Method, device and equipment for adaptive adjustment of operating parameters of heat exchange station

Abstract

The application provides a heat exchange station operation parameter adaptive adjustment method, device and equipment, and relates to the technical field of urban central heating system. The method comprises the following steps: obtaining original operation data of each heat exchange station based on a preset period. According to the original operation data, an overall standardized score sequence corresponding to each preset index is generated; the overall standardized score sequence comprises scores of each heat exchange station. Based on the overall standardized score sequence corresponding to each preset index, target weights of each preset index are obtained; according to the overall standardized score sequence and the target weights of each preset index, the operation grade of each heat exchange station is determined. For each heat exchange station, an operation parameter adjustment target corresponding to the operation grade of the heat exchange station is determined; according to the operation parameter adjustment target, the actual heat supply and / or the circulating water flow of the heat exchange station are adjusted. The method is used to improve the regulation accuracy of the heating system.

Description

Technical Field

[0001] This application relates to the technical field of urban centralized heating systems, and more specifically, to a method, apparatus, and equipment for adaptive adjustment of operating parameters of a heat exchange station. Background Technology

[0002] Currently, in urban centralized heating systems, heat exchange stations, as intermediate heat exchange devices between heat sources and residential users, undertake the core functions of heat exchange, circulation and distribution, and regional heating load regulation. The operational quality of heat exchange stations directly determines the indoor heating temperature of users in the area, the overall heating energy consumption, and the long-term operational safety of the equipment. Most heating groups uniformly manage dozens to hundreds of decentralized heat exchange stations. Daily operation and maintenance inspections, equipment repairs, energy consumption control, and rectification of heating defects all require a unified, quantitative, and standardized hierarchical evaluation standard as a basis for management decision support.

[0003] In existing technologies, maintenance personnel typically rely on on-site manual inspections to subjectively judge the quality of heat exchange station operation and manually guide the optimization of heat exchange station operating parameters based on their judgments, resulting in poor accuracy in the control of the heating system. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, and equipment for adaptive adjustment of operating parameters of a heat exchange station, which solves the above-mentioned problems existing in the prior art and can improve the control accuracy of the heating system.

[0005] Firstly, an adaptive adjustment method for the operating parameters of a heat exchange station is provided, which may include: Based on a preset cycle, acquire the raw operating data of each heat exchange station; Based on the original operating data, an overall standardized score sequence corresponding to each preset indicator is generated; the overall standardized score sequence includes the scores of each heat exchange station; Based on the overall standardized score sequence corresponding to each preset indicator, the target weight of each preset indicator is obtained; according to the overall standardized score sequence and the target weight of each preset indicator, the operating level of each heat exchange station is determined. For each heat exchange station, determine the operating parameter adjustment target corresponding to the operating level of the heat exchange station; adjust the actual heat supply and / or circulating water flow rate of the heat exchange station according to the operating parameter adjustment target.

[0006] Secondly, an adaptive adjustment device for the operating parameters of a heat exchange station is provided, the device including: The acquisition module is used to acquire the raw operating data of each heat exchange station based on a preset cycle. The generation module is used to generate an overall standardized score sequence corresponding to each preset indicator based on the original operating data; the overall standardized score sequence includes the scores of each heat exchange station; The determination module is used to obtain the target weight of each preset indicator based on the overall standardized score sequence corresponding to each preset indicator; and to determine the operating level of each heat exchange station according to the overall standardized score sequence and the target weight of each preset indicator. The adjustment module is used to determine the operating parameter adjustment target corresponding to the operating level of each heat exchange station; and to adjust the actual heat supply and / or circulating water flow of the heat exchange station according to the operating parameter adjustment target.

[0007] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0008] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0009] This application provides a method, apparatus, and equipment for adaptive adjustment of operating parameters of a heat exchange station. Based on a preset cycle, it acquires raw operating data from each heat exchange station. According to the raw operating data, it generates an overall standardized score sequence corresponding to each preset indicator; the overall standardized score sequence includes the score of each heat exchange station. Based on the overall standardized score sequence corresponding to each preset indicator, it obtains the target weight of each preset indicator; based on the overall standardized score sequence and target weight of each preset indicator, it determines the operating level of each heat exchange station. For each heat exchange station, it determines the operating parameter adjustment target corresponding to the operating level of that heat exchange station; based on the operating parameter adjustment target, it adjusts the actual heat supply and / or circulating water flow rate of the heat exchange station. In this scheme, compared with the traditional heat exchange station grading and evaluation scheme of existing technology that uses human subjective scoring with equal weighting, this application relies on real-time raw operating data of the heat exchange station to carry out automatic rating throughout the entire process, completely eliminating the scoring bias caused by the human experience and subjective differences of the evaluators; based on the operating level obtained from the rating, the corresponding operating parameter adjustment target is determined, and the actual heat supply and / or circulating water flow of the heat exchange station is adjusted according to the operating parameter adjustment target, thereby comprehensively improving the refined operation and management level of the entire network of heat exchange station clusters and greatly improving the control accuracy of the heating system. Attached Figure Description

[0010] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0011] Figure 1 A flowchart illustrating an adaptive adjustment method for operating parameters of a heat exchange station provided in an embodiment of this application; Figure 2 A flowchart illustrating another method for adaptive adjustment of operating parameters of a heat exchange station provided in an embodiment of this application; Figure 3 A schematic diagram of the structure of an adaptive adjustment device for operating parameters of a heat exchange station provided in an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0013] Currently, in urban centralized heating systems, heat exchange stations, as intermediate heat exchange devices between heat sources and residential users, undertake the core functions of heat exchange, circulation and distribution, and regional heating load regulation. The operational quality of heat exchange stations directly determines the indoor heating temperature of users in the area, the overall heating energy consumption, and the long-term operational safety of the equipment. Most heating groups uniformly manage dozens to hundreds of decentralized heat exchange stations. Daily operation and maintenance inspections, equipment repairs, energy consumption control, and rectification of heating defects all require a unified, quantitative, and standardized hierarchical evaluation standard as a basis for management decision support.

[0014] In existing technologies, heat exchange station operation is typically assessed through manual on-site inspections by maintenance personnel, who subjectively judge the quality of the station's operation and then manually guide the optimization of operating parameters based on their assessments. This results in poor accuracy in the control of the heating system. Currently, the domestic heating industry generally lacks standardized, fully quantified, and automated methods for classifying and evaluating heat exchange stations. Most heating companies have not established standardized quantitative evaluation models, and maintenance personnel rely solely on on-site manual inspections to subjectively judge the quality of heat exchange station operation. This lack of integration with real-time operating data from the smart heating system backend, user room temperature data, and overall station energy consumption data for comprehensive automatic rating is a significant problem. Furthermore, the lack of a unified benchmark between heat exchange stations of different areas and sizes makes horizontal comparison difficult. The system cannot automatically screen out substandard heat exchange stations with consistently high energy consumption, frequent equipment failures, or inadequate heating performance. The results of manual ratings cannot provide feedback for optimizing heat exchange station operating parameters, scheduling maintenance inspections, and allocating resources. This leads to the following technical problems: (1) The average weighting of indicators leads to distorted and inaccurate rating results. Existing technology assigns equal weights to all indicators, treating core operational indicators that directly determine heating quality, such as user room temperature, water pump energy consumption, and equipment failure, as the same as secondary management indicators such as on-site hygiene and paper records. The core defects of some high-energy-consuming and large-area low-temperature heating heat exchange stations are masked, and the final rating score cannot truly reflect the actual operating status of the heat exchange station.

[0015] (2) It relies entirely on subjective scoring by humans and cannot be automated in batches. The rating work depends on the subjective judgment of the inspectors on site. The energy consumption, temperature and fault data automatically collected by the platform cannot be automatically used in the scoring calculation. Different evaluators have different evaluation standards, and the scores of the same heat exchange station vary greatly in multiple ratings. It is impossible to achieve a unified horizontal comparison of hundreds of heat exchange stations across the entire region.

[0016] (3) There is a lack of a penalty point mechanism for abnormal operating conditions, and the control of safety and heating defects is insufficient. The traditional scoring model cannot set penalty point rules for severe operating conditions such as equipment shutdown failure, pipeline overpressure, low temperature of users in the whole area, and batch user complaints. Heat exchange stations with major heating hazards are easily judged as qualified stations, leaving heating safety risks.

[0017] (4) Open-loop single rating, without implementation guidance and closed-loop optimization logic. Traditional solutions only complete a single scoring and grading process, without a data closed-loop optimization link; the rating results cannot provide reverse guidance for heat exchange station parameter adjustment, maintenance scheduling, and operation and maintenance resource allocation; they only complete the evaluation process and cannot promote the rectification of operational defects of substandard heat exchange stations.

[0018] The adaptive adjustment method for operating parameters of a heat exchange station provided in this application embodiment can be applied to electronic devices, terminal devices, smart heating platforms, adaptive adjustment devices or equipment for operating parameters of a heat exchange station, or other devices or equipment capable of executing this embodiment, without limitation. In this embodiment, the execution subject is described as an electronic device.

[0019] The terminal can be a user equipment (UE) such as a mobile phone, smartphone, laptop computer, digital broadcast receiver, personal digital assistant (PDA), or tablet computer (PAD), handheld device, in-vehicle device, wearable device, computing device, or other processing device connected to a wireless modem, mobile station (MS), or mobile terminal. This terminal has the ability to communicate with one or more core networks via a radio access network (RAN).

[0020] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0021] Figure 1 This is a flowchart illustrating an adaptive adjustment method for operating parameters of a heat exchange station, provided in an embodiment of this application. Figure 1 As shown, the method may include: Step S101: Based on a preset cycle, obtain the raw operating data of each heat exchange station.

[0022] For example, based on a preset period, raw operational data of each heat exchange station in the entire region is acquired. This raw operational data includes data on multiple preset indicators, such as heating quality indicators, equipment operation indicators, energy consumption and economic indicators, and safety and maintenance indicators. Heating quality indicators include the average room temperature per user in the area and the percentage of users whose temperatures do not meet the standards. Equipment operation indicators include the number of circulating pump / plate heat exchanger failures and the fluctuation range of the supply and return water pressure difference. Energy consumption and economic indicators include the heat consumption per unit area of ​​the heat exchange station and the water and electricity consumption per unit area of ​​the circulating water pump. Safety and maintenance indicators include on-site safety production compliance scores, user complaint rate, and the completion rate of inspection logs.

[0023] Step S102: Generate the overall standardized score sequence corresponding to each preset indicator based on the original operating data; the overall standardized score sequence includes the score of each heat exchange station.

[0024] For example, raw operational data are categorized and collected according to preset indicators to generate a single-indicator data array for each preset indicator; the single-indicator data array includes data from all heat exchange stations for that preset indicator. The single-indicator data array for each preset indicator is then standardized using a dimensionless scale from 0 to 100 to generate an overall standardized score sequence for each preset indicator; the overall standardized score sequence includes the scores of each heat exchange station.

[0025] Step S103: Based on the overall standardized score sequence corresponding to each preset indicator, obtain the target weight of each preset indicator; determine the operating level of each heat exchange station according to the overall standardized score sequence and target weight of each preset indicator.

[0026] For example, the target weight of each preset indicator is dynamically calculated based on the overall standardized score sequence corresponding to each preset indicator. The overall standardized score sequence of each preset indicator and the target weight are then weighted to obtain the target comprehensive score for each heat exchange station. Based on the target comprehensive score of each heat exchange station, the operating level of each heat exchange station is determined.

[0027] Step S104: For each heat exchange station, determine the operating parameter adjustment target corresponding to the operating level of the heat exchange station; adjust the actual heat supply and / or circulating water flow of the heat exchange station according to the operating parameter adjustment target.

[0028] For example, for each heat exchange station, an operating parameter adjustment target corresponding to the operating level of the heat exchange station is determined; the operating parameter adjustment target is sent to a preset adjustment mechanism, which includes a primary side electric regulating valve or a circulating pump frequency converter, so that the adjustment mechanism adjusts the actual heat supply and / or circulating water flow of the heat exchange station according to the operating parameter adjustment target.

[0029] The method provided in this application acquires raw operating data of each heat exchange station based on a preset period. Based on the raw operating data, an overall standardized score sequence corresponding to each preset indicator is generated; the overall standardized score sequence includes the score of each heat exchange station. Based on the overall standardized score sequence corresponding to each preset indicator, the target weight of each preset indicator is obtained; based on the overall standardized score sequence and target weight of each preset indicator, the operating level of each heat exchange station is determined. For each heat exchange station, an operating parameter adjustment target corresponding to the operating level of that heat exchange station is determined; based on the operating parameter adjustment target, the actual heat supply and / or circulating water flow rate of that heat exchange station is adjusted. In this scheme, compared with the traditional heat exchange station grading and evaluation scheme of existing technology that uses human subjective scoring with equal weighting, this application relies on real-time raw operating data of the heat exchange station to carry out automatic rating throughout the entire process, completely eliminating the scoring bias caused by the human experience and subjective differences of the evaluators; based on the operating level obtained from the rating, the corresponding operating parameter adjustment target is determined, and the actual heat supply and / or circulating water flow of the heat exchange station is adjusted according to the operating parameter adjustment target, thereby comprehensively improving the refined operation and management level of the entire network of heat exchange station clusters and greatly improving the control accuracy of the heating system.

[0030] Figure 2 A flowchart illustrating an adaptive adjustment method for operating parameters of a heat exchange station provided in this application is shown below. Figure 2 As shown, in this embodiment... Figure 1 Based on the embodiments, the method is described in detail below, and the method includes: Step S201: Based on a preset cycle, obtain the raw operating data of each heat exchange station.

[0031] For example, based on a preset period, raw operational data of each heat exchange station across the entire region is acquired. This raw operational data includes data on multiple preset indicators, such as heating quality indicators, equipment operation indicators, energy consumption and economic indicators, and safety and maintenance indicators. Heating quality indicators include the average room temperature per user in the area and the percentage of users whose temperatures do not meet standards. Equipment operation indicators include the number of circulating pump / plate heat exchanger failures and the fluctuation range of supply and return water pressure difference. Energy consumption and economic indicators include the heat consumption per unit area of ​​the heat exchange station and the water and electricity consumption per unit area of ​​the circulating water pump. Safety and maintenance indicators include on-site safety compliance scores, user complaint rate, and the completion rate of inspection logs. The raw operational data is shown in Table 1 below: Table 1 Complete List of Pre-set Indicators for Four-Dimensional Hierarchy

[0032] Optionally, the scope of all participating heat exchange stations can be defined. Specifically, participating heat exchange stations refer to all normally operating heat exchange stations within the current period of the heating company's complete preset number of days. Stations that have been shut down for a long time, undergoing equipment upgrades, or experiencing pipeline supply interruptions are automatically excluded and not included in the current dataset. For example, if the preset number of days is 15 days, this is not limited. At the beginning of each period, a valid set of stations is automatically selected to generate an independent indicator dataset for that period. The valid set of stations includes all normally operating heat exchange stations, and is used to uniformly calculate the maximum value of each preset indicator. and minimum value Extreme values ​​are used only within the current period. They are collected and recalculated in the next period. Historical extreme values ​​are not reused across periods to ensure fairness in horizontal comparisons between sites within the same period.

[0033] Optionally, the raw operational data can be cleaned based on the raw data cleaning rules. Specifically, outlier data (negative numbers, out-of-range extreme values, and continuous 0 values) caused by sensor disconnection, equipment maintenance, and signal failure can be automatically removed, and structured raw operational data can be output after cleaning.

[0034] Step S202: Based on the original operating data, generate the overall standardized score sequence corresponding to each preset indicator; the overall standardized score sequence includes the score of each heat exchange station.

[0035] For example, since the original data contains completely different physical dimensions such as temperature, percentage, energy consumption, and number of failures, it cannot be directly weighted for calculation. Therefore, this step completes a unified quantization conversion: 1. Global Indicators , Complete extraction operation details 1.1. Collect the raw operational data of all heat exchange stations in the entire region after cleaning according to the secondary indicators, and generate a single indicator data array X for each preset indicator of the effective heat exchange stations in the entire region. list =[x1,x2,…,x m ], m is the total number of effective heat exchange stations in this period; the single index data array includes the original measured data x of each heat exchange station for each preset index. i .

[0036] 1.2. Automatically remove outliers from the array caused by sensor malfunctions or maintenance; 1.3. Calculate the maximum value of the valid array after cleaning and removal, and denot it as... The minimum value is denoted as Store it in the platform's periodic parameter data table as a unified benchmark for standardized calculation of all sites in this period; 1.4. A set of extreme values ​​is automatically recalculated every 15 days, and remains fixed throughout the cycle. The extreme value benchmark is updated in the next cycle.

[0037] 2. Criteria for distinguishing between positive and negative indicators For positive indicators, the larger the indicator value, the better the operating quality of the heat exchange station; the optimization objective is to maximize the indicator value. For negative indicators, the smaller the indicator value, the better the operating quality of the heat exchange station; the optimization objective is to minimize the indicator value. The standardized conversion formula is as follows:

[0038] In the formula: The standardized score of the i-th preset indicator; The original measured data for the indicator; This sets the maximum and minimum values ​​for this preset indicator for all participating heat exchange stations in the region. Finally, it outputs the overall standardized score sequence for all preset indicators across the region, including the standardized scores for each heat exchange station. .

[0039] Therefore, based on the rules for classifying positive and negative heating indicators, the logic for cleaning and extracting the maximum / minimum values ​​of the whole-domain periodic indicators, and the dimensionless standardized calculation formula for 0~100 points, a unified quantitative scoring of multiple types of heterogeneous indicators such as heating quality, equipment operation, energy consumption economy, and safe operation and maintenance is achieved.

[0040] Step S203: Based on the overall standardized score sequence corresponding to each preset indicator, obtain the target weight of each preset indicator; In one example, step S203 includes: determining the standard deviation of each preset indicator based on the overall standardized score sequence corresponding to each preset indicator; and obtaining the target weight of each preset indicator based on the standard deviation of each preset indicator.

[0041] For example, based on the overall standardized score sequence corresponding to each preset indicator, the standard deviation of each preset indicator is calculated; based on the standard deviation of each preset indicator, the target weight of each preset indicator is obtained. Specifically: 1) Standard deviation σ i Extraction instructions σ i σ represents the standard deviation of the overall standardized score series of the same preset indicator for all stations across the entire region during this period. The greater the discrete difference in the overall standardized score series of the same preset indicator, the lower the σ value. i The larger the value, the higher the corresponding weight w. i The higher the value, the more the weight of core indicators with significant differences in heating quality, energy consumption, and equipment failure will be automatically amplified.

[0042] 2) The weight calculation formula is as follows:

[0043] in, The target weight of the i-th preset indicator satisfies the constraints. ; is the standard deviation of the overall standardized score sequence of the same preset indicator, where n is the total number of preset indicators.

[0044] Therefore, this step automatically assigns weights based on the dispersion of the overall standardized score sequence, abandoning manual equal weighting and improving the accuracy of the weights. The weight calculation method based on data standard deviation abandons the manual averaging scoring model and automatically amplifies the weights of core indicators such as heating and safety.

[0045] Step S204: Determine the operating level of each heat exchange station based on the overall standardized score sequence and target weight of each preset indicator.

[0046] In one example, step S204 includes: determining the basic comprehensive score of each heat exchange station based on the overall standardized score sequence and target weight of each preset indicator; obtaining abnormal operating condition data of each heat exchange station; determining the target penalty correction coefficient of each heat exchange station based on the abnormal operating condition data; determining the target comprehensive score of each heat exchange station based on the basic comprehensive score and the target penalty correction coefficient; and determining the operating level of each heat exchange station based on the target comprehensive score.

[0047] In one example, abnormal operating condition data includes multiple types of the following: equipment fault alarm data, user complaint data, heating temperature non-compliance data, and on-site safety hazard data. Based on the abnormal operating condition data, the target penalty correction coefficient for each heat exchange station is determined, including: comparing the equipment fault alarm data, user complaint data, heating temperature non-compliance data, and on-site safety hazard data with preset abnormal judgment conditions to obtain multiple comparison result data; determining combined data based on multiple comparison result data; identifying the abnormal level of each heat exchange station in the current cycle based on the combined data; and determining the target penalty correction coefficient for each heat exchange station corresponding to the abnormal level based on the abnormal level.

[0048] In one example, the method further includes: acquiring historical scoring data for each heat exchange station over multiple periods and current scoring data for the current period; wherein, the historical scoring data includes the historical comprehensive scoring sequence for each heat exchange station and the historical standardized score sequence for each preset indicator in each heat exchange station; the current scoring data includes the daily standardized score for each preset indicator in each heat exchange station; for each heat exchange station, determining the baseline predicted score for the next period based on the historical comprehensive scoring sequence; and determining the predicted level label for each heat exchange station in the next period based on the baseline predicted score, the historical standardized score sequence, and the daily standardized score.

[0049] In one example, the predicted grade label for each heat exchange station is determined based on the baseline predicted score, historical standardized score sequence, and daily standardized score. This includes: determining the historical score change rate sequence and historical comprehensive score change rate sequence for each preset indicator based on the historical standardized score sequence and historical comprehensive score sequence; for each preset indicator in each heat exchange station, determining the trend influence coefficient of that preset indicator based on the historical standardized score sequence and historical comprehensive score sequence; determining the rate of change of each preset indicator in the current cycle based on the daily standardized score of each preset indicator; determining the corrected baseline predicted score based on the baseline predicted score, the trend influence coefficient of each preset indicator, and the rate of change; and determining the predicted grade label for each heat exchange station in the next cycle based on the corrected baseline predicted score.

[0050] For example, based on the overall standardized score sequence of each preset indicator (the overall standardized score sequence includes the standardized scores of each heat exchange station) . ) and target weight w i Calculate the basic comprehensive score for each heat exchange station. The calculation formula is as follows:

[0051] Acquire abnormal operating condition data from each heat exchange station. Real-time abnormal operating condition data includes several of the following types: equipment fault alarm data, user complaint data, heating temperature non-compliance data, and on-site safety hazard data. Equipment fault data includes the total number of faults and the maximum continuous downtime for the current period, automatically recorded and reported by fault alarm modules deployed on circulating pumps and plate heat exchangers. User complaint data includes the number of valid complaints for the current period, automatically counted by the smart heating platform's complaint system. Low temperature non-compliance data includes the percentage of users with temperatures below 18℃ for the current period, calculated by the processor after being aggregated from resident room temperature data collection terminals. On-site safety hazard data includes the level of on-site safety hazards (no hazard / general hazard / major hazard) and any missing records for the current period, manually entered and confirmed.

[0052] Data on equipment fault alarms, user complaints, substandard heating temperatures, and on-site safety hazards are compared with preset anomaly judgment conditions to obtain multiple comparison results. Based on these results, combined data is determined. Then, based on this combined data, the anomaly level of each heat exchange station in the current cycle is identified. The anomaly level includes at least three levels: no anomaly, general anomaly, and severe anomaly. Based on the anomaly level, a target penalty correction coefficient corresponding to that level is determined for each heat exchange station. The logic for multi-condition combination judgment is as follows: when a heat exchange station simultaneously meets multiple anomaly judgment conditions, the anomaly level jumps to a more severe level based on the level corresponding to each individual anomaly judgment. The more anomaly judgment conditions met and the higher the severity of each anomaly, the higher the overall anomaly level obtained from the combined judgment.

[0053] Finally, based on the basic comprehensive score and the target penalty correction coefficient, the target comprehensive score for each heat exchange station is determined; based on the target comprehensive score, the operational level of each heat exchange station is determined. Specifically, the basic comprehensive score for each heat exchange station is obtained based on a weighted summation. Then, by matching real-time abnormal operating condition data, the corresponding target penalty correction coefficient ζ is automatically retrieved to correct the basic comprehensive score. The total score (0~100 points) for the heat exchange station is obtained: 1) The target penalty correction coefficient ζ is graded and its triggering conditions are automatically determined by the platform. Normal operating conditions without abnormalities (no equipment fault alarms, ≤5 monthly complaints, <3% of users operating at low temperatures, and no on-site safety hazards). There are general malfunctions / a small number of complaints (single circulating pump malfunction, 5-30 complaints per month, 3%-8% of users are in low-temperature conditions, and minor missing on-site records). There are issues such as shutdowns, safety hazards, and large-scale non-compliance by users (plate heat exchanger shutdowns, pipeline overpressure and pressure relief, more than 30 monthly complaints, low-temperature users accounting for more than 8%, and major on-site safety hazards). The more severe the working condition problem, the smaller the penalty coefficient and the lower the total score.

[0054] 2) Comprehensive score calculation formula

[0055] in: The final comprehensive score for the heat exchange station (0~100 points); ∑(wi Xi) is the unpenalized weighted base total score, i.e. .

[0056] Optionally, the system can automatically classify based on a fixed score threshold, generate an operational level, and push it synchronously to the operation and maintenance system; site levels, rectification work orders, and parameter adjustment records can all be fed back to the platform database as a data source for subsequent closed-loop correction of the scoring model.

[0057] The four-level standardized grading criteria and supporting control strategies are as follows: Level I (Excellent Heat Exchange Station): The equipment operates stably, energy consumption meets standards, and there are no user complaints; existing operating parameters are maintained, and the frequency of inspections is reduced; Level II (Qualified Heat Exchange Station): Minor operational defects exist, but do not affect the overall heating effect; routine, fixed-cycle inspections and maintenance are performed; Level III (Early Warning Heat Exchange Station): Energy consumption significantly exceeds standards, and equipment experiences frequent minor malfunctions; a rectification warning work order is issued, and the frequency of on-site inspections is increased; Level IV (unqualified heat exchange station): Significantly abnormal operating conditions, potential heating safety risks, and poor heating quality in the area will be identified and subject to special supervision, with a deadline set for comprehensive rectification. The final output will determine the operating level and differentiated operation and maintenance dispatch instructions for each heat exchange station.

[0058] Optionally, perform periodic closed-loop correction of the scoring model. Specifically, calculate the fluctuation data (i.e., standard deviation) of the comprehensive score of all heat exchange stations in the entire region for this period. Collect the new round of original operating data after rectification; This refers to the original operating data collected after maintenance personnel at heat exchange stations that were rated as unqualified (Level IV) or under warning (Level III) in the previous cycle, following the completion of equipment maintenance, parameter adjustments, and pipeline maintenance actions as instructed by the rectification order. This is based on the fluctuation data (i.e., standard deviation) of the comprehensive score of all heat exchange stations in the entire region for this cycle. The new round of original operating data after rectification is fixed at a standard cycle of 15 days, and the weights of various preset indicators are periodically and adaptively fine-tuned. If the data fluctuation of a certain preset indicator is large in this cycle and has a significant impact on the comprehensive score of the heat exchange station, a larger weight adjustment step size is selected for that indicator to accelerate weight optimization; if the preset indicator operates stably for a long period and the overall network score fluctuates little, a smaller adjustment step size is selected to maintain the stability of the original weights. The iterative calculation formula has a built-in normalization constraint, and the sum of all weights after iteration always equals 1. The closed-loop weight iterative calculation formula (i.e., the scoring model) is as follows:

[0059] in: For the next cycle Optimized preset indicators The weights; For the current period Preset indicators used The weights; The adaptive weight adjustment step size for a single preset indicator is set, with an overall value range of 0.02 to 0.06. The greater the fluctuation of the indicator data, the more significant the disturbance to the comprehensive score. The larger the value, the stronger the weight adjustment; the more stable the indicator, the greater the adjustment. Take the smaller value; The absolute value of the baseline fluctuation coefficient for the overall periodic scoring is the standard deviation of the comprehensive score of all heat exchange stations in the region for this period, reflecting the overall operational fluctuation level of the entire region. It is updated every 15 days. This then outputs the weights of the preset indicators after the next period's iterative optimization. .

[0060] Optionally, the refinement rules for the step size α are as follows: Core indicators such as heating quality, energy consumption, and equipment failure: periodic data fluctuates greatly and has a strong impact on the classification results, so α is taken as 0.05~0.06; auxiliary management indicators such as ledgers and on-site sanitation: the data of the entire network is stable over a long period of time and the differences between stations are small, so α is taken as 0.02~0.03.

[0061] Therefore, this step iteratively generates new weights, which automatically overwrite the initial weight values ​​of the next cycle step S203, completing the complete data loop of "collection - rating - control - correction".

[0062] Optionally, historical scoring data for each heat exchange station over multiple periods and current scoring data for the current period can be obtained. The historical scoring data includes the historical comprehensive scoring sequence for each heat exchange station and the historical standardized score sequence for each preset indicator in each heat exchange station. The current scoring data includes the daily standardized score for each preset indicator in each heat exchange station. For each heat exchange station, based on the retrieved historical comprehensive scoring sequence, an exponentially weighted moving average method is used to calculate the baseline predicted score for the next period.

[0063] Based on the retrieved historical standardized scores and historical comprehensive score sequences, for each preset indicator, the cross-period correlation between the historical score change rate of that preset indicator and the comprehensive score change rate of the next period is calculated, specifically as follows: Based on the historical standardized score sequence and the historical comprehensive score sequence, the historical score change rate sequence and the historical comprehensive score change rate sequence for each preset indicator are determined. For example, the historical score change rate sequence is obtained by directly subtracting the scores of two adjacent periods in the historical standardized score sequence; the historical comprehensive score change rate sequence is obtained by directly subtracting the scores of two adjacent periods in the historical comprehensive score sequence. For each preset indicator in each heat exchange station, the trend influence coefficient of that preset indicator is calculated based on the historical standardized score sequence and the historical comprehensive score sequence.

[0064] For each heat exchange station and each preset indicator, the processor calculates the average of the daily standardized scores for the next period within the current cycle, based on the retrieved daily standardized scores for the current cycle, and also calculates the average of the daily standardized scores for the previous period within the current cycle. Then, the difference between the average scores of the two periods is used as the real-time rate of change of the preset indicator in the current cycle.

[0065] Then, a unified conversion of the direction of change is performed. Specifically, for each heat exchange station and each preset indicator, the processor converts the calculated real-time rate of change into the indicator change amount with a "positive improvement direction," that is, obtains the absolute value of the rate of change. For positive indicators, the larger the indicator value, the better, such as room temperature; for negative indicators, the smaller the indicator value, the better, such as heat consumption and number of failures. After this conversion, an absolute value of the rate of change > 0 indicates that the indicator is showing an improving trend in this cycle, and an absolute value of the rate of change < 0 indicates a deteriorating trend.

[0066] For each preset indicator at each heat exchange station, the absolute value of the trend influence coefficient, the absolute value of the converted rate of change, and the weight wi of the current period are multiplied to obtain the correction value of the preset indicator. The correction values ​​of multiple preset indicators are summed to obtain the total correction value. The total correction value is added to the baseline prediction score to obtain the corrected baseline prediction score. Based on the corrected baseline prediction scores of each heat exchange station, the processor predicts the prediction level label for each heat exchange station in the next period according to a preset fixed score threshold. It can also retrieve the actual level label for each heat exchange station in the current period, compare the actual level label with the predicted level label for the next period, and generate a risk level identifier. The generated risk level identifier and the corresponding predicted level label are pushed to the operation and maintenance scheduling terminal.

[0067] For example, from historical data, for each preset indicator, N data pairs are constructed: "historical indicator change - next period total score change". For instance, the change in the heat consumption indicator in period 2 (relative to period 1) and the change in the total score in period 3 (relative to period 2) are used as the first data pair; the change in the heat consumption indicator in period 3 and the change in the total score in period 4 are used as the second data pair; and so on. Then, using the Pearson correlation coefficient formula, these N data pairs are calculated to obtain the cross-period trend influence coefficient of the heat consumption indicator. The trend influence coefficient ranges from -1 to 1. The closer the absolute value of the trend influence coefficient is to 1, the stronger its predictive ability for the change in the indicator in this period on the change in the comprehensive score in the next period; a positive value indicates that when the indicator improves, the comprehensive score in the next period tends to rise, and a negative value indicates that when the indicator improves, the comprehensive score in the next period tends to fall.

[0068] The daily standardized score extreme value benchmark is fixed as the extreme value of the entire effective heat exchange station in the previous complete cycle. , The standardized score remains fixed throughout the current period; the historical period standardized score and the daily standardized score use the same extreme value standardization calculation formula, but the extreme value benchmarks are different. The former is based on the global extreme value of each period, while the latter is fixed on the global extreme value of the previous period.

[0069] Therefore, a tiered penalty coefficient is set up for equipment failures, large-scale heating deficiencies, safety hazards, and batch complaints to achieve targeted deductions for severe operating conditions. A tiered anomaly penalty correction coefficient is added to automatically deduct points from the overall score based on the severity of the fault, accurately identifying high-risk and substandard heat exchange stations.

[0070] Step S205: For each heat exchange station, determine the operating parameter adjustment target corresponding to the operating level of that heat exchange station; For example, for each heat exchange station, based on the operating level, a preset differentiated operating parameter adjustment target library is matched to determine the operating parameter adjustment target corresponding to that operating level. The operating parameter adjustment target includes at least one of the following: a setpoint for the water supply temperature, a target value for the opening of the primary side electric regulating valve, and a target value for the operating frequency of the circulating pump.

[0071] Step S206: Adjust the actual heat supply and / or circulating water flow of the heat exchange station according to the operating parameter adjustment target.

[0072] In one example, step S206 includes: generating a control signal for driving the primary-side electric regulating valve or the frequency converter of the circulating pump of the heat exchange station according to the operating parameter adjustment target; sending the control signal to the primary-side electric regulating valve or the frequency converter of the circulating pump so that the primary-side electric regulating valve or the frequency converter of the circulating pump receives the control signal and adjusts the actual heat supply and / or circulating water flow of the heat exchange station according to the control signal.

[0073] For example, a control signal is generated to drive the primary-side electric regulating valve or the frequency converter of the circulating pump in the heat exchange station based on the setpoint of the water supply temperature, the target opening value of the primary-side electric regulating valve, and the target operating frequency value of the circulating pump. The control signal is then sent to the primary-side electric regulating valve or the frequency converter of the circulating pump, causing the primary-side electric regulating valve or the frequency converter of the circulating pump to receive the control signal and adjust the actual heat supply and / or circulating water flow rate of the heat exchange station accordingly. Through the above adjustment, the actual heat supply and / or circulating water flow rate of the heat exchange station are adjusted accordingly.

[0074] The method provided in this application acquires raw operating data of each heat exchange station based on a preset period. Based on the raw operating data, an overall standardized score sequence corresponding to each preset indicator is generated; the overall standardized score sequence includes the score of each heat exchange station. Based on the overall standardized score sequence corresponding to each preset indicator, the target weight of each preset indicator is obtained; based on the overall standardized score sequence and target weight of each preset indicator, the operating level of each heat exchange station is determined. For each heat exchange station, an operating parameter adjustment target corresponding to the operating level of that heat exchange station is determined; based on the operating parameter adjustment target, the actual heat supply and / or circulating water flow rate of that heat exchange station is adjusted. Compared to the traditional heat exchange station grading and evaluation schemes that rely on subjective, equal-weighted scoring by existing technologies, this application utilizes real-time raw operational data from the heat exchange station for automatic rating throughout the entire process. This completely eliminates scoring biases caused by the subjective experience and differences in the evaluation criteria of the evaluators. By automatically distinguishing between core operational indicators and auxiliary management indicators through an objective weighting mechanism based on data standard deviation, the rating score can truly and objectively reflect the energy consumption level, equipment operating conditions, and regional heating service quality of the heat exchange station. The accompanying tiered abnormal operating condition penalty mechanism can accurately identify substandard heat exchange stations with safety hazards and large-scale heating defects, thus compensating for the loopholes in traditional manual rating and control.

[0075] This application comprehensively standardizes a complete set of practical rules for extreme value cleaning and extraction of full-domain indicators, quantitative conversion of positive and negative indicators, graded determination of penalty coefficients, and adaptive iteration of weights. It includes complete numerical calculation examples, with all calculation steps executed automatically, eliminating the need for manual data screening and conversion. The entire method constructs a complete closed-loop data flow chain, with the output data from the preceding steps fully supporting all subsequent calculations. Post-rectification operational data drives the evaluation model's adaptive iterative optimization, and weights are dynamically adjusted according to the heating conditions across the entire network. The algorithm formulas are simple and computationally inefficient, allowing for implementation based on the enterprise's existing smart heating platform database without the need for additional hardware. It is compatible with batch synchronous horizontal hierarchical management of hundreds of plate heat exchange stations across the entire network, significantly reducing the workload of manual evaluation for heating companies and comprehensively improving the refined operation and management level of the entire network's heat exchange station cluster. Ultimately, it achieves a data-driven, fully automated closed-loop iterative optimization process: a complete closed-loop evaluation method chain encompassing multi-source data collection, indicator standardization processing, objective weight calculation, comprehensive scoring and penalty correction, four-level hierarchical management, and periodic correction of model parameters. It also enables differentiated operation and maintenance strategies based on the station level: high-level heat exchange stations maintain existing operating parameters and reduce inspection frequency; low-level heat exchange stations are issued targeted rectification work orders and allocated operation and maintenance resources, ultimately reducing the workload of heating operation and maintenance across the entire region, reducing equipment failures at heat exchange stations, and reducing the overall heating energy consumption of the network.

[0076] Corresponding to the above method, this application embodiment also provides an adaptive adjustment device for the operating parameters of a heat exchange station, such as... Figure 3 As shown, the device includes: The acquisition module 41 is used to acquire the raw operating data of each heat exchange station based on a preset cycle; The generation module 42 is used to generate an overall standardized score sequence corresponding to each preset indicator based on the original operating data; the overall standardized score sequence includes the scores of each heat exchange station. The determination module 43 is used to obtain the target weight of each preset indicator based on the overall standardized score sequence corresponding to each preset indicator; and to determine the operating level of each heat exchange station according to the overall standardized score sequence and target weight of each preset indicator. The adjustment module 44 is used to determine the operating parameter adjustment target corresponding to the operating level of each heat exchange station; and to adjust the actual heat supply and / or circulating water flow of the heat exchange station according to the operating parameter adjustment target.

[0077] The functions of each functional unit of the adaptive adjustment device for operating parameters of the heat exchange station provided in the above embodiments of this application can be implemented through the above methods and steps. Therefore, the specific working process and beneficial effects of each unit in the adaptive adjustment device for operating parameters of the heat exchange station provided in the embodiments of this application will not be repeated here.

[0078] This application also provides an electronic device, such as... Figure 4 As shown, it includes a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540.

[0079] Memory 530 is used to store computer programs; The processor 510 performs the above steps when executing the program stored in the memory 530.

[0080] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0081] The communication interface is used for communication between the aforementioned electronic devices and other devices.

[0082] The memory may include random access memory (RAM) or non-volatile memory (NVM), such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0083] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0084] The implementation methods and beneficial effects of the various components of the electronic device in the above embodiments for solving the problem can be found in [reference needed]. Figure 1 The steps in the illustrated embodiments are used to implement the electronic device. Therefore, the specific working process and beneficial effects of the electronic device provided in this application will not be repeated here.

[0085] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the adaptive adjustment method for the operating parameters of the heat exchange station described in any of the above embodiments.

[0086] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the adaptive adjustment method for the operating parameters of the heat exchange station described in any of the above embodiments.

[0087] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0089] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0090] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0091] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0092] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for adaptive adjustment of operating parameters of a heat exchange station, characterized in that, include: Based on a preset cycle, acquire the raw operating data of each heat exchange station; Based on the original operating data, an overall standardized score sequence corresponding to each preset indicator is generated; the overall standardized score sequence includes the scores of each heat exchange station; Based on the overall standardized score sequence corresponding to each preset indicator, the target weight of each preset indicator is obtained; according to the overall standardized score sequence and the target weight of each preset indicator, the operating level of each heat exchange station is determined. For each heat exchange station, determine the operating parameter adjustment target corresponding to the operating level of the heat exchange station; adjust the actual heat supply and / or circulating water flow rate of the heat exchange station according to the operating parameter adjustment target.

2. The method as described in claim 1, characterized in that, Based on the overall standardized score sequence of each preset indicator and the target weight, the operating level of each heat exchange station is determined, including: Based on the overall standardized score sequence of each preset indicator and the target weight, the basic comprehensive score of each heat exchange station is determined; Obtain abnormal operating condition data for each heat exchange station; determine the target penalty correction coefficient for each heat exchange station based on the abnormal operating condition data; The target comprehensive score for each heat exchange station is determined based on the basic comprehensive score and the target penalty correction coefficient. Based on the comprehensive score of the aforementioned targets, the operational level of each heat exchange station is determined.

3. The method as described in claim 2, characterized in that, The abnormal operating condition data includes multiple types of the following: equipment fault alarm data, user complaint data, heating temperature not meeting standards data, and on-site safety hazard data. Based on the abnormal operating condition data, the target penalty correction coefficient for each heat exchange station is determined, including: The equipment fault alarm data, user complaint data, heating temperature non-compliance data, and on-site safety hazard data are compared with preset anomaly judgment conditions to obtain multiple comparison result data. Based on multiple comparison results, determine the combined data; Based on the combined data, identify the anomaly level of each heat exchange station in the current cycle; Based on the anomaly level, determine the target penalty correction coefficient for each heat exchange station corresponding to that anomaly level.

4. The method as described in claim 1, characterized in that, Based on the overall standardized score sequence corresponding to each of the preset indicators, the target weights of each of the preset indicators are obtained, including: Based on the overall standardized score sequence corresponding to each preset indicator, the standard deviation of each preset indicator is determined. The target weight of each preset indicator is obtained based on the standard deviation of each preset indicator.

5. The method as described in claim 1, characterized in that, Adjusting the actual heat supply and / or circulating water flow rate of the heat exchange station according to the aforementioned operating parameter adjustment targets includes: Based on the operating parameter adjustment target, a control signal is generated to drive the primary side electric regulating valve or the frequency converter of the circulating pump in the heat exchange station; The control signal is sent to the primary side electric regulating valve or the circulating pump frequency converter, so that the primary side electric regulating valve or the circulating pump frequency converter receives the control signal and adjusts the actual heat supply and / or circulating water flow of the heat exchange station according to the control signal.

6. The method as described in claim 1, characterized in that, The method further includes: The system acquires historical scoring data for each heat exchange station over multiple periods and current scoring data for the current period. The historical scoring data includes the historical comprehensive scoring sequence for each heat exchange station and the historical standardized score sequence for each preset indicator in each heat exchange station. The current scoring data includes the daily standardized score for each preset indicator in each heat exchange station. For each heat exchange station, a baseline predicted score for the next period is determined based on the historical comprehensive score sequence. Based on the baseline prediction score, the historical standardized score sequence, and the daily standardized score, the prediction level label for each heat exchange station in the next cycle is determined.

7. The method as described in claim 6, characterized in that, Based on the baseline prediction score, the historical standardized score sequence, and the daily standardized score, a prediction level label is determined for each heat exchange station, including: Based on the historical standardized score sequence and the historical comprehensive score sequence, determine the historical score change rate sequence and the historical comprehensive score change rate sequence for each preset indicator; For each preset indicator in each heat exchange station, the trend influence coefficient of the preset indicator is determined based on the historical standardized score sequence and the historical comprehensive score sequence. Based on the daily standardized score of each preset indicator, the rate of change of each preset indicator in the current cycle is determined. The corrected benchmark prediction score is determined based on the benchmark prediction score, the trend influence coefficient of each preset indicator, and the rate of change. Based on the revised baseline prediction score, the prediction level label for each heat exchange station in the next cycle is determined.

8. An adaptive adjustment device for operating parameters of a heat exchange station, characterized in that, include: The acquisition module is used to acquire the raw operating data of each heat exchange station based on a preset cycle. The generation module is used to generate an overall standardized score sequence corresponding to each preset indicator based on the original operating data; the overall standardized score sequence includes the scores of each heat exchange station; The determination module is used to obtain the target weight of each preset indicator based on the overall standardized score sequence corresponding to each preset indicator; and to determine the operating level of each heat exchange station according to the overall standardized score sequence and the target weight of each preset indicator. The adjustment module is used to determine the operating parameter adjustment target corresponding to the operating level of each heat exchange station; and to adjust the actual heat supply and / or circulating water flow of the heat exchange station according to the operating parameter adjustment target.

9. An electronic device, characterized in that, The electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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