Intelligent sensing grid steady-state adaptive cruise control method and control device

Abstract

The present invention discloses an intelligently perceived grid steady-state adaptive cruise control method and control device, relating to the technical field of power systems. The method comprises: establishing a device set comprising a plurality of grid devices with location identifiers; reading a predetermined set of key points and labeling them with the devices to a three-dimensional grid model to generate a target grid model; performing dynamic cruise monitoring of the grid according to a predetermined cruise strategy, recording real-time cruise information and rendering it into the grid model; determining whether the real-time cruise record meets grid constraints based on the grid model; if so, issuing a steady-state analysis instruction and evaluating the real-time steady-state index using a steady-state evaluation function; and issuing a control instruction when the steady-state index is not within a predetermined threshold, and executing grid control. This achieves the technical effect of improving the dynamic adjustment capability of monitoring and optimizing monitoring response efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an intelligently perceived grid steady-state adaptive cruise control method and a control device. Background Art

[0002] In power grid management, steady-state control and patrol monitoring are key components in ensuring safe and stable grid operation. Existing grid steady-state monitoring and control systems typically rely on fixed monitoring points, monitoring routes, and pre-set control strategies. These systems lack dynamic adaptability and are unable to cope with the complex and ever-changing realities of the power grid. Technical issues include a single monitoring model, delayed monitoring response, and an inability to adapt to dynamic changes. Summary of the Invention

[0003] The present invention provides an intelligent-perception grid steady-state adaptive cruise control method and control device to solve the technical problems in the prior art of single monitoring mode, delayed monitoring response, and inability to adapt to dynamic changes, thereby achieving the technical effects of improving the dynamic adjustment capability of monitoring and optimizing the monitoring response efficiency.

[0004] In a first aspect, the present invention provides a method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception, wherein the method comprises:

[0005] A device set is formed, where the device set refers to a collection of devices in a target power grid, and the device set includes multiple devices with location identifiers. A predetermined key point set is read, and the predetermined key point set and the multiple devices with location identifiers are marked in a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid. Dynamic cruise monitoring is performed on the target power grid based on a predetermined cruise strategy to obtain real-time cruise records, and the real-time cruise records are rendered into the target power grid model. A determination is made, based on the target power grid model, as to whether the real-time cruise records comply with predetermined power grid constraints. If so, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced based on the steady-state analysis instruction to evaluate and analyze the real-time cruise records to obtain a real-time steady-state index. When the real-time steady-state index is not within a predetermined steady-state index threshold, a control processing instruction is issued, and control of the target power grid is executed based on the control processing instruction.

[0006] In a second aspect, the present invention further provides an intelligent sensing grid steady-state adaptive cruise control device, wherein the control device comprises:

[0007] The device set construction module is used to construct a device set. The device set refers to a collection of devices in the target power grid, and the device set includes multiple devices with location identifiers.

[0008] A power grid model marking module, which is used to read a predetermined set of key points and mark the predetermined set of key points and the multiple devices with location identifiers into a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid.

[0009] A dynamic cruise monitoring module is used to perform dynamic cruise monitoring on the target power grid based on a predetermined cruise strategy, obtain real-time cruise records, and render the real-time cruise records to the target power grid model.

[0010] A compliance determination module is used to determine whether the real-time cruise record complies with predetermined grid constraints in combination with the target grid model.

[0011] The steady-state analysis module is used to issue a steady-state analysis instruction if it meets the requirements, and introduce a steady-state evaluation function based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index.

[0012] The discrimination and regulation module is used to issue a regulation processing instruction when the real-time steady-state index is not within a predetermined steady-state index threshold, and to perform regulation on the target power grid based on the regulation processing instruction.

[0013] The present invention discloses an intelligently perceived grid steady-state adaptive cruise control method and control device, comprising: constructing a device set, which refers to a collection of devices in a target grid and includes multiple devices with location identifiers; reading a predetermined key point set and marking it and the multiple devices with location identifiers into a three-dimensional grid model to generate a target grid model, which is a three-dimensional structural representation of the target grid; performing dynamic cruise monitoring on the target grid based on a predetermined cruise strategy, generating real-time cruise records, and rendering the real-time cruise records into the target grid model; determining whether the real-time cruise records meet predetermined grid constraints based on the target grid model; if they meet, issuing a steady-state analysis instruction, and introducing a steady-state evaluation function based on the instruction to evaluate and analyze the real-time cruise records to obtain a real-time steady-state index; and issuing a control processing instruction when the real-time steady-state index is not within a predetermined steady-state index threshold range, and performing corresponding control operations on the target grid according to the instruction. The intelligently perceived grid steady-state adaptive cruise control method and control device disclosed in the present invention solve the technical problems of a single monitoring mode, delayed monitoring response, and inability to adapt to dynamic changes, thereby achieving the technical effect of improving the dynamic adjustment capability of monitoring and optimizing monitoring response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 Schematic diagram of the flow of the intelligent sensing grid steady-state adaptive cruise control method of the present invention;

[0015] Figure 2 This is a structural diagram of the intelligent sensing grid steady-state adaptive cruise control device of the present invention.

[0016] Explanation of the reference numerals: equipment set construction module 11 , power grid model marking module 12 , dynamic cruise monitoring module 13 , compliance determination module 14 , steady-state analysis module 15 , determination and control module 16 . DETAILED DESCRIPTION

[0017] The technical solutions provided in the embodiments of the present invention are designed to solve the technical problems of the prior art, such as the single monitoring mode, delayed monitoring response, and inability to adapt to dynamic changes. The overall concept adopted is as follows:

[0018] First, a device set is assembled. This device set refers to the collection of all devices in the target power grid, each with unique location information. Next, a predetermined set of key points is retrieved, combined with the device set, and marked within a 3D power grid model to generate the target power grid model. This 3D power grid model intuitively displays the 3D structure of the power grid, including the locations of all key devices and nodes. After obtaining the target power grid model, dynamic patrol monitoring of the target power grid is performed based on a predetermined patrol strategy. Patrol information is recorded in real time and rendered into the 3D power grid model. Next, the target power grid model determines whether these patrol records meet predetermined grid constraints. If the patrol records meet these constraints, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced to evaluate and analyze the patrol records to determine a real-time steady-state index. If the real-time steady-state index falls outside the predetermined steady-state index threshold, a control processing instruction is issued to execute control operations on the target power grid to ensure safe and stable operation.

[0019] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings. Example 1

[0020] Figure 1 The figure is a flow chart of the method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to the present invention, wherein the method comprises:

[0021] A device set is formed, where the device set refers to a collection of devices in a target power grid, and the device set includes a plurality of devices with location identifiers.

[0022] Specifically, the target grid management platform or the target grid equipment database is first interacted with to obtain the equipment information of multiple devices in the target grid, thereby forming a device set. The device set is a collection of multiple devices, and each device has its own location identifier, so as to quickly determine the location of multiple devices for grid analysis, optimization or management. Among them, the equipment included in the device set includes substations, transmission lines, switchgear, etc.

[0023] For example, an API (application programming interface) or other interface is used to obtain device information from the power grid management platform or device database, including device type, status, location, energy consumption, production date, maintenance history, etc. The location information corresponding to multiple devices is then extracted and marked as location identifiers to generate the aforementioned device set.

[0024] A predetermined key point set is read, and the predetermined key point set and the multiple devices with location identifiers are marked in a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid.

[0025] Specifically, the predetermined key point set refers to a collection of multiple power grid equipment locations requiring adaptive cruise control. This corresponds to a set of critical power equipment with specific importance or functionality requiring adaptive cruise control for endpoint control. Examples include substations, important distribution equipment, and critical transmission lines. The location information of these key points may be stored in the predetermined key point set and accessible via a programming interface or file read operation.

[0026] Specifically, after obtaining the predetermined set of key points, multiple key points and multiple power devices are marked in the 3D power grid model to determine their locations within the 3D model. The resulting 3D structural model, including all key points and devices, is used for power grid analysis, optimization, or management.

[0027] In some embodiments, target multi-dimensional feature information of the target power grid is acquired, and the three-dimensional power grid model is constructed according to target structural feature information in the target multi-dimensional feature information.

[0028] The three-dimensional power grid model is a digital model of the target power grid constructed by using GIS software or specialized three-dimensional modeling software.

[0029] Specifically, the interactive power grid management platform or device database first obtains multidimensional feature information about devices, including their location, type, status, and energy consumption; the grid's topology, power flow information, and operational status; and geographic information about the grid's coverage area (such as topography, landforms, and building distribution). A three-dimensional power grid model is then constructed based on the structural feature information within this multi-dimensional feature information. This structural feature information includes device location information and the grid's topology. For example, this involves determining the specific location of each device in three-dimensional space based on the device's location information (including longitude, latitude, and altitude). Then, based on the grid's topology and topological connection paths, the digital models of the multiple devices obtained through location mapping are connected. Finally, the grid model is integrated with the geographic information to ensure its accurate positioning in geographic space, thereby generating the aforementioned three-dimensional power grid model.

[0030] The three-dimensional power grid model created through the above method steps helps to better understand the structure and operation status of the power grid by marking the location of the equipment and the topology of the power grid in the model, thereby making better decisions.

[0031] In some embodiments, the target multidimensional feature information also includes target line feature information; the target line feature information is analyzed to obtain first line feature information of a first line intersection; the first line feature information is normalized and weighted to obtain a first weight of the first line intersection; when the first weight reaches a predetermined weight threshold, the first line intersection is recorded as a first key point, and the predetermined key point set is formed.

[0032] Specifically, the above-mentioned target multi-dimensional feature information also includes target line feature information, wherein the line feature information refers to the detailed information of each line in the power grid, including line length, line type, line electrical characteristics, line operating status, etc.

[0033] Specifically, the target line characteristic information is analyzed to obtain first line characteristic information for the first line intersection. This includes analyzing the line geometry and topology, identifying the intersections of each line in the power grid, and extracting relevant characteristic information from the identified line intersections, such as the intersection's geographic location, the number of connected lines, and the intersection's electrical characteristics (such as load capacity and voltage level). The extracted line characteristic information is then normalized to eliminate dimensional differences between different features. The normalized characteristic information is weighted based on the importance of each feature, and a comprehensive weight is calculated for each line intersection, which is output as a first weight. This first weight is used to quantitatively reflect the importance of the line intersection and to quantitatively represent the importance of different intersections.

[0034] Optionally, a predetermined weight threshold is set to determine whether a line intersection is a key point. When the weight of a line intersection reaches or exceeds the predetermined weight threshold, the line intersection is recorded as a key point. Furthermore, all identified key points are stored as a predetermined key point set, which provides a target reference for the subsequent construction of the 3D power grid model.

[0035] Dynamic cruise monitoring is performed on the target power grid based on a predetermined cruise strategy to obtain real-time cruise records, and the real-time cruise records are rendered to the target power grid model.

[0036] Specifically, dynamic patrol monitoring of your target power grid is continued according to a pre-defined patrol strategy. This pre-defined patrol strategy refers to patrol plans and rules established in power grid management for periodic or irregular inspection and monitoring of equipment and key points within the power grid. For example, this pre-defined patrol strategy controls power grid inspection equipment or personnel to perform dynamic patrol monitoring.

[0037] In some embodiments, the predetermined cruise strategy includes a predetermined device cruise strategy and a predetermined key point cruise strategy, wherein the predetermined device cruise strategy includes a predetermined device cruise path and a predetermined device cruise period, and the predetermined key point cruise strategy includes a predetermined key point cruise path and a predetermined key point cruise period;

[0038] Performing dynamic cruise monitoring on the device set in the target power grid according to the predetermined device cruise path to obtain real-time device cruise records;

[0039] Specifically, the predetermined equipment cruise path refers to one or more cruise routes pre-set according to the distribution of power grid equipment. The path covers all key equipment in the power grid to ensure that the operating status of the equipment can be fully monitored during the cruise process.

[0040] Specifically, a scheduled device cruise cycle refers to a pre-set cruise frequency based on the device's importance and operating status. Different devices require different cruise cycles to ensure timely detection and resolution of problems. For example, devices are categorized and prioritized based on their importance, failure rate, and historical data to determine the cruise cycles for different devices. Furthermore, by combining big data analysis and machine learning techniques, potential equipment failure risks can be predicted and cruise cycles can be dynamically adjusted. By optimizing cruise cycles, we can optimize cruise resource utilization and reduce maintenance costs while ensuring equipment safety.

[0041] Specifically, a predefined key point patrol route refers to one or more predefined patrol routes based on the distribution of key points in the power grid (such as substations and switch stations). These routes cover all key points, ensuring comprehensive monitoring of their operating status during the patrol process. In other words, the predefined key point patrol route is the route for patrol monitoring of lines or other grid components outside the equipment.

[0042] Specifically, the predetermined cruise cycle for key points refers to a pre-set cruise frequency based on the importance and operating status of the key points. Different key points may require different cruise cycles to ensure timely detection and resolution of problems. Exemplary methods for determining the predetermined cruise cycle for key points include: utilizing risk assessment models to conduct risk analysis on key points and determine the cruise cycle; dynamically adjusting the cruise cycle by real-time monitoring the operating status of key points, combining historical data and prediction models, etc. By rationally setting the cruise cycle for key points, it is possible to optimize the utilization of cruise resources and improve the effectiveness of cruise while ensuring the safety of key points.

[0043] Furthermore, dynamic patrol monitoring is performed on the target power grid's equipment set based on the predefined patrol routes, generating real-time patrol records. Dynamic patrol monitoring involves real-time monitoring of power grid equipment according to predefined patrol routes and cycles, recording the equipment's operating status and environmental parameters to promptly detect and address abnormalities. Specifically, this involves collecting real-time operational data from various sensors installed on the equipment (such as temperature, vibration, and current sensors). This data is transmitted to a monitoring center in real time via wireless communication technologies (such as LoRa and NB-IoT), where it is processed and analyzed to generate real-time patrol records.

[0044] Determine whether the real-time cruise record complies with predetermined grid constraints in combination with the target grid model.

[0045] Specifically, the predetermined grid constraints are determined in conjunction with the predetermined cruise strategy described above. These predetermined grid constraints are used to preliminarily determine whether real-time cruise records are usable data, thereby ensuring the validity and reliability of the real-time cruise records and meeting grid monitoring and analysis standards. Exemplary predetermined grid constraints include grid route constraints, data collection accuracy constraints, data collection time constraints, and data collection cycle constraints.

[0046] The power grid route constraint specifies that data collection must be conducted along a predetermined power grid route during patrol monitoring to ensure coverage of all key nodes and lines. The collection accuracy constraint specifies the accuracy requirements for data collected by patrol equipment to ensure data accuracy and reliability. The collection time constraint specifies that patrol equipment must complete data collection within a predetermined time period to ensure data timeliness. The collection period constraint specifies that patrol equipment must collect data at a predetermined interval to ensure data continuity and comparability.

[0047] If it is in compliance, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index.

[0048] Specifically, if all parameters in the real-time cruise record meet the predetermined grid constraints, the recorded data is preliminarily determined to be usable data. Correspondingly, if any parameter does not meet the predetermined grid constraints, the recorded data is preliminarily determined to be unusable data and a warning signal is issued.

[0049] Specifically, a steady-state analysis command is issued to assess the steady-state operation of a power grid when real-time cruise records meet predetermined grid constraints. A steady-state evaluation function is a mathematical function used to assess the steady-state operation of a power grid. This function combines various parameters from the real-time cruise records to calculate the grid's real-time steady-state index, thereby quantitatively evaluating the target grid's stability performance.

[0050] In some embodiments, evaluating and analyzing the real-time cruise record to obtain a real-time steady-state index includes:

[0051] The real-time device cruise record is analyzed in combination with the predetermined device cruise cycle to obtain first device operating status information of the first device, where the first device refers to any one device in the device set; the first device operating status information is analyzed to obtain a first device status index; dynamic cruise monitoring is performed on the predetermined key point set in the target power grid according to the predetermined key point cruise path to obtain real-time key point cruise records; the real-time key point cruise records are analyzed in combination with the predetermined key point cruise cycle to obtain a first load of the first key point; a target unit duration is determined according to the predetermined device cruise cycle and the predetermined key point cruise cycle; a steady-state analysis is performed on the first device status index and the first load according to the steady-state evaluation function to obtain the real-time steady-state index under the target unit duration.

[0052] Specifically, first, any one device in the device set is selected, and its operating status information is extracted to obtain the first device operating status information of the first device. The first device operating status information may include various parameter information such as voltage, current, temperature, load, power factor, harmonics, etc. Then, according to the state evaluation operator preset by the target power grid, combined with the operating status information of the first device, its state index is calculated and output as the first device state index, which quantifies the operating status of the first device and represents the overall health status of the device. The state evaluation operator is a weighted evaluation operator determined by professional technicians or expert systems of the target power grid according to the actual control needs of the target power grid, and different power grid devices may correspond to different state evaluation operators.

[0053] Specifically, according to a pre-defined patrol monitoring route for key points, a set of predefined key points in the target power grid are monitored in real time, collecting operational data from these key points. The patrol data for these key points is then processed based on the patrol monitoring periodicity of each point, and the first load is extracted from it. The load at a key point represents the power demand or transmission capacity of that point.

[0054] Specifically, the cruise cycles of equipment and key points are usually different, so in this step, we calculate the average of the two or set a fixed monitoring cycle to determine the target unit duration. This target unit duration will serve as the basic time period for analyzing the steady-state index.

[0055] Among them, a longer target unit duration helps reduce the impact of short-term fluctuations. In other words, short-term load fluctuations or equipment status changes can be averaged over a longer time frame, preventing short-term anomalies from affecting the overall assessment results, thereby obtaining a more stable operating status index and improving the representativeness and accuracy of the data. A shorter target unit duration, on the other hand, helps achieve more timely and sensitive operating status monitoring. In other words, a short target unit duration can promptly capture operating status changes of equipment or key points in the power grid, especially in the event of emergencies or abnormal equipment status. This helps to identify problems in real time and quickly take countermeasures to prevent the problem from escalating.

[0056] Specifically, the steady-state evaluation function is an empirical function determined based on historical steady-state analysis data. It combines the device status index and load analysis to assess the stability of the device or system over a target time period. This steady-state evaluation function uses the first device status index and the first load as input parameters to evaluate the device's operating state under the current load and determine whether the device is operating stably and efficiently.

[0057] Optionally, the steady-state evaluation function is constructed based on mathematical analysis or machine learning methods. Exemplarily, mathematical analysis methods use statistical analysis, regression models, and other mathematical methods to analyze and model historical data, extracting core parameters that influence equipment steady-state (such as equipment load and temperature). Based on the weights and correlations of these parameters, a steady-state evaluation function is constructed. Exemplarily, machine learning methods use large amounts of historical data to train a machine learning model, automatically extracting complex correlations in steady-state analysis, and constructing a more accurate steady-state evaluation model. These methods include decision trees, support vector machines (SVMs), or neural networks.

[0058] Specifically, the real-time stability index measures system stability over a target unit of time. It reflects the balance between equipment and load at key points, and whether there are overloads, equipment malfunctions, or other unstable factors. For example, a higher stability index value indicates more stable equipment and better load matching.

[0059] By using the above-mentioned method steps and combining them with a steady-state evaluation function (whether based on mathematical analysis or machine learning methods) for real-time analysis, the current steady-state operation of the device or system can be quickly evaluated, thereby improving the accuracy of the analysis results.

[0060] In some implementations, if the predetermined device cruise cycle is greater than the predetermined key point cruise cycle, the first cycle duration of the predetermined device cruise cycle is obtained, and the first cycle duration is recorded as the target unit duration; if the predetermined device cruise cycle is less than the predetermined key point cruise cycle, the second cycle duration of the predetermined key point cruise cycle is obtained, and the second cycle duration is recorded as the target unit duration.

[0061] Optionally, the longer cycle length between the predetermined equipment cruise cycle and the predetermined key point cruise cycle is used as the target unit duration, so that the analysis process uses all equipment status indexes and load information, which helps to improve the accuracy of the analysis.

[0062] In some implementations, the method further includes:

[0063] The target external feature information in the dynamic external feature record is extracted based on the target unit time length; the target external temperature feature information and the target external radiation feature information in the target external feature information are analyzed to obtain a target external interference coefficient; and the real-time steady-state index is adjusted based on the target external interference coefficient.

[0064] Optionally, the dynamic external characteristic record includes various characteristic information of the external environment, such as temperature, radiation, humidity, wind speed, etc. External temperature characteristic information and external radiation characteristic information within the target unit duration are extracted from the dynamic external characteristic record to reflect the external environment level of the target power grid within the target unit duration. Specifically, extreme temperature or high radiation level will affect the accuracy of monitoring data and the stability of the target power grid.

[0065] Specifically, by analyzing historical data or conducting interference experiments, the impact characteristics of external characteristic information on the target power grid are determined, and an external interference evaluation function is determined. Then, the acquired external temperature characteristic information and external radiation characteristic information are input into the external interference evaluation function to perform interference analysis and output a corresponding target external interference coefficient. For example, the greater the external interference, the greater the corresponding external interference coefficient, and the external interference coefficient is greater than or equal to 1.

[0066] Optionally, an adjustment formula can be used to adjust the real-time steady-state index based on the target external interference coefficient. The adjusted real-time steady-state index is the product of the real-time steady-state index and the target external interference coefficient. By extracting external characteristic information within the target unit time, analyzing characteristics such as temperature and radiation, calculating the target external interference coefficient, and adjusting the real-time steady-state index based on the interference coefficient, the impact of the external environment on the steady-state operation of the power grid can be more accurately reflected. This helps improve the accuracy and reliability of power grid operation monitoring and ensure the safe and stable operation of the power grid.

[0067] When the real-time steady-state index is not within a predetermined steady-state index threshold, a control processing instruction is issued, and the target power grid is controlled based on the control processing instruction.

[0068] Specifically, if the real-time steady-state index falls below a predetermined steady-state index threshold, a control processing instruction is generated. Based on this control processing instruction, real-time cruise records are used to locate the control target and match the control plan. This adaptive control of the target power grid is then performed to ensure the grid returns to steady-state operation. Control plans include adjusting load distribution, activating backup power sources, and adjusting generator output.

[0069] In some embodiments, the method further comprises:

[0070] Obtain a first cruise time of the first device operating status information; generate a first device status time series of the first device according to a first correspondence between the first cruise time and the first device status index; obtain a second cruise time of the first load; generate a first load time series of a first key point according to a second correspondence between the second cruise time and the first load; perform trend forecasting analysis on the first device status time series and the first load time series in turn to obtain a first forecast state index and a first forecast load respectively; perform steady-state analysis based on the steady-state evaluation function in combination with the first forecast state index and the first forecast load to obtain a forecast steady-state index; when the forecast steady-state index is not within the predetermined steady-state index threshold, issue a feedforward control processing instruction; and perform predictive control on the target power grid based on the feedforward control processing instruction.

[0071] Specifically, the first device state time series and the first load time series refer to serialized device state information and device load information, reflecting the state changes of the target device over time. By performing trend analysis on the first device state time series and the first load time series, the state change pattern or regularity of the target device can be obtained, thereby predicting the device state at a certain point in the future, facilitating forward-looking feedforward control of the target power grid. Prediction methods include time series analysis and machine learning.

[0072] Furthermore, based on the same method principle as the above-mentioned steady-state analysis, according to the obtained predicted state index and the first predicted load, combined with the steady-state evaluation function, analysis is performed to obtain a predicted steady-state index. The predicted steady-state index reflects the predicted stability level of the equipment at a certain time node in the future. If the predicted steady-state index is not within the predetermined steady-state index threshold range, a control instruction is issued in advance to prevent potential grid instability and ensure the steady-state operation of the grid in the future time period.

[0073] By acquiring equipment operating status and load information, generating corresponding time-series data, and performing trend forecasting analysis on this data, potential grid operation issues can be identified in advance. If the predicted steady-state index falls below the predetermined steady-state index threshold, a feedforward control processing instruction is issued, and predictive control is executed based on this instruction, ensuring the safe and stable operation of the grid and improving its operational reliability and response capabilities.

[0074] In summary, the intelligent perception grid steady-state adaptive cruise control method provided by the present invention has the following technical effects:

[0075] The system constructs a device set (a device set refers to a collection of devices in a target power grid, including multiple devices with location identifiers); reads a predetermined key point set and tags it and multiple devices with location identifiers into a three-dimensional power grid model to generate a target power grid model, which is a three-dimensional structural representation of the target power grid; based on a predetermined cruise strategy, it performs dynamic cruise monitoring on the target power grid, generates real-time cruise records, and renders the real-time cruise records into the target power grid model; combines the target power grid model with the target power grid model to determine whether the real-time cruise records meet the predetermined power grid constraints; if so, issues a steady-state analysis instruction, and based on this instruction, introduces a steady-state evaluation function to evaluate and analyze the real-time cruise records to obtain a real-time steady-state index; if the real-time steady-state index is not within the predetermined steady-state index threshold, issues a control processing instruction, and performs corresponding control operations on the target power grid based on this instruction. This achieves the technical effect of improving the dynamic adjustment capability of monitoring and optimizing the monitoring response efficiency. Example 2

[0076] Figure 2 This is a schematic diagram of the structure of the intelligent sensing grid steady-state adaptive cruise control device of the present invention. For example, Figure 1 The flow chart of the intelligent sensing grid steady-state adaptive cruise control method of the present invention can be shown as follows: Figure 2 The structure shown is implemented.

[0077] Based on the same concept as the intelligent-perception grid steady-state adaptive cruise control method in the above embodiment, the present invention also provides an intelligent-perception grid steady-state adaptive cruise control device comprising:

[0078] The device set building module 11 is used to build a device set, where the device set refers to a collection of devices in a target power grid, and the device set includes a plurality of devices with location identifiers.

[0079] The power grid model marking module 12 is used to read a predetermined key point set and mark the predetermined key point set and the multiple devices with location identifiers into a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid.

[0080] The dynamic cruise monitoring module 13 is configured to perform dynamic cruise monitoring on the target power grid based on a predetermined cruise strategy, obtain real-time cruise records, and render the real-time cruise records to the target power grid model.

[0081] The compliance determination module 14 is configured to determine whether the real-time cruise record complies with predetermined grid constraints in combination with the target grid model.

[0082] The steady-state analysis module 15 is configured to issue a steady-state analysis instruction if the conditions are met, and introduce a steady-state evaluation function based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index.

[0083] The determining and regulating module 16 is configured to issue a regulating and controlling instruction when the real-time steady-state index is not within a predetermined steady-state index threshold, and to perform regulation and controlling on the target power grid based on the regulating and controlling instruction.

[0084] The power grid model marking module 12 includes:

[0085] The line characteristic information analysis unit is used to analyze the target line characteristic information to obtain first line characteristic information of the first line intersection.

[0086] The line characteristic information normalization weighted processing unit is used to perform normalization weighted processing on the first line characteristic information to obtain a first weight of the first line intersection.

[0087] The weight threshold judgment and key point formation unit is used to record the first line intersection as a first key point and form the predetermined key point set when the first weight reaches a predetermined weight threshold.

[0088] In some embodiments, the dynamic cruise monitoring module 13 includes:

[0089] The device cruise monitoring and record acquisition unit is used to perform dynamic cruise monitoring on the device set in the target power grid according to the predetermined device cruise path to obtain real-time device cruise records.

[0090] A device operation status analysis and status index calculation unit is configured to analyze the real-time device cruise record in conjunction with the predetermined device cruise period to obtain first device operation status information of a first device, where the first device is any device in the device set, and to obtain a first device status index by analyzing the first device operation status information.

[0091] The key point cruise monitoring and record acquisition unit is used to perform dynamic cruise monitoring on the predetermined key point set in the target power grid according to the predetermined key point cruise path to obtain real-time key point cruise records.

[0092] The key point load analysis and calculation unit is used to analyze the real-time key point cruise record in combination with the predetermined key point cruise cycle to obtain a first load of a first key point.

[0093] The unit duration determination unit is used to determine the target unit duration according to the predetermined device cruise cycle and the predetermined key point cruise cycle.

[0094] A steady-state index calculation unit is used to perform a steady-state analysis on the first device status index and the first load according to the steady-state evaluation function to obtain the real-time steady-state index under the target unit time length.

[0095] In some embodiments, the steady-state analysis module 15 includes:

[0096] In some embodiments, the key point load analysis and calculation unit in the dynamic cruise monitoring module 13 is further used to:

[0097] If the predetermined device cruise cycle is greater than the predetermined key point cruise cycle, the first cycle duration of the predetermined device cruise cycle is obtained, and the first cycle duration is recorded as the target unit duration; if the predetermined device cruise cycle is less than the predetermined key point cruise cycle, the second cycle duration of the predetermined key point cruise cycle is obtained, and the second cycle duration is recorded as the target unit duration.

[0098] In some embodiments, the steady-state index calculation unit in the dynamic cruise monitoring module 13 includes:

[0099] The external feature information extraction unit is used to extract the target external feature information in the dynamic external feature record based on the target unit duration.

[0100] The external characteristic information analysis unit is used to analyze the target external temperature characteristic information and the target external radiation characteristic information in the target external characteristic information to obtain the target external interference coefficient.

[0101] A steady-state index adjustment unit is configured to adjust the real-time steady-state index based on the target external interference coefficient.

[0102] In some embodiments, the control device further comprises:

[0103] The device status information and cruise time association unit is used to obtain the first cruise time of the first device operation status information.

[0104] The device state sequence generating unit is configured to generate a first device state sequence of the first device according to a first correspondence between the first cruise time and the first device state index.

[0105] The load information and cruising time associating unit is used to obtain the second cruising time of the first load.

[0106] A load time sequence generating unit is configured to generate a first load time sequence of a first key point according to a second corresponding relationship between the second cruising time and the first load.

[0107] The time series prediction and analysis unit is used to perform trend prediction analysis on the first device state time series and the first load time series in sequence to obtain a first predicted state index and a first predicted load respectively.

[0108] The predicted steady-state index analysis unit is configured to perform a steady-state analysis based on the steady-state evaluation function in combination with the first predicted state index and the first predicted load to obtain a predicted steady-state index.

[0109] The feedforward control processing instruction issuing unit is used to issue a feedforward control processing instruction when the predicted steady-state index is not within the predetermined steady-state index threshold.

[0110] A predictive control execution unit is used to perform predictive control execution on the target power grid based on the feedforward control processing instruction.

[0111] In some embodiments, the control device further comprises:

[0112] The three-dimensional model construction unit is used to obtain target multi-dimensional feature information of the target power grid and construct the three-dimensional power grid model according to target structural feature information in the target multi-dimensional feature information.

[0113] It should be understood that the embodiments mentioned in this specification focus on their differences from other embodiments. The specific embodiments in the aforementioned embodiment one are also applicable to the intelligent sensing grid steady-state adaptive cruise control device described in embodiment two. For the sake of brevity of the specification, no further elaboration is given here.

[0114] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. An intelligent sensing grid steady-state adaptive cruise control method, characterized in that: include: Establishing a device set, wherein the device set refers to a collection of devices in the target power grid, and the device set includes multiple devices with location identifiers; Reading a predetermined key point set, and marking the predetermined key point set and the plurality of devices with location identifiers into a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid; Performing dynamic cruise monitoring on the target power grid based on a predetermined cruise strategy to obtain real-time cruise records, and rendering the real-time cruise records to the target power grid model; determining whether the real-time cruise record complies with predetermined grid constraints in combination with the target grid model; If it is in compliance, a steady-state analysis instruction is issued, and a steady-state evaluation function is introduced based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index; When the real-time steady-state index is not within a predetermined steady-state index threshold, issuing a control processing instruction, and performing control execution on the target power grid based on the control processing instruction; The predetermined cruise strategy includes a predetermined equipment cruise strategy and a predetermined key point cruise strategy, wherein the predetermined equipment cruise strategy includes a predetermined equipment cruise path and a predetermined equipment cruise cycle, and the predetermined key point cruise strategy includes a predetermined key point cruise path and a predetermined key point cruise cycle; Performing dynamic cruise monitoring on the device set in the target power grid according to the predetermined device cruise path to obtain real-time device cruise records; analyzing the real-time device cruise record in combination with the predetermined device cruise period to obtain first device operating status information of a first device, where the first device refers to any one device in the device set; Analyzing the first device operating status information to obtain a first device status index; Performing dynamic cruise monitoring on the predetermined key point set in the target power grid according to the predetermined key point cruise path to obtain real-time key point cruise records; Analyzing the real-time key point cruise record in combination with the predetermined key point cruise cycle to obtain a first load of a first key point; Determine the target unit duration according to the predetermined equipment cruise cycle and the predetermined key point cruise cycle; A steady-state analysis is performed on the first device state index and the first load according to the steady-state evaluation function to obtain the real-time steady-state index under the target unit time length.

2. The method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 1, characterized in that: Target multi-dimensional feature information of the target power grid is acquired, and the three-dimensional power grid model is constructed according to target structural feature information in the target multi-dimensional feature information.

3. The method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 2, characterized in that: include: The target multi-dimensional feature information also includes target line feature information; Analyzing the target line characteristic information to obtain first line characteristic information of a first line intersection; performing normalized weighting processing on the first line characteristic information to obtain a first weight of the first line intersection; When the first weight reaches a predetermined weight threshold, the first line intersection is recorded as a first key point, and the predetermined key point set is formed.

4. The method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 1, characterized in that: If the predetermined device cruise cycle is greater than the predetermined key point cruise cycle, the first cycle duration of the predetermined device cruise cycle is obtained, and the first cycle duration is recorded as the target unit duration; if the predetermined device cruise cycle is less than the predetermined key point cruise cycle, the second cycle duration of the predetermined key point cruise cycle is obtained, and the second cycle duration is recorded as the target unit duration.

5. The method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 1, characterized in that: Also includes: Extracting target external feature information from the dynamic external feature record based on the target unit duration; Analyzing target external temperature characteristic information and target external radiation characteristic information in the target external characteristic information to obtain a target external interference coefficient; The real-time steady-state index is adjusted based on the target external interference coefficient.

6. The method for controlling steady-state adaptive cruise control of a power grid based on intelligent perception according to claim 1, characterized in that: Also includes: Obtaining a first cruise time of the operating status information of the first device; generating a first device state time sequence of the first device according to a first correspondence between the first cruise time and the first device state index; Obtaining a second cruising time of the first load; generating a first load time series at a first key point according to a second corresponding relationship between the second cruising time and the first load; performing trend prediction analysis on the first device state time series and the first load time series in sequence to obtain a first predicted state index and a first predicted load, respectively; Performing a steady-state analysis based on the steady-state evaluation function in combination with the first predicted state index and the first predicted load to obtain a predicted steady-state index; When the predicted steady-state index is not within the predetermined steady-state index threshold, issuing a feedforward control processing instruction; Predictive regulation is performed on the target power grid based on the feedforward regulation processing instruction.

7. An intelligent sensing grid steady-state adaptive cruise control device, characterized in that: The control device is used to execute the intelligent perception grid steady-state adaptive cruise control method according to any one of claims 1 to 6, and the control device includes: A device set building module, wherein the device set building module is used to build a device set, wherein the device set refers to a collection of devices in the target power grid, and the device set includes a plurality of devices with location identifiers; A power grid model marking module, the power grid model marking module is used to read a predetermined key point set and mark the predetermined key point set and the plurality of devices with location identifiers into a three-dimensional power grid model to obtain a target power grid model, wherein the three-dimensional power grid model refers to a three-dimensional structural model of the target power grid; A dynamic cruise monitoring module, configured to perform dynamic cruise monitoring on the target power grid based on a predetermined cruise strategy, obtain real-time cruise records, and render the real-time cruise records to the target power grid model; a compliance determination module, configured to determine whether the real-time cruise record complies with predetermined grid constraints in combination with the target grid model; A steady-state analysis module, wherein the steady-state analysis module is configured to issue a steady-state analysis instruction if the conditions are met, and introduce a steady-state evaluation function based on the steady-state analysis instruction to evaluate and analyze the real-time cruise record to obtain a real-time steady-state index; The discrimination and regulation module is used to issue a regulation processing instruction when the real-time steady-state index is not within a predetermined steady-state index threshold, and to perform regulation on the target power grid based on the regulation processing instruction.

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