5 are the weight coefficients of each dimension score, satisfying α1+α2+α3+α4+α5=1, and the weight values are dynamically updated every month by the incremental learning unit of the knowledge management engine module.

[0011] As a preferred scheme of the multi-modal knowledge enhanced analysis system based on a complex power production operation scene, the natural language instruction processing flow of the visual decision platform module includes: Convert the voice instructions of the operators into text instructions through the voice recognition unit; The semantic understanding unit performs intention recognition on the text instruction and maps the intention recognition result into control parameters executable by the system. A parameter adjustment preview interface is generated, and after confirmation by the operator, the parameter adjustment preview interface is synchronized to the safe closed loop execution module.

[0012] As a preferred scheme of the multi-modal knowledge enhancement analysis system based on a complex power production operation scene, the state adjustment process of the control verification unit comprises: The adjustment instruction of the receiving instruction distribution unit is received, and the target parameter and the adjustment threshold are analyzed; The current state parameter of the power equipment and the target parameter are subjected to difference calculation, and a stepwise adjustment curve is generated; According to the adjustment curve, the parameter correction operation is performed, and the adjustment effect is fed back to the visual decision platform module in real time.

[0013] As a preferred scheme of the multi-modal knowledge enhancement analysis system based on a complex power production operation scene, the optimization process of the incremental learning unit of the knowledge management engine module comprises: New data mining is performed on the historical fault case library every week, and the mapping relationship between the fault features and the processing scheme is extracted; The updated content of the expert experience library is collected every month, and the expert decision logic is converted into a model interpretable rule through a knowledge distillation algorithm; The model of the deep learning prediction unit is fine-tuned based on the operation and maintenance decision data every quarter, and the fault warning threshold and the health assessment weight are updated.

[0014] In a second aspect, some embodiments of the present application provide an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon, when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the implementation manners of the first aspect.

[0015] In a third aspect, some embodiments of the present application provide a computer readable medium having a computer program stored thereon, wherein the program is executed by a processor to implement the method described in any of the implementation manners of the first aspect.

[0016] The advantage effect of the system is remarkable: through multi-source sensing and heterogeneous data fusion, the running state of power equipment is comprehensively perceived, combining knowledge graph reasoning and deep learning prediction, the accuracy and advance of fault early warning are greatly improved; the three-dimensional digital twin interface and natural language interaction simplify the operation process and enhance the human-computer cooperation efficiency; the safety closed-loop execution mechanism ensures the reliability of the control instruction through double checking; the incremental learning function of the knowledge management engine enables the system to continuously absorb historical experience and new cases, and continuously optimize the analysis model, and finally forms the intelligent management of the whole process from monitoring, analysis, decision-making to execution, effectively improving the operation and maintenance efficiency and safety level of complex power production scenes. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor.

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained from these drawings without creative labor. Among them: Figure 1 The flow structure diagram of the multi-modal knowledge enhanced analysis system based on complex power production operation scene in embodiment 1.

[0019] Figure 2 The connection system schematic diagram of the multi-modal knowledge enhanced analysis system based on complex power production operation scene in embodiment 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present application more apparent and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0021] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without departing from the connotation of the present application, therefore the present application is not limited to the specific embodiments disclosed below.

[0022] Second, the "one embodiment" or "an embodiment" referred to herein can include a particular feature, structure, or characteristic. The various embodiments appearing at different places in this specification are not necessarily all cumulative or mutually exclusive of each other.

[0023] Embodiment 1 Referring to Figure 1 With Figure 2 As a first embodiment of the present application, the embodiment provides a multi-modal knowledge enhanced analysis system based on a complex power production operation scene, which comprises, a data acquisition module 100, a multi-modal fusion analysis module 200, a visual decision platform module 300, a safety closed-loop execution module 400, and a knowledge management engine module 500; The data acquisition module 100 comprises a multi-source sensing unit 101 and a heterogeneous data analysis unit 102. The multi-source sensing unit 101 is used to collect acoustic, optical, thermodynamic and electromagnetic signals in real time in the power production field. The heterogeneous data analysis unit 102 is used to structure the device log, operation and maintenance record and environmental monitoring data; The multi-modal fusion analysis module 200 associates the power equipment operation state through a knowledge graph reasoning unit 201 and a deep learning prediction unit 202, generates a device health assessment report and a fault warning signal, and transmits the analysis result to the safety closed-loop execution module 400; The visual decision platform module 300 supports the operator to adjust the system strategy through a three-dimensional digital twin interface or a natural language instruction, and the generated control instruction is transmitted to the safety closed-loop execution module 400 in real time; The safety closed-loop execution module 400 comprises a control verification unit 401 and an instruction distribution unit 402. The control verification unit 401 is embedded in the power equipment controller to realize state adjustment. The instruction distribution unit 402 receives the output instruction of the multi-modal fusion analysis module 200 and the visual decision platform module 300, and drives the control verification unit 401 to perform verification operation after safety verification; The knowledge management engine module 500 integrates historical fault cases and expert experience library, continuously optimizes the algorithm model of the multi-modal fusion analysis module 200 through an incremental learning unit 501, and feeds back the operation and maintenance decision data to the visual decision platform module 300.

[0024] The multi-source sensing unit 101 comprises a distributed microphone array, an infrared thermal imager, a camera, a laser radar and a three-phase current transformer, wherein: The distributed microphone array is used to collect device operation abnormal sound signals, and the sampling frequency is not less than 44.1 kHz; The infrared thermal imager is used to capture the temperature field distribution of the surface of the equipment, and the temperature measurement range is -20℃ to 300℃. The high-definition industrial camera is used to collect the appearance and operation scene video stream of the equipment, and the resolution is not less than 1920*1080 pixels. The laser radar is used to generate three-dimensional point cloud data of the working area, and the point cloud density is not less than 100 points / m2. The three-phase current transformer is used to collect the current signal at the incoming line end of the equipment, and the measurement accuracy is 0.2 level.

[0025] The structured processing of the heterogeneous data analysis unit 102 includes: Field extraction is performed on the equipment log, and the timestamp, equipment ID, operating parameter and abnormal code are retained; Entity recognition is performed on the operation and maintenance record to generate triadic data including fault phenomenon, treatment measures and executor; The environmental monitoring data is spatio-temporally aligned to establish a multi-dimensional database indexed by latitude and longitude coordinates and time axis.

[0026] The knowledge graph reasoning unit 201 constructs a power equipment topology knowledge graph, which includes equipment entity nodes, component association edges and operating rule attributes, and realizes the following reasoning through a graph neural network algorithm: Fault tracing reasoning based on spatio-temporal association rules to locate the initial abnormal point causing the chain fault; Influence range reasoning based on component dependency relationship to predict the impact of a single equipment fault on related systems.

[0027] The deep learning prediction unit 202 adopts a multi-modal feature fusion model based on an attention mechanism, and the input layer of the multi-modal feature fusion model includes: The multi-source sensing unit 101 collects acoustic signal spectrum features, optical image texture features, thermodynamic temperature distribution features and electromagnetic signal waveform features; The heterogeneous data analysis unit 102 outputs structured data time series features; The output layer of the model includes equipment health score and three-level fault warning signal.

[0028] The equipment health score output by the deep learning prediction unit 202 is calculated by a weighted summation formula: H=α1S1+α2S2+α3S3+α4S4+α5S5 Wherein: H is the total score of the equipment health; S1 is the acoustic signal evaluation score, reflecting the abnormality of the equipment operation abnormal sound, which is calculated from the signal-to-noise ratio of the acoustic signal spectrum features; S2 is an optical feature evaluation score, determined based on the defect detection result of the equipment appearance image; S3 is a thermodynamic state score, calculated according to the deviation rate of the equipment surface temperature field distribution and the standard temperature threshold; S4 is an electromagnetic signal stability score, determined by the fluctuation amplitude of the current and voltage signals; S5 is a historical operation and maintenance matching degree score, obtained by comparing the similarity of the current state and the historical health state; α1 to α5 are weight coefficients of each dimension score, satisfying α1+α2+α3+α4+α5=1, and the weight values are dynamically updated by the incremental learning unit 501 of the knowledge management engine module 500 every month. The initial weight of the transformer equipment is set as α1=0.2, α2=0.15, α3=0.25, α4=0.2, and α5=0.2, and the initial weight of the transmission line equipment is set as α1=0.1, α2=0.2, α3=0.15, α4=0.3, and α5=0.25.

[0029] The natural language instruction processing flow of the visual decision platform module 300 includes: The voice instruction of the operator is converted into a text instruction by the voice recognition unit; The text instruction is subjected to intent recognition by the semantic understanding unit, and is mapped into a control parameter executable by the system; A parameter adjustment preview interface is generated, and after confirmation by the operator, it is synchronized to the safe closed-loop execution module 400.

[0030] The state adjustment process of the control verification unit 401 includes: The adjustment instruction of the instruction distribution unit 402 is received, and the target parameter and the adjustment threshold are analyzed; the difference between the current state parameter and the target parameter of the power equipment is calculated, and a stepwise adjustment curve is generated; The parameter correction operation is performed according to the adjustment curve, and the adjustment effect is fed back to the visual decision platform module 300 in real time.

[0031] The optimization process of the incremental learning unit 501 of the knowledge management engine module 500 includes: New data mining is performed on the historical fault case library every week, and the mapping relationship between the fault features and the processing scheme is extracted; The updated content of the expert experience library is collected every month, and the expert decision logic is converted into a model interpretable rule by a knowledge distillation algorithm; The model of the deep learning prediction unit 202 is fine-tuned based on the operation and maintenance decision data every quarter, and the fault warning threshold and the health evaluation weight are updated.

[0032] Example 2 The second embodiment of the present application, which is different from the first embodiment, further comprises a test preparation and implementation process. This embodiment takes a 110kV main transformer of a certain 220kV substation as the monitoring object, and the application system performs a 4-day operation state evaluation. The transformer model is SFZ11-50000 / 110, with a service life of 5 years, mainly undertaking regional power distribution hub functions, and needs to realize real-time monitoring and fault warning of health status through multi-modal data acquisition. Multi-source sensing unit 101: Deploy a distributed microphone array, an infrared thermal imager, a 4K industrial camera, a laser radar, and a 0.2-level three-phase current transformer in the transformer body and the surrounding area. Heterogeneous data analysis unit 102: Interface with the transformer operation and maintenance system to extract structured data including: daily load curve, insulating oil chromatographic analysis data, and historical fault records. Acoustic signal: The noise spectrum is concentrated in 60-200Hz during normal operation, and high-frequency components above 300Hz appear during abnormal operation. Temperature data: The normal top oil temperature is 35-45℃, and the hot spot temperature does not exceed 65℃. Current signal: The rated current is 800A, and the allowable fluctuation range is ±5%. The deep learning prediction unit 202 analyzes the collected data to obtain the index scores (S1-S5) as shown in the following table: Table 1: Index score table of collected data

[0033] Health degree calculation: Transformer weight coefficient: α1=0.2, α2=0.15, α3=0.25, α4=0.2, α5=0.2.

[0034] Day 1: H=0.2×92+0.15×95+0.25×90+0.2×96+0.2×98=93.8 points (normal).

[0035] Day 2: H=0.2×91+0.15×94+0.25×88+0.2×95+0.2×97=92.2 points (normal).

[0036] Day 3: H=0.2×85+0.15×93+0.25×72+0.2×94+0.2×96=85.1 points (warning).

[0037] Day 4: H=0.2×78+0.15×90+0.25×65+0.2×92+0.2×95=80.6 points (warning).

[0038] Table 2: Comparison table of operation monitoring data

[0039] Through 4-day continuous operation monitoring, the system successfully realizes the whole-process automation from multi-modal data acquisition to closed-loop control, verifying the effectiveness of the system technical features.

[0040] Embodiment 3 The third embodiment of the present application differs from the first embodiment and the second embodiment in that it further includes test preparation and implementation process: The test selects six typical power consumption areas of a provincial capital city power grid as the electricity sales prediction object, including commercial core area, high-density residential area, emerging industrial area, science and technology park area, old urban area and agricultural demonstration area, aiming to verify the accuracy and adaptability of the system in multi-scene electricity sales prediction. The test period is from July 1 to December 31, 2024, and a control group and an experimental group are set up, both of which use the same basic data source and prediction period. In the system deployment stage, multi-source data acquisition schemes are configured according to different regional characteristics: intelligent electric meters and passenger flow sensors are deployed in 10 main commercial circles in the commercial core area; a group of temperature and humidity sensors and electric behavior recognition terminals are installed every 200 households in the high-density residential area; the production plan system of 3 key enterprises in the emerging industrial area is connected to obtain daily production capacity data; photovoltaic output monitoring instruments and enterprise R&D investment statistical terminals are deployed in the science and technology park area; power grid renovation progress data and resident household number change records are collected in the old urban area; irrigation electricity metering devices and weather station data interfaces are connected in the agricultural demonstration area. In the data acquisition stage, multi-source sensing units synchronously acquire multi-modal signals of six types of areas: electrical signals cover real-time power consumption load, voltage fluctuation, power factor; environmental signals include daily average temperature and humidity, rainfall, light intensity; social and economic signals include regional passenger flow, enterprise capacity, R&D investment; time characteristic signals cover workday and holiday identification, seasonal attribute, policy adjustment node. The heterogeneous data analysis unit automatically connects the power marketing system, weather platform and statistical bureau database at 3 am every day to extract structured data including: daily electricity sales in the past 5 years, user type proportion, monthly GDP growth, electricity price adjustment record. The operation flow of the multi-modal fusion analysis module is: generating the next day's electricity sales prediction at 9 o'clock every day, generating the current week's prediction at 9 o'clock every Monday, and generating the next month's prediction at 15 o'clock on the last day of the month. The knowledge graph reasoning unit calls the electricity sales influencing factor topology library and identifies the key influencing factor weights through the graph attention network; the deep learning prediction unit loads the multi-modal feature data of the previous 30 days and outputs the time-of-use electricity sales prediction results. The visualization decision platform generates the prediction curve and confidence interval, supports dispatch personnel to adjust the prediction parameters through the touch screen, and the instructions are updated after the secondary verification of the safety closed loop execution module. The knowledge management engine starts incremental learning on the 5th of each month, integrates 20-30 new electricity sales deviation cases and 5-8 expert experiences of the previous month, optimizes the model through transfer learning, and improves the adaptability to complex scenarios. Table 3: Comparison table of commercial core area electricity sales prediction data

[0041] Table 4: Comparison table of high-density residential area electricity sales prediction data

[0042] Table 5: Comparison table of emerging industrial area electricity sales prediction data

[0043] Table 6: Comparison table of science and technology park electricity sales prediction data

[0044] Table 7: Comparison table of old urban area electricity sales prediction data

[0045] Table 8: Comparison table of agricultural demonstration area electricity sales prediction data

[0046] From the electricity sales prediction data of the six types of areas, the system shows significant advantages in complex electricity consumption scenarios. Its innovation lies in three dimensions: multi-modal influencing factor fusion, knowledge-enhanced prediction mechanism, and dynamic correction capability, effectively making up for the inherent defects of traditional electricity sales prediction technology. The core limitation of traditional electricity sales prediction methods is the dependence on single-modal data and the lack of scene adaptability. Test data from the commercial core area shows that traditional methods only predict based on historical electricity sales, ignoring the synergistic effects of passenger flow and air temperature, resulting in a prediction error of 6.8% on Monday. However, by integrating passenger flow and air temperature threshold effects into the analysis through multi-modal fusion, the system reduces the error to 2.1% and improves the accuracy by 69.1%. The comparison in the agricultural demonstration area is more representative: the traditional method's prediction of 88MWh historical data on Thursday has an error of 11.5% due to the lack of consideration of the 3mm rainfall's inhibitory effect on irrigation electricity. By integrating irrigation area and rainfall characteristics, the system reduces the error to 3.7%, verifying the creative value of multi-source data complementarity. The multi-modal fusion analysis mechanism shows stronger robustness in complex scenarios. Saturday data from the emerging industrial area shows that the traditional method has a prediction error of 15.0% for 180MWh historical electricity sales due to its inability to identify the correlation between "enterprise work rate 0%" and "production capacity 0 million tons". However, by using the knowledge graph reasoning unit to mine the topological relationship between "work rate - production capacity - electricity", and combining the feature weights output by the deep learning model, the system reduces the error to 4.2%, with a decrease of 72.0%. In the coupled influence analysis of server load and R&D investment in the science and technology park, the traditional method has a prediction error of 8.2% due to isolated data processing. By integrating 128-dimensional electricity load features and 24-dimensional economic features, the system captures the implicit rule of "R&D investment increasing by 10,000 yuan, electricity consumption +2%", and reduces the error to 2.5%, demonstrating the academic innovation of multi-modal feature interaction. The incremental learning mechanism of the knowledge management engine realizes the continuous optimization of prediction performance. The power grid reconstruction progress data in the old urban area shows that the system's prediction error for the "reconstruction 60%" stage is 3.0% in the first month. After integrating the expert experience of "reconstruction every 10% increase in electricity consumption by 3%", the error for the "reconstruction 70%" stage decreases to 3.3% in the third month, significantly improving the model's adaptability. The extreme weather case in the high-density residential area is more convincing: the traditional method has a prediction deviation of 8% for the 30°C high temperature on Friday, while the system reduces the error from 3.0% to 2.8% after absorbing the previous month's extreme high temperature case through incremental learning, verifying the creative adaptation of the knowledge enhancement mechanism to dynamic scenarios. The verification mechanism of the safety closed-loop execution module further guarantees the reliability of the prediction. After the commercial core area dispatchers issue the "correct holiday weight" instruction, the system performs historical error verification and logical verification, and corrects the Saturday prediction value from 220MWh to 215MWh, with a deviation of only 3.2% from the actual value, which is 69.5% higher than the traditional manual correction. This closed-loop design of "prediction - verification - correction" solves the problem of blind human intervention in traditional open-loop prediction, demonstrating the engineering practical value. In summary, the test data of the six types of regions prove from different dimensions that the system breaks through the limitations of traditional single-factor prediction through multi-modal data fusion, improves the adaptability in complex scenarios with the help of knowledge enhancement mechanism, and has significantly better power selling prediction accuracy, response efficiency and robustness than the prior art, thereby providing an innovative solution for accurate scheduling and transaction decision of the power market. In the description of the present application, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as limiting the present application; the terms "first", "second", "third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance; in addition, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connecting" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be direct connection, or indirect connection through an intermediate medium; it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0047] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multimodal knowledge augmentation analysis system based on complex power production operation scenarios, characterized in that, It includes a data acquisition module (100), a multimodal fusion analysis module (200), a visualization decision-making platform module (300), a secure closed-loop execution module (400), and a knowledge management engine module (500). The data acquisition module (100) includes a multi-source sensing unit (101) and a heterogeneous data parsing unit (102). The multi-source sensing unit (101) is used to collect acoustic, optical, thermodynamic and electromagnetic signals from the power production site in real time. The heterogeneous data parsing unit (102) is used to perform structured processing on equipment logs, operation and maintenance records and environmental monitoring data. The multimodal fusion analysis module (200) associates the operating status of power equipment with the knowledge graph reasoning unit (201) and the deep learning prediction unit (202), generates equipment health assessment reports and fault warning signals, and transmits the analysis results to the safety closed-loop execution module (400). The visualization decision platform module (300) allows operators to adjust system strategies through a three-dimensional digital twin interface or natural language commands, and the generated control commands are synchronized to the safety closed-loop execution module (400) in real time. The safety closed-loop execution module (400) includes a control verification unit (401) and an instruction distribution unit (402). The control verification unit (401) is embedded in the power equipment controller to realize state adjustment. The instruction distribution unit (402) receives the output instructions from the multimodal fusion analysis module (200) and the visualization decision platform module (300), and drives the control verification unit (401) to perform the verification operation after safety verification. The knowledge management engine module (500) integrates historical fault cases and expert experience base, continuously optimizes the algorithm model of the multimodal fusion analysis module (200) through the incremental learning unit (501), and feeds back the operation and maintenance decision data to the visualization decision platform module (300).

2. The multimodal knowledge enhancement analysis system based on complex power production operation scenarios according to claim 1, characterized in that, The multi-source sensing unit (101) includes a distributed microphone array, an infrared thermal imager, a camera, a lidar, and a three-phase current transformer, wherein: The distributed microphone array is used to collect abnormal noise signals from the equipment, and the infrared thermal imager is used to capture the temperature field distribution on the surface of the equipment. The camera is used to capture video streams of the device's appearance and operating scenarios; The lidar is used to generate three-dimensional point cloud data of the work area; The three-phase current transformer is used to collect the current signal at the incoming end of the equipment.

3. The multimodal knowledge enhancement analysis system based on complex power production operation scenarios according to claim 1, characterized in that, The structured processing of the heterogeneous data parsing unit (102) includes: Extract fields from device logs, retaining timestamps, device IDs, operating parameters, and exception codes; Perform entity recognition on operation and maintenance records to generate triplet data containing fault symptoms, handling measures, and executors; Spatiotemporal alignment of environmental monitoring data was performed to establish a multidimensional database indexed by latitude and longitude coordinates and time axis.

4. The multimodal knowledge enhancement analysis system based on complex power production operation scenarios according to claim 1, characterized in that, The knowledge graph reasoning unit (201) constructs a power equipment topology knowledge graph, which includes equipment entity nodes, component association edges, and operating rule attributes. The following reasoning is achieved through a graph neural network algorithm: Fault source tracing reasoning based on spatiotemporal correlation rules locates the initial anomaly point that triggers a chain of faults; Based on the impact range reasoning of component dependencies, predict the impact of a single device failure on related systems.

5. The multimodal knowledge enhancement analysis system based on complex power production operation scenarios according to claim 1, characterized in that, The deep learning prediction unit (202) employs a multimodal feature fusion model based on an attention mechanism, the input layer of which includes: The multi-source sensing unit (101) collects acoustic signal spectrum features, optical image texture features, thermodynamic temperature distribution features, and electromagnetic signal waveform features; The heterogeneous data parsing unit (102) outputs structured data time series features, and the output layer of the model includes equipment health score and three-level fault warning signal; The three-level fault warning signals are divided into warning, alarm, and emergency shutdown based on the equipment health score from high to low. The device health score output by the deep learning prediction unit (202) is calculated using a weighted summation formula: H=α1S1+α2S2+α3S3+α4S4+α5S5 in: H represents the total health score of the equipment; S1 is the acoustic signal evaluation score, which reflects the degree of abnormality of the abnormal noise during equipment operation. It is calculated from the signal-to-noise ratio of the acoustic signal spectrum characteristics. S2 is the optical feature evaluation score, determined based on the defect detection results of the equipment appearance image; S3 is the thermodynamic state score, calculated based on the deviation rate between the equipment surface temperature field distribution and the standard temperature threshold. S4 is the electromagnetic signal stability score, which is determined by the fluctuation amplitude of current and voltage signals; S5 is the historical operation and maintenance matching score, which is obtained by comparing the similarity between the current status and the historical health status. α1 to α5 are the weight coefficients of the scores for each dimension, satisfying α1+α2+α3+α4+α5=1, and the weight values ​​are dynamically updated monthly by the incremental learning unit (501) of the knowledge management engine module (500).

6. The multimodal knowledge enhancement analysis system based on complex power production operation scenarios according to claim 1, characterized in that, The natural language instruction processing flow of the visualization decision-making platform module (300) includes: The voice command from the operator is converted into a text command through a voice recognition unit; The semantic understanding unit performs intent recognition on the text instructions and maps them into control parameters that the system can execute. A parameter adjustment preview interface is generated, and after confirmation by the operator, it is synchronized to the safety closed-loop execution module (400).

7. The multimodal knowledge enhancement analysis system based on complex power production operation scenarios according to claim 1, characterized in that, The state adjustment process of the control verification unit (401) includes: Receive adjustment instructions from instruction distribution unit (402), and parse target parameters and adjustment thresholds; The difference between the current state parameters and the target parameters of the power equipment is calculated to generate a step-by-step adjustment curve; Perform parameter correction operations according to the adjustment curve, and provide real-time feedback on the adjustment effect to the visualization decision-making platform module (300).

8. The multimodal knowledge enhancement analysis system based on complex power production operation scenarios according to claim 1, characterized in that, The optimization process of the incremental learning unit (501) of the knowledge management engine module (500) includes: Every week, new data mining is performed on the historical failure case database to extract the mapping relationship between failure characteristics and handling solutions; We collect updated content from the expert experience database every month and use a knowledge distillation algorithm to transform expert decision-making logic into rules that the model can interpret. The model of the deep learning prediction unit (202) is fine-tuned quarterly based on operation and maintenance decision data, and the fault warning threshold and health assessment weight are updated.

9. An electronic device, characterized in that... include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the system as described in any one of claims 1-8.

10. A computer-readable storage medium having executable instructions stored thereon, characterized in that... When executed by the processor, this instruction causes the processor to implement the system as described in any one of claims 1-8.

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