Power supply data intelligent management method adaptive to park power supply system

Abstract

The invention belongs to the technical field of park power supply supervision, and particularly relates to a power supply data intelligent management method adaptive to a park power supply system, which comprises park power supply data acquisition, power equipment health hazard analysis, park power transmission loss analysis, park power supply supervision optimization and real-time early warning. The method focuses on the individual health of power equipment, accurately identifies high-risk power supply influence equipment, evaluates the electric energy transmission loss risk through multi-level logic, quantifies multiple parameters, accurately reflects the power supply stability, excavates the risk based on three core dimensions of equipment health, power transmission loss and power supply stability, and improves the power supply reliability. Dual prevention and control of dominant and potential risks are realized, so that a park power supervisor can accurately position a risk core, supervision difficulty and response time are greatly reduced, and safe, stable and continuous operation of a park power system is ensured.

Description

Technical Field

[0001] This invention relates to the field of power supply monitoring technology in industrial parks, specifically a method for intelligent management of power supply data adapted to industrial park power supply systems. Background Technology

[0002] As the core carrier of industrial production, commercial operation and public services, the power supply system of the park covers a variety of power equipment, complex power transmission network and diversified power load. The safety, stability and efficiency of power supply directly determine the overall operating efficiency of the park. With the continuous growth of electricity demand in the park, higher requirements are put forward for the intelligent management of the power supply system.

[0003] Currently, some park power supply monitoring technologies have emerged in the industry, attempting to replace traditional manual inspections with automation. For example, Chinese invention patent CN120069531A discloses a smart park power monitoring and management system. This invention's technical solution involves continuously collecting equipment operation data, with the equipment health assessment layer calculating and updating the equipment health index in real time. When the index is lower than a preset threshold, an early warning is automatically triggered to notify maintenance personnel to respond to equipment problems. However, although the above-mentioned inventions can monitor the health status of equipment and provide early warning of faults, their data collection and analysis focus on equipment operating parameters and health status, without integrating multi-dimensional data on transmission losses and core data on power supply stability. They cannot comprehensively assess the risks of the park's power system and provide accurate early warnings, nor can they fully reflect the overall operating status of the park's power supply system. This is not conducive to ensuring the safe, stable and continuous operation of the park's power system. Therefore, a solution is proposed. Summary of the Invention

[0004] The purpose of this invention is to overcome the above-mentioned technical defects and provide an intelligent management method for power supply data that is adapted to the power supply system of a park.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent management method for power supply data adapted to a park power supply system, comprising the following steps: Step 1: The power supply data multi-dimensional acquisition and processing module comprehensively collects real-time operation data and historical data of the park's power supply system, preprocesses and merges the collected multi-dimensional power supply data, forms a power supply fusion dataset, and outputs it in real time. Step 2: The power equipment health hazard output module analyzes the health hazards of power equipment in the park one by one. Through analysis, the corresponding power equipment is marked as equipment that affects power supply or equipment that stabilizes the system, and the marking information of all power equipment is sent to the park power supply optimization decision module. Step 3: The park's power transmission loss analysis module analyzes the power transmission loss status of the park, assigns a power loss warning symbol TX-1 or TX-2 based on the analysis, and sends the power loss warning symbol TX-1 or TX-2 to the park's power supply monitoring and optimization module. Step 4: The park power supply monitoring and optimization module analyzes the data to determine whether to generate a monitoring and optimization alarm signal. If a monitoring and optimization alarm signal is generated, it is sent to the park power supply monitoring terminal. Step 5: When the power supply monitoring terminal in the park receives the monitoring optimization alarm signal, it issues a corresponding early warning.

[0006] Furthermore, in step two, the specific analysis process of the power equipment health hazard output module includes: The system acquires information about the power equipment in the park, collects the actual load of the corresponding power equipment and calculates the equipment load rate L by comparing it with the corresponding rated load, marks the excess value of the operating temperature of the corresponding power equipment compared with the ambient temperature as the equipment temperature rise value T, and collects the usage time t and insulation resistance value Q of the corresponding power equipment. The health hazard coefficient Rrisk is obtained by analyzing and calculating the equipment health hazard analysis formula. The health hazard coefficient Rrisk is compared with the corresponding preset health hazard coefficient threshold Rmax. If Rrisk ≥ Rmax, the corresponding power equipment is marked as equipment that affects power supply; if Rrisk < Rmax, the corresponding power equipment is marked as stable equipment.

[0007] Furthermore, in step three, the specific analysis process of the park's transmission loss analysis module is as follows: Several detection periods are set within a unit of time. The input power and actual power consumption of the park are collected within the corresponding detection period. The difference between the input power and the actual power consumption is calculated to obtain the power loss value of the park. The power loss value of the park is compared with the preset power loss threshold of the park. If the power loss value of the park exceeds the preset power loss threshold of the park, the corresponding detection period is marked as the power loss period. The number of power loss periods per unit time is obtained and the ratio is calculated with the total number of detection periods to obtain the power loss occupancy value. The power loss occupancy value is compared with the preset power loss occupancy threshold. If the power loss occupancy value exceeds the preset power loss occupancy threshold, a power loss warning symbol TX-1 is assigned.

[0008] Furthermore, if the measured power loss value does not exceed the preset power loss measurement threshold, the power loss value of the park during all detection periods within a unit of time is averaged to obtain the power loss performance value, and the park power loss value with the largest value is marked as the power loss amplitude value. The power loss characteristic value is calculated by weighting and summing the measured power loss value, the power loss performance value, and the power loss amplitude value. The power loss characteristic value is then compared with a preset power loss characteristic threshold. If the power loss characteristic value exceeds the preset power loss characteristic threshold, a power loss warning symbol TX-1 is assigned; if the power loss characteristic value does not exceed the preset power loss characteristic threshold, a power loss warning symbol TX-2 is assigned.

[0009] Furthermore, in step four, the specific analysis process of the park power supply monitoring and optimization module is as follows: The system obtains the marking information of all power equipment in the park, as well as the power loss warning symbol TX-1 or TX-2 in the park. If the power loss warning symbol TX-1 is received or there are power supply-affecting devices in the park, a regulatory optimization alarm signal is generated. If a power loss warning symbol TX-2 is received and there are no power supply-affecting devices in the park, the power supply supervision optimization coefficient is obtained through analysis. The power supply supervision optimization coefficient is then compared with the preset power supply supervision optimization coefficient threshold. If the power supply supervision optimization coefficient exceeds the preset power supply supervision optimization coefficient threshold, a supervision optimization alarm signal is generated.

[0010] Furthermore, the specific method for analyzing and obtaining the power supply supervision optimization coefficient is as follows: The health hazard coefficients of all power equipment in the park are obtained. The ratio of the health hazard coefficient of the corresponding power equipment to the corresponding preset health hazard coefficient threshold is calculated to obtain the health hazard ratio value. The average of the health hazard ratio values ​​of all power equipment is calculated to obtain the health hazard characteristic value. The power loss characteristic value and stability influence coefficient of the park are retrieved. The power supply supervision optimization coefficient is obtained by weighted summation of the health hazard characteristic value, power loss characteristic value and stability influence coefficient.

[0011] Furthermore, the power supply supervision and optimization decision-making module of the park is connected to the power supply stability impact analysis module. The power supply stability impact analysis module analyzes the power supply stability risk status of the park, obtains the stability impact coefficient of the park through analysis, and sends the stability impact coefficient to the power supply supervision and optimization decision-making module of the park.

[0012] Furthermore, the specific analysis process of the power supply stability impact analysis module is as follows: The deviation of the operating voltage of the park's power supply system from the rated voltage is obtained and marked as the park voltage fluctuation coefficient, and the deviation of the operating frequency of the park's power supply system from the rated frequency is marked as the park frequency fluctuation coefficient; the additional reserve power supply capacity that can be called up at any time in the park's power supply system is collected and marked as the standby capacity value, and the power supply standby capacity risk value is calculated by the ratio of the preset standby capacity safety threshold to the standby capacity value; the real-time power supply stability coefficient is calculated by weighted summation of the park voltage fluctuation coefficient, the park frequency fluctuation coefficient and the power supply standby capacity risk value. A rectangular coordinate system is established with time as the X-axis and the real-time power supply stability coefficient as the Y-axis. Based on all real-time power supply stability coefficients within a unit time, a power supply stability curve is plotted in the first quadrant of the rectangular coordinate system. A stability standard ray is plotted in the first quadrant of the rectangular coordinate system, parallel to the X-axis and with its endpoint located on the Y-axis. The Y-axis coordinate value corresponding to the stability standard ray is the preset threshold value of the real-time power supply stability coefficient. The region enclosed by the power supply stability curve located above the stability standard ray and the stability standard ray is obtained. The region is filled, and the area of ​​all filled regions is summed to calculate the stability influence coefficient.

[0013] Furthermore, the park power supply supervision optimization decision module communicates with the supervision effectiveness assessment module. When the park power supply supervision optimization decision module does not generate a supervision optimization alarm signal, the supervision effectiveness assessment module analyzes the supervision effectiveness performance of the park power system during the detection period and determines whether a supervision effectiveness abnormal signal is generated. When a supervision effectiveness abnormal signal is generated, it is sent to the park power supply supervision terminal. When the park power supply supervision terminal receives the supervision effectiveness abnormal signal, it issues a corresponding warning.

[0014] Furthermore, the specific analysis process for the regulatory effectiveness assessment module is as follows: The frequency of power supply system failures in the park during the detection period is obtained and marked as the power supply failure coefficient. When the power supply system failure in the park causes a power outage, the duration and area of ​​the power outage are compared with the preset duration threshold and the preset area threshold respectively. The number of power outages during the detection period whose duration or area exceeds the corresponding preset threshold is marked as the power supply failure risk coefficient. The total amount of loss caused by the power supply system failure in the park during the detection period is marked as the power supply loss coefficient. The effectiveness impact value is calculated by weighting and summing the power supply failure coefficient, power supply risk factor, and power supply loss coefficient. The effectiveness impact value is then compared with the preset effectiveness impact threshold. If the effectiveness impact value exceeds the preset effectiveness impact threshold, an abnormal signal for regulatory effectiveness is generated.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, by focusing on the individual health of power equipment to accurately identify high-risk power supply impact equipment, and by assessing the risk of power transmission loss through multi-level logic, and quantifying multiple parameters to accurately reflect power supply stability, the comprehensive judgment based on the three core dimensions of equipment health, transmission loss and power supply stability achieves dual prevention and control of explicit and potential risks. This helps park power regulators to accurately locate the core risks, significantly reduce the difficulty of supervision and response time, and ensure the safe, stable and continuous operation of the park's power system.

[0016] 2. In this invention, the regulatory effectiveness assessment module evaluates the actual effect of the park's power system regulatory measures based on previous operating data when no regulatory optimization alarm signal is generated. It promptly identifies hidden deficiencies in the regulatory plan and generates abnormal signals, promotes iterative optimization of the regulatory plan, strengthens the supervision of the park's power system in a timely manner, and further ensures the long-term safe, stable, and efficient operation of the park's power supply system. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings; Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a system block diagram of Embodiment 1 of the present invention; Figure 3 This is a system block diagram of Embodiment 2 of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: As Figure 1-2 As shown, the present invention proposes an intelligent management method for power supply data adapted to a park power supply system, comprising the following steps: Step 1: The multi-dimensional power supply data acquisition and processing module comprehensively collects real-time and historical data of the park's power supply system. It preprocesses (including noise filtering, missing value imputation, etc.) and merges the collected multi-dimensional power supply data to form a power supply fusion dataset and outputs it in real time. This avoids the limitations of single data types or scattered data, ensuring the integrity, accuracy, and usability of the data. It provides multi-dimensional data support for subsequent intelligent analysis and ensures the accuracy of subsequent analysis results from the source.

[0020] Step Two: The Power Equipment Health Hazard Output Module analyzes the health hazards of each power device in the park (such as transformers, circuit breakers, and other types of power equipment). Through analysis, the corresponding power devices are marked as either power supply impact devices or stability devices, and the marking information of all power devices is sent to the park's power supply optimization decision-making module. This not only achieves a refined assessment of the operational risks of each power device, avoiding omissions or misjudgments of equipment hazards, but also provides information support for the analysis process of the park's power supply optimization decision-making module, ensuring the accuracy of its analysis results. The specific analysis process is as follows: The system acquires information about the power equipment in the park, collects the actual load of the corresponding power equipment and calculates the equipment load rate L by comparing it with the corresponding rated load, marks the excess value of the operating temperature of the corresponding power equipment compared with the ambient temperature as the equipment temperature rise value T, and collects the usage time t and insulation resistance value Q of the corresponding power equipment. The health hazard coefficient Rrisk is obtained by analyzing and calculating using the equipment health hazard analysis formula, as follows: Where k is a preset equipment type coefficient and k > 0; it should be noted that the larger the value of the health hazard coefficient Rrisk, the higher the operating risk of the corresponding power equipment. The health hazard coefficient Rrisk is compared with the corresponding preset health hazard coefficient threshold Rmax. If Rrisk ≥ Rmax, it indicates that the operation risk of the corresponding power equipment is high, and the corresponding power equipment is marked as a power supply-affecting device. If Rrisk < Rmax, it indicates that the operation risk of the corresponding power equipment is low, and the corresponding power equipment is marked as a stable device.

[0021] Step 3: The park's transmission loss analysis module analyzes the park's transmission loss status and assigns either a power loss warning symbol TX-1 or TX-2. This TX-1 or TX-2 warning symbol is then sent to the park's power supply monitoring and optimization module. This not only accurately assesses the power transmission performance of the park's power system but also provides information support for the park's power supply optimization decision-making module. This ensures that subsequent monitoring and optimization analysis considers both equipment health and power transmission efficiency, further guaranteeing the accuracy of the analysis results. The specific analysis process is as follows: Several detection periods are set within a unit of time. The input power and actual power consumption of the park are collected within the corresponding detection period. The difference between the input power and the actual power consumption is calculated to obtain the power loss value of the park. The power loss value of the park is compared with the preset power loss threshold of the park. If the power loss value of the park exceeds the preset power loss threshold of the park, the corresponding detection period is marked as the power loss period. The number of power loss periods per unit time is obtained and the ratio is calculated with the total number of detection periods to obtain the power loss pre-measured value. The power loss pre-measured value is compared with the preset power loss pre-measured threshold. If the power loss pre-measured value exceeds the preset power loss pre-measured threshold, it indicates that the power transmission risks in the park's power system are relatively serious, and a power loss warning symbol TX-1 is assigned.

[0022] If the measured power loss value does not exceed the preset power loss measurement threshold, the power loss value of the park during all detection periods within a unit of time is averaged to obtain the power loss performance value, and the park power loss value with the largest value is marked as the power loss amplitude value. The characteristic value of power loss is obtained by weighted summation of the measured value of power loss, the performance value of power loss, and the amplitude value of power loss. Specifically, the measured value of power loss, the performance value of power loss, and the amplitude value of power loss are each assigned a corresponding preset weight coefficient. The measured value of power loss, the performance value of power loss, and the amplitude value of power loss are then multiplied by the corresponding preset weight coefficient, and the sum of the three sets of products is marked as the characteristic value of power loss. It should be noted that the larger the value of the power loss characteristic value, the more serious the overall power transmission risks in the park's power system. The power loss characteristic value is compared with the preset power loss characteristic threshold. If the power loss characteristic value exceeds the preset power loss characteristic threshold, it indicates that the overall power transmission risks in the park's power system are relatively serious, and a power loss warning symbol TX-1 is assigned. If the power loss characteristic value does not exceed the preset power loss characteristic threshold, it indicates that the overall power transmission performance of the park's power system is relatively good, and a power loss warning symbol TX-2 is assigned.

[0023] Step 4: The park power supply monitoring and optimization module optimizes the park power supply monitoring. It analyzes the data to determine whether a monitoring optimization alarm signal is generated. When a monitoring optimization alarm signal is generated, it is sent to the park power supply monitoring terminal to remind the park power monitoring personnel to take corresponding optimization measures in a timely manner. This can not only quickly respond to explicit problems such as high equipment risk and high power loss, but also uncover potential comprehensive hidden dangers under "low power loss + no explicit equipment risk" through the power supply monitoring optimization coefficient, thus achieving dual prevention and control of explicit and potential risks. Furthermore, it enables park power regulators to accurately pinpoint core risks and quickly and effectively implement reasonable control and optimization measures, significantly reducing the difficulty and complexity of park power supply supervision, improving the timeliness and relevance of regulatory responses, and ensuring the safe, stable, and continuous operation of the park's power system with a high level of intelligence and automation; the specific analysis process is as follows: The system obtains the marking information of all power equipment in the park, as well as the power loss warning symbol TX-1 or TX-2 in the park. If the power loss warning symbol TX-1 is received or there are equipment in the park that affect power supply, it indicates that there is a potential safety hazard in the park's power system and that corresponding optimization measures need to be taken in a timely manner. In this case, a regulatory optimization alarm signal is generated. If the power loss warning symbol TX-2 is received and there are no power supply-affecting devices in the park, the health hazard coefficients of all power equipment in the park are obtained. The ratio of the health hazard coefficient of the corresponding power equipment to the corresponding preset health hazard coefficient threshold is calculated to obtain the health hazard ratio value. The average of the health hazard ratio values ​​of all power equipment is calculated to obtain the health hazard characteristic value. The power loss characteristic value and stability impact coefficient of the park are retrieved. The power supply supervision optimization coefficient is obtained by weighted summation of the health hazard characteristic value, power loss characteristic value, and stability impact coefficient. Specifically, each of the three coefficients is assigned a pre-defined weight, and then multiplied by its respective pre-defined weight. The sum of these three products is then labeled as the power supply supervision optimization coefficient. It should be noted that a larger power supply supervision optimization coefficient indicates a more severe overall safety hazard in the park's power system. The power supply supervision optimization coefficient is compared with the preset power supply supervision optimization coefficient threshold. If the power supply supervision optimization coefficient exceeds the preset power supply supervision optimization coefficient threshold, it indicates that the overall safety hazards of the park's power system are relatively serious, and a supervision optimization alarm signal is generated.

[0024] Step 5: When the power supply monitoring terminal in the park receives the monitoring optimization alarm signal, it issues a corresponding early warning.

[0025] Furthermore, the power supply supervision and optimization decision-making module of the park is connected to the power supply stability impact analysis module. The power supply stability impact analysis module analyzes the power supply stability risk status of the park, obtains the stability impact coefficient of the park through analysis, and sends the stability impact coefficient to the power supply supervision and optimization decision-making module of the park. This accurately quantifies the stability risk of the park's power supply system and fills the gap in the lack of quantitative assessment of power supply stability in traditional management. Furthermore, its output stability impact coefficient provides important stability dimension data for the park's power supply monitoring and optimization module, enabling monitoring and optimization decisions to cover the three core dimensions of "equipment health - transmission loss - power supply stability," further enhancing the scientific rigor and comprehensiveness of the monitoring and optimization analysis. The specific analysis process of the power supply stability impact analysis module is as follows: The deviation of the operating voltage of the park's power supply system from the rated voltage is obtained and marked as the park's voltage fluctuation coefficient, and the deviation of the operating frequency of the park's power supply system from the rated frequency is marked as the park's frequency fluctuation coefficient; and the additional reserve power supply capacity that can be called up at any time in the park's power supply system is collected and marked as the standby capacity value, and the power supply standby capacity risk value is obtained by calculating the ratio of the preset standby capacity safety threshold to the standby capacity value. The real-time power supply stability coefficient is calculated by weighting and summing the voltage fluctuation coefficient, frequency fluctuation coefficient, and power supply backup capacity risk value within the park. Specifically, each of the three factors is assigned a corresponding preset weight coefficient, and each is multiplied by its respective preset weight coefficient. The sum of these three products is then labeled as the real-time power supply stability coefficient. It should be noted that a larger real-time power supply stability coefficient indicates a higher overall level of power supply stability risk within the park. A rectangular coordinate system is established with time as the X-axis and the real-time power supply stability coefficient as the Y-axis. Based on all real-time power supply stability coefficients within a unit time, a power supply stability curve is plotted in the first quadrant of the rectangular coordinate system. A stability standard ray is plotted in the first quadrant of the rectangular coordinate system, parallel to the X-axis and with its endpoint located on the Y-axis. The Y-axis coordinate value corresponding to the stability standard ray is the preset threshold value of the real-time power supply stability coefficient. The region enclosed by the power supply stability curve located above the stability standard ray and the stability standard ray is obtained. The region is filled, and the areas of all filled regions are summed to calculate the stability influence coefficient. It should be noted that the larger the value of the stability influence coefficient, the worse the overall power supply stability of the park is per unit time.

[0026] Example 2: Figure 3 As shown, the difference between this embodiment and Embodiment 1 is that the park power supply supervision optimization decision module is connected to the supervision effectiveness assessment module. When the park power supply supervision optimization decision module does not generate a supervision optimization alarm signal, the supervision effectiveness assessment module analyzes the supervision effectiveness performance of the park power system during the detection period and determines whether to generate a supervision effectiveness abnormal signal. When an abnormal signal of regulatory effectiveness is generated, it is sent to the park's power supply monitoring terminal. Upon receiving the abnormal signal, the park's power supply monitoring terminal issues a corresponding warning to remind park power regulators to adjust the park's power regulation measures and plans in a timely manner, strengthen the supervision of the park's power supply system, and further ensure the long-term safe, stable, and efficient operation of the park's power supply system. The specific analysis process is as follows: The frequency of power supply system failures in the park during the detection period is obtained and marked as the power supply failure coefficient. When the power supply system failure in the park causes a power outage, the duration and area of ​​the power outage are compared with the preset duration threshold and the preset area threshold respectively. The number of power outages during the detection period whose duration or area exceeds the corresponding preset threshold is marked as the power supply failure risk coefficient. The total amount of loss caused by the power supply system failure in the park during the detection period is marked as the power supply loss coefficient. The effectiveness impact value is obtained by weighting and summing the power supply failure coefficient, power supply failure coefficient, and power supply loss coefficient. Specifically, the power supply failure coefficient, power supply failure coefficient, and power supply loss coefficient are each assigned a corresponding preset weight coefficient, and the power supply failure coefficient, power supply failure coefficient, and power supply loss coefficient are multiplied by the corresponding preset weight coefficients. The sum of the three sets of products is then marked as the effectiveness impact value. It should be noted that the larger the value of the effectiveness impact value, the worse the overall performance of the park's supervision effectiveness. When the effectiveness impact value is compared with the preset effectiveness impact threshold, if the effectiveness impact value exceeds the preset effectiveness impact threshold, it indicates that the overall performance of the park's supervision effectiveness is poor and supervision of the park's power supply system needs to be strengthened in a timely manner, thus generating a supervision effectiveness anomaly signal.

[0027] The working principle of this invention is as follows: During use, the multi-dimensional power supply data acquisition and processing module collects multi-dimensional power supply data from the park; the power equipment health hazard output module focuses on the individual health of equipment and accurately identifies high-risk equipment affected by power supply; the park transmission loss analysis module evaluates the risk of power transmission loss through multi-level logic; the power supply stability impact analysis module quantifies various parameters and accurately reflects power supply stability; and the park power supply supervision optimization decision module mines risks from three core dimensions: equipment health, transmission loss, and power supply stability. It can quickly respond to explicit problems such as high-risk equipment and high power loss, and can also mine potential comprehensive hazards under low power loss and no explicit equipment risks, achieving dual prevention and control of explicit and potential risks. This helps park power regulators accurately locate the core risks, significantly reduces the difficulty of supervision and response time, and ensures the safe, stable, and continuous operation of the park's power system.

[0028] In this invention, the threshold, preset value, or preset range settings are for result comparison and analysis to determine whether the result is good or bad. The magnitude of these values ​​is determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions. Similarly, the preset weight coefficients and influence factors are assigned specific values ​​based on the magnitude of each parameter's influence on the result, ultimately reflecting the impact on the result. These settings are also determined by a combination of large-scale model analysis of sample data and human experience, and can also be appropriately adjusted based on seasonal or common-sense influence conditions.

[0029] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for intelligent management of power supply data adapted to a park power supply system, characterized in that, Includes the following steps: Step 1: The multi-dimensional power supply data acquisition and processing module comprehensively collects real-time and historical data of the park's power supply system, and preprocesses and integrates the collected multi-dimensional power supply data. Step 2: The power equipment health hazard output module analyzes the health hazards of power equipment in the park one by one, and marks the corresponding power equipment as equipment that affects power supply or equipment that stabilizes the system through analysis. Step 3: The park's power transmission loss analysis module analyzes the power transmission loss status of the park and assigns a power loss warning symbol TX-1 or TX-2 based on the analysis. Step 4: The park power supply monitoring and optimization module analyzes the data to determine whether to generate a monitoring and optimization alarm signal. If a monitoring and optimization alarm signal is generated, it is sent to the park power supply monitoring terminal. Step 5: When the power supply monitoring terminal in the park receives the monitoring optimization alarm signal, it issues a corresponding early warning.

2. The intelligent management method for power supply data adapted to a park power supply system according to claim 1, characterized in that, The specific analysis process of the power equipment health hazard output module includes: The actual load of the corresponding power equipment is collected and the ratio of it to the corresponding rated load is calculated to obtain the equipment load rate. The excess value of the operating temperature of the corresponding power equipment compared with the ambient temperature is marked as the equipment temperature rise value. The usage time and insulation resistance value of the corresponding power equipment are also collected. The health hazard coefficient Rrisk is calculated. If Rrisk ≥ Rmax, the corresponding power equipment is marked as equipment that affects power supply; otherwise, the corresponding power equipment is marked as stable equipment.

3. The intelligent management method for power supply data adapted to a park power supply system according to claim 1, characterized in that, The specific analysis process of the power transmission loss analysis module in the industrial park is as follows: Several detection periods are set within a unit of time. If the power loss value of the park exceeds the preset power loss threshold, the corresponding detection period is marked as a power loss period. The number of power loss periods per unit time is obtained and the ratio is calculated with the total number of detection periods to obtain the power loss occupancy value. If the power loss occupancy value exceeds the preset power loss occupancy threshold, a power loss warning symbol TX-1 is assigned.

4. The intelligent management method for power supply data adapted to a park power supply system according to claim 3, characterized in that, If the measured power loss value does not exceed the preset power loss measurement threshold, the power loss characteristic value is calculated by weighted summation of the measured power loss value, the power loss performance value, and the power loss amplitude value. If the power loss characteristic value exceeds the preset power loss characteristic threshold, the power loss warning symbol TX-1 is assigned. Otherwise, assign the power loss warning symbol TX-2.

5. The intelligent management method for power supply data adapted to a park power supply system according to claim 1, characterized in that, The specific analysis process of the power supply monitoring and optimization module in the park is as follows: The system acquires the marking information of all power equipment in the park, as well as the power loss warning symbol TX-1 or TX-2. If a power loss warning symbol TX-1 is received or there are power supply-affecting devices in the park, a regulatory optimization alarm signal is generated. If a power loss warning symbol TX-2 is received and there are no power supply-affecting devices in the park, the power supply regulatory optimization coefficient is obtained through analysis. If the power supply regulatory optimization coefficient exceeds the preset power supply regulatory optimization coefficient threshold, a regulatory optimization alarm signal is generated.

6. The intelligent management method for power supply data adapted to a park power supply system according to claim 5, characterized in that, The specific method for analyzing and obtaining the power supply supervision optimization coefficient is as follows: The ratio of the health hazard coefficient of the corresponding power equipment to the corresponding preset health hazard coefficient threshold is used to calculate the health hazard ratio value. The average of the health hazard ratio values ​​of all power equipment is used to calculate the health hazard characteristic value. The power supply supervision optimization coefficient is obtained by weighted summation of the health hazard characteristic value, power loss characteristic value and stability influence coefficient.

7. The intelligent management method for power supply data adapted to a park power supply system according to claim 5, characterized in that, The power supply supervision and optimization decision-making module of the park is connected to the power supply stability impact analysis module. The power supply stability impact analysis module analyzes the power supply stability risk status of the park and sends the stability impact coefficient to the power supply supervision and optimization decision-making module of the park.

8. The intelligent management method for power supply data adapted to a park power supply system according to claim 7, characterized in that, The specific analysis process of the power supply stability impact analysis module is as follows: Based on all real-time power supply stability coefficients within a unit time, a power supply stability curve is plotted in the first quadrant of a rectangular coordinate system. The region enclosed by the power supply stability curve above the stability standard ray and the stability standard ray is obtained. The enclosed region is filled, and the areas of all filled regions are summed to calculate the stability influence coefficient.

9. The intelligent management method for power supply data adapted to a park power supply system according to claim 7, characterized in that, The park power supply supervision optimization decision module communicates with the supervision effectiveness assessment module. When the park power supply supervision optimization decision module does not generate a supervision optimization alarm signal, the supervision effectiveness assessment module analyzes the supervision effectiveness performance of the park power system during the detection period and sends it to the park power supply supervision terminal when an abnormal supervision effectiveness signal is generated.

10. A method for intelligent management of power supply data adapted to a park power supply system according to claim 9, characterized in that, The specific analysis process of the regulatory effectiveness assessment module is as follows: the effectiveness impact value is calculated by weighting and summing the power supply failure coefficient, power supply risk factor and power supply loss coefficient. If the effectiveness impact value exceeds the preset effectiveness impact threshold, an abnormal regulatory effectiveness signal is generated.

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