Power distribution switch control equipment energy-saving optimization method and system based on data fusion

Abstract

The invention relates to the technical field of equipment energy-saving control, in particular to a power distribution switch control equipment energy-saving optimization method and system based on data fusion, and the method comprises the steps: importing control record data and equipment parameter data of control equipment into an equipment control difficulty degree analysis model, and analyzing the control difficulty degree of the control equipment; importing the switch operation data and the current operation state data of the power distribution switch into an operation stability analysis model, and analyzing the operation stability of the power distribution switch; constructing a control equipment energy-saving benefit evaluation model, importing the control difficulty degree analysis result of the control equipment and the operation stability analysis result of the power distribution switch into the control equipment energy-saving benefit evaluation model, and evaluating the energy-saving benefit of the control equipment; and adjusting the energy-saving mode according to an energy-saving benefit evaluation result of the control equipment. Energy-saving mode parameters can be accurately and dynamically optimized, energy saving and equipment operation are balanced, and the adaptability and the energy-saving effect of the energy-saving mode are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment energy-saving control technology, and in particular to a method and system for optimizing energy-saving of distribution switch control equipment based on data fusion. Background Art

[0002] During the operation of power distribution systems, the proper adaptation of energy-saving modes is crucial for equipment energy efficiency and safety. As a key node in the power distribution process and energy-saving regulation, the operating status of distribution switch control equipment is subject to multiple influences. Under different energy-saving modes, the triggering conditions and execution timing of control operations, such as start / stop and power regulation, vary significantly, affecting the operating status of the distribution switch control equipment. Regarding control operation frequency, the frequency and duration of distribution switch opening and closing operations fluctuate significantly between peak and off-peak load periods. High-frequency control operations accelerate mechanical wear and electrical aging of equipment, while low-frequency operation can accumulate hidden faults due to long periods of equipment downtime, further impacting the operating status of the distribution switch control equipment. Furthermore, various loss modes, such as Joule heat loss, hysteresis loss, and eddy current loss, dynamically change with operating conditions and constantly impact the operating status of the distribution switch control equipment. These intertwined factors contribute to serious energy efficiency issues in the operation of distribution switch control equipment under these multiple influences. Therefore, improving the energy efficiency of distribution switch control equipment and minimizing equipment energy consumption and operation and maintenance costs while meeting power supply needs through refined energy-saving mode regulation have become the research focus of existing technologies.

[0003] However, existing technologies for optimizing energy conservation in distribution switch control equipment focus solely on the number of operations or simple timing sequences, ignoring the chaotic distribution of control operation types, fluctuations in control frequency, and the complex impact of manual and automatic control on the control difficulty and energy conservation of control equipment. Furthermore, these technologies fail to effectively integrate the synergistic effects of control behavior interference and inherent equipment losses. Consequently, energy conservation modes are poorly adapted to actual operational needs, failing to achieve optimal energy conservation while ensuring equipment stability, thus threatening the goals of energy conservation upgrades and safe operation of distribution systems.

[0004] In order to solve these problems, the present application designs a method and system for energy-saving optimization of distribution switch control equipment based on data fusion. Summary of the Invention

[0005] To overcome the shortcomings and deficiencies of existing technologies, the present invention provides a data-fusion-based energy-saving optimization method and system for power distribution switch control equipment. This method, through a comprehensive analysis of the control difficulty of the control equipment and the operational stability of the power distribution switch, accurately assesses the energy-saving benefits of the control equipment. Based on the evaluation results of the energy-saving benefits of the control equipment, the energy-saving mode is adjusted and optimized. This method accurately and dynamically optimizes energy-saving mode parameters, balancing energy conservation with equipment operation, and improving the adaptability and energy-saving effectiveness of the energy-saving mode.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, an embodiment of the present invention provides a method for optimizing energy conservation of a distribution switch control device based on data fusion, comprising the following steps: S1. Obtain the switch operation data and current operation status data of the distribution switch, and simultaneously obtain the control record data and device parameter data of the control device; S2. Importing the control record data and device parameter data of the control device into the device control difficulty analysis model to analyze the control difficulty of the control device; S3. Importing the switch operation data and current operation status data of the distribution switch into the operation stability analysis model to analyze the operation stability of the distribution switch; S4. Construct a control equipment energy-saving benefit evaluation model, import the control difficulty analysis results of the control equipment and the operation stability analysis results of the distribution switch into the control equipment energy-saving benefit evaluation model, and evaluate the energy-saving benefits of the control equipment; S5. Adjust the energy-saving mode according to the energy-saving benefit evaluation results of the control equipment.

[0007] In a preferred technical solution of the present invention, the analysis of the control difficulty of the control device in step S2 includes the following specific steps: S21, extracting control record data and device parameter data of the control device in the current energy-saving mode; S22. Based on the control record data and device parameter data of the control device in the current energy-saving mode, construct a device control difficulty analysis model, analyze the control difficulty of the control device in the current energy-saving mode, and obtain a control difficulty analysis result of the control device in the current energy-saving mode; The calculation formula for control difficulty is: ; Where Kn is the control difficulty of the control device in the current energy-saving mode, fz is the analysis result of the control operation complexity of the control device in the current energy-saving mode, fb is the analysis result of the control frequency fluctuation level of the control device in the current energy-saving mode, ps is the number of manual controls of the control device in the current energy-saving mode, and pt is the total number of controls of the control device in the current energy-saving mode.

[0008] In the preferred technical solution of the present invention, the process of constructing the equipment control difficulty analysis model in step S22 includes the following specific steps: S221. Analyze the control operation complexity of the control device in the current energy-saving mode based on the control record data and device parameter data of the control device in the current energy-saving mode to obtain a control operation complexity analysis result of the control device in the current energy-saving mode; S222: Analyze the control frequency fluctuation level of the control device in the current energy-saving mode based on the control record data of the control device in the current energy-saving mode to obtain an analysis result of the control frequency fluctuation level of the control device in the current energy-saving mode.

[0009] In a preferred technical solution of the present invention, the analysis of the operating stability of the distribution switch in step S3 includes the following specific steps: S31, extracting switch operation data and current operation status data of the power distribution switch in the current energy-saving mode; S32. Based on the switch operation data and current operation status data of the power distribution switch in the current energy-saving mode, construct an operation stability analysis model, analyze the operation stability of the power distribution switch in the current energy-saving mode, and obtain an operation stability analysis result of the power distribution switch in the current energy-saving mode; The calculation formula for the operating stability of the distribution switch is: ; Where Yw is the operating stability of the distribution switch in the current energy-saving mode, gr is the analysis result of the control behavior interference degree generated by the control equipment during the operation of the distribution switch in the current energy-saving mode, and sh is the analysis result of the operating loss degree of the distribution switch in the current energy-saving mode.

[0010] In the preferred technical solution of the present invention, the construction process of the running stability analysis model in step S32 includes the following specific steps: S321. Analyze, based on the switch operation data and current operation status data of the power distribution switch in the current energy-saving mode, the control behavior interference degree generated by the control device during the operation of the power distribution switch in the current energy-saving mode, to obtain an analysis result of the control behavior interference degree generated by the control device during the operation of the power distribution switch in the current energy-saving mode; S322. Based on the switch operation data and current operation status data of the distribution switch in the current energy-saving mode, analyze the operation loss degree of the distribution switch in the current energy-saving mode to obtain the analysis result of the operation loss degree of the distribution switch in the current energy-saving mode.

[0011] In the preferred technical solution of the present invention, the energy-saving benefit evaluation model of the control equipment is constructed in step S4, including the following specific steps: S41. Extracting and analyzing the control difficulty analysis results of the control device in the current energy-saving mode and the operation stability analysis results of the distribution switch; S42. Evaluate the energy-saving benefits of the control device in the current energy-saving mode based on the control difficulty analysis results of the control device in the current energy-saving mode and the operation stability analysis results of the distribution switch; Among them, the evaluation formula for the energy-saving benefit of the control equipment is: ; Where NX is the energy-saving benefit of the control device in the current energy-saving mode, Kn is the control difficulty analysis result of the control device in the current energy-saving mode, and Yw is the operation stability analysis result of the distribution switch in the current energy-saving mode.

[0012] In a preferred technical solution of the present invention, in step S5, adjusting the energy-saving mode according to the energy-saving benefit evaluation result of the control device includes the following specific steps: S51. Obtain energy-saving benefit evaluation results of control devices in all energy-saving modes; S52. Obtain the energy-saving mode corresponding to the maximum energy-saving benefit evaluation result of the control device in all energy-saving modes as the optimal energy-saving mode, and adjust the energy-saving mode of the distribution switch control device to the optimal energy-saving mode.

[0013] In a second aspect, an embodiment of the present invention further provides an energy-saving optimization system for distribution switch control equipment based on data fusion, comprising: The data acquisition module is used to obtain the switch operation data and current operation status data of the distribution switch, and the control record data and device parameter data of the control device; A control difficulty analysis module is used to import the control record data and device parameter data of the control device into the device control difficulty analysis model to analyze the control difficulty of the control device; An operation stability analysis module is used to import the switch operation data and current operation status data of the distribution switch into the operation stability analysis model to analyze the operation stability of the distribution switch; The energy-saving benefit evaluation module is used to build an energy-saving benefit evaluation model for control equipment. The control difficulty analysis results of the control equipment and the operation stability analysis results of the distribution switch are imported into the energy-saving benefit evaluation model to evaluate the energy-saving benefits of the control equipment. Energy-saving mode adjustment module, used to adjust the energy-saving mode according to the energy-saving benefit evaluation results of the control equipment; The control module is used to control the operation of the data acquisition module, the control difficulty analysis module, the operation stability analysis module, the energy-saving benefit evaluation module and the energy-saving mode adjustment module.

[0014] Compared with the prior art, the present invention has the following advantages and beneficial effects: The present invention imports the control record data and device parameter data of the control device into the device control difficulty analysis model to analyze the control difficulty of the control device; imports the switch operation data and current operating status data of the distribution switch into the operation stability analysis model to analyze the operation stability of the distribution switch; constructs a control device energy-saving benefit evaluation model, imports the control difficulty analysis results of the control device and the operation stability analysis results of the distribution switch into the control device energy-saving benefit evaluation model to evaluate the energy-saving benefit of the control device; and adjusts the energy-saving mode based on the energy-saving benefit evaluation results of the control device. This system can accurately and dynamically optimize the energy-saving mode parameters, balance energy saving and device operation, and improve the adaptability and energy-saving effect of the energy-saving mode. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings: Figure 1 This is a schematic diagram of the overall process of the energy-saving optimization method for power distribution switch control equipment based on data fusion of the present invention; Figure 2 This is a workflow diagram of step S2 in the energy-saving optimization method for power distribution switch control equipment based on data fusion of the present invention; Figure 3 This is a workflow diagram of step S3 in the energy-saving optimization method for power distribution switch control equipment based on data fusion of the present invention; Figure 4 It is a structural diagram of the energy-saving optimization system for distribution switch control equipment based on data fusion of the present invention. DETAILED DESCRIPTION

[0016] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0017] Example 1:

[0018] like Figure 1 As shown, this embodiment provides a method for energy-saving optimization of distribution switch control equipment based on data fusion, which specifically includes the following steps: S1. Obtain the switch operation data and current operation status data of the distribution switch, and simultaneously obtain the control record data and device parameter data of the control device; S2. Importing the control record data and device parameter data of the control device into the device control difficulty analysis model to analyze the control difficulty of the control device; S3. Importing the switch operation data and current operation status data of the distribution switch into the operation stability analysis model to analyze the operation stability of the distribution switch; S4. Construct a control equipment energy-saving benefit evaluation model, import the control difficulty analysis results of the control equipment and the operation stability analysis results of the distribution switch into the control equipment energy-saving benefit evaluation model, and evaluate the energy-saving benefits of the control equipment; S5. Adjust the energy-saving mode according to the energy-saving benefit evaluation results of the control equipment.

[0019] In this embodiment, if Figure 2 As shown, in step S2, the control difficulty of the control device is analyzed, including the following specific steps: S21, extracting control record data and device parameter data of the control device in the current energy-saving mode; S22. Based on the control record data and device parameter data of the control device in the current energy-saving mode, construct a device control difficulty analysis model, analyze the control difficulty of the control device in the current energy-saving mode, and obtain a control difficulty analysis result of the control device in the current energy-saving mode; The calculation formula for control difficulty is: ; Where Kn is the control difficulty of the control device in the current energy-saving mode, fz is the analysis result of the control operation complexity of the control device in the current energy-saving mode, fb is the analysis result of the control frequency fluctuation level of the control device in the current energy-saving mode, ps is the number of manual control operations of the control device in the current energy-saving mode, and pt is the total number of control operations of the control device in the current energy-saving mode.

[0020] For example, this embodiment comprehensively considers the influence of the complexity of the control operation and the level of control frequency fluctuation on the control difficulty, and introduces the manual control ratio to modify the control difficulty calculation formula. Factors such as different operation types and execution time deviations determine the complexity of the control logic, which is the basis for measuring the control difficulty. Therefore, this embodiment introduces fz as a parameter of the calculation formula to reflect the influence of the complexity of the control operation on the control difficulty. The stability of the control frequency is related to the operating rhythm of the control device. The greater the fluctuation of the control frequency, the higher the control difficulty of the device. Therefore, this embodiment introduces fb to reflect the influence of the control frequency fluctuation level on the control difficulty. Furthermore, this embodiment adopts , calculating the synergistic effect of the normalized control operation complexity and control frequency fluctuation level can accurately reflect the joint contribution of the two to the control difficulty, and avoid a single factor dominating the calculation result, so that the control operation complexity and control frequency fluctuation level can be adapted at different levels, and at the same time make the calculation result more in line with the difficulty judgment logic of multi-factor coupling in the actual control operation scenario. Specifically, this embodiment reflects the impact of manual intervention on the control difficulty by introducing the ratio of manual control times to the total control times. In this embodiment, a high proportion of manual operations means that the control logic of the control device is more difficult to automate and more complex to coordinate. Using the ratio of manual control times to the total control times as a correction term can further refine the evaluation of the control difficulty, thereby comprehensively and accurately quantifying the control difficulty of the control device under the current energy-saving mode.

[0021] In this embodiment, the process of constructing the device control difficulty analysis model in step S22 includes the following specific steps: S221. Analyze the control operation complexity of the control device in the current energy-saving mode based on the control record data and device parameter data of the control device in the current energy-saving mode to obtain a control operation complexity analysis result of the control device in the current energy-saving mode; The calculation formula for the complexity of control operation is: ; where fz is the complexity of the control operation of the control device in the current energy-saving mode, n is the number of control operation types of the control device in the current energy-saving mode in the device parameter data, pi is the ratio of the number of control operations of the i-th type in the current energy-saving mode to the total number of control operations in the control record data, tci is the difference between the average execution time of the i-th type of control operation in the current energy-saving mode of the control device in the control record data and the design standard execution time, and stc is the standard deviation of the execution time of all types of control operations of the control device in the current energy-saving mode in the control record data.

[0022] For example, this embodiment analyzes the complexity of control operations from two dimensions: operation type distribution and operation duration deviation. The operation type distribution is calculated, and pi is the proportion of the number of control operations of type i. The larger the entropy value, the more dispersed and disordered the distribution of the control operation types is, which can reflect the complexity of the control operation at the control operation type level. Furthermore, this embodiment is divided by Normalizing the entropy value can make the entropy values ​​under different types of control operations comparable, and at the same time make the results of this part in the range of 0 to 1, so as to facilitate subsequent combined analysis with other factors. It is the sum of the differences between the average execution time of various types of operations and the designed standard execution time, which can reflect the cumulative impact of operation time deviations. stc is the standard deviation of the execution time of all types of operations, which can reflect the discrete degree of execution time fluctuations. The effect of duration deviation fluctuation is quantified and the effect is quantified by adding 1 and summing it with the operation type distribution item. By multiplying the time duration, the influence of the time duration deviation can be incorporated into the calculation of the overall control operation complexity, which shows that the larger the execution time deviation and the more significant its relative fluctuation, the stronger the amplification effect on the control operation complexity. It can effectively characterize the disorder of discrete events, thereby meeting the requirements for quantifying the complexity of operation type distribution; introducing duration deviation to reflect the actual working conditions in which the deviation of operation execution duration from the design standard in actual control will increase the difficulty of control; and comprehensively analyzing the complexity of operation type distribution and duration deviation fluctuations to reflect the complexity of control operations. It can comprehensively cover the complex factors of control operations in type distribution and timing execution, and complete the accurate quantification of the complexity of control operations.

[0023] S222. Analyze the control frequency fluctuation level of the control device in the current energy-saving mode based on the control record data of the control device in the current energy-saving mode to obtain an analysis result of the control frequency fluctuation level of the control device in the current energy-saving mode; Among them, the calculation formula for controlling frequency fluctuation level is: ; Where fb is the control frequency fluctuation level of the control device in the current energy-saving mode, M is the number of historical operation cycles of the control device in the current energy-saving mode in the control record data, and fm is the control frequency of the control device in the mth historical operation cycle in the current energy-saving mode in the control record data. is the average value of the control frequency of the control device in all historical operation cycles in the current energy-saving mode in the control record data, fmax and fmin are the maximum and minimum values ​​of the control frequency of the control device in all historical operation cycles in the current energy-saving mode in the control record data, respectively.

[0024] Exemplarily, this embodiment analyzes the control frequency fluctuation level from two aspects: the degree of frequency dispersion and the extreme value difference. It is the coefficient of variation of the control frequency, which can eliminate the influence of different mean values ​​of the control frequency, thereby accurately reflecting the discrete fluctuation degree of the control frequency around the mean value and reflecting the stability characteristics of the control frequency. Furthermore, this embodiment introduces the hyperbolic tangent function to describe the influence of the frequency extreme value difference relative to the mean value. The larger the extreme value difference, the closer the function value is to 1, and the more obvious the amplification effect on the control frequency fluctuation level. Therefore, this embodiment takes into account that the maximum and minimum value differences of the control frequency in practice will significantly affect the operating rhythm and coordination difficulty of the control equipment. Therefore, the nonlinear characteristics of the hyperbolic tangent function are used to reasonably reflect the influence of the maximum and minimum value differences of the control frequency. At the same time, divided by Normalization is performed to make control frequency fluctuations with different means comparable, thereby achieving accurate quantification of regular fluctuations and extreme differences in control frequency, and realizing accurate calculation of the impact of control frequency fluctuation levels on control difficulty.

[0025] In this embodiment, if Figure 3 As shown, in step S3, the operation stability of the distribution switch is analyzed, including the following specific steps: S31, extracting switch operation data and current operation status data of the power distribution switch in the current energy-saving mode; S32. Based on the switch operation data and current operation status data of the power distribution switch in the current energy-saving mode, construct an operation stability analysis model, analyze the operation stability of the power distribution switch in the current energy-saving mode, and obtain an operation stability analysis result of the power distribution switch in the current energy-saving mode; The calculation formula for the operating stability of the distribution switch is: ; Where Yw is the operating stability of the distribution switch in the current energy-saving mode, gr is the analysis result of the control behavior interference degree generated by the control equipment during the operation of the distribution switch in the current energy-saving mode, and sh is the analysis result of the operating loss degree of the distribution switch in the current energy-saving mode.

[0026] Exemplarily, this embodiment evaluates the operational stability of the distribution switch based on the control behavior interference degree and the operational loss degree. This embodiment calculates the operational stability of the distribution switch by taking the negative sum of the control behavior interference degree and the operational loss degree as an exponential term, which can reflect that the greater the control behavior interference and the greater the operational loss, the lower the operational stability: under actual working conditions, the control behavior interference degree and the operational loss degree will destroy the stable operating state of the distribution switch. This embodiment adopts an exponential function and utilizes its nonlinear mapping characteristics to the input to effectively distinguish the differences in the operational stability of the distribution switch under different interference and loss levels. When the interference and loss are small, the operational stability of the distribution switch rises rapidly and approaches 1; when the interference and loss are large, the operational stability of the distribution switch drops sharply and approaches 0, which can meet the law that the operational stability of the distribution switch changes with interference and loss, thereby intuitively and accurately quantifying the operational stability of the distribution switch in the current energy-saving mode.

[0027] In this embodiment, the construction process of the running stability analysis model in step S32 includes the following specific steps: S321. Analyze, based on the switch operation data and current operation status data of the power distribution switch in the current energy-saving mode, the control behavior interference degree generated by the control device during the operation of the power distribution switch in the current energy-saving mode, to obtain an analysis result of the control behavior interference degree generated by the control device during the operation of the power distribution switch in the current energy-saving mode; The calculation formula for the degree of interference of control behavior is: ; Where gr is the control behavior interference degree generated by the control device during the operation of the distribution switch in the current energy-saving mode, sf is the standard deviation of the control frequency of the control device in all historical operation cycles in the current energy-saving mode, T is the total duration of all historical operation cycles of the distribution switch control device in the current energy-saving mode in the switch operation data, u is the rated voltage value of the distribution switch terminal in the switch operation data, and ut is the real-time voltage value of the distribution switch terminal in all historical operation cycles in the current energy-saving mode in the current operation status data.

[0028] For example, this embodiment comprehensively analyzes the influence of control frequency and voltage fluctuation on the interference degree of control behavior. The control frequency mean is used to reflect the average rhythm of control behavior; the control frequency standard deviation is used to reflect the frequency fluctuation degree; Quantify the stability of the control frequency. The more stable the control frequency is, The larger the value, the more stable the control behavior. Furthermore, this embodiment also analyzes the interference degree of the control behavior in combination with the voltage fluctuation. When the voltage fluctuation is large, the interference degree will still increase. The time integral of the voltage deviation measures the cumulative impact of voltage fluctuations and quantifies the relative voltage fluctuation level by dividing by the product of time and rated voltage. Based on the above, this embodiment combines the control frequency characteristics with the voltage fluctuation characteristics in a multiplicative form. This reflects how the control behavior of the control device interferes with the operation of the distribution switch by affecting parameters such as voltage, and that frequency instability and voltage fluctuations increase the degree of interference, thereby accurately quantifying the degree of interference caused by the control behavior on the operation of the distribution switch.

[0029] S322. Analyze the degree of operation loss of the power distribution switch in the current energy-saving mode based on the switch operation data and current operation status data of the power distribution switch in the current energy-saving mode to obtain an analysis result of the degree of operation loss of the power distribution switch in the current energy-saving mode; The calculation formula for the degree of operating loss is: ; Where, sh is the operating loss degree of the distribution switch in the current energy-saving mode, M is the number of historical operating cycles of the control device in the current energy-saving mode, is the average carrying current value of the distribution switch in the mth historical operation cycle in the current energy-saving mode in the current operation status data, is the average value of the contact resistance of the distribution switch contacts in all historical operating cycles in the current energy-saving mode in the current operating status data, Pe is the rated power loss at the distribution switch end in the switch operating data, Ty is the average operating temperature of the internal conductive circuit of the distribution switch in all historical operating cycles in the current energy-saving mode in the current operating status data, and Tmax is the maximum value of the safe operating temperature of the internal conductive circuit of the distribution switch in the switch operating data.

[0030] For example, this embodiment analyzes the operating loss of the power distribution switch in the current energy-saving mode from three dimensions: current, resistance, and temperature. Current heat loss is the core of the operating loss of the power distribution switch. This embodiment is based on Joule's law and uses The cumulative degree of current heat loss is the main source of operating loss of the distribution switch; by summing multiple operating cycles and dividing by the product of M and rated power loss, the loss is normalized and averaged, so that the calculation result of the operating loss degree can reflect the degree of average loss relative to the rated loss under different operating cycles. Furthermore, temperature rise will accelerate equipment aging and increase loss. This embodiment introduces , which can reflect the impact of temperature on operating losses. The higher the temperature and the closer it is to the maximum safety value, the faster the operating loss of the distribution switch. Based on the above content, this embodiment can fully quantify the operating loss of the distribution switch in the current energy-saving mode.

[0031] In this embodiment, the energy-saving benefit evaluation model of the control device is constructed in step S4, including the following specific steps: S41. Extracting and analyzing the control difficulty analysis results of the control device in the current energy-saving mode and the operation stability analysis results of the distribution switch; S42. Evaluate the energy-saving benefits of the control device in the current energy-saving mode based on the control difficulty analysis results of the control device in the current energy-saving mode and the operation stability analysis results of the distribution switch; Among them, the evaluation formula for the energy-saving benefit of the control equipment is: ; Where NX is the energy-saving benefit of the control device in the current energy-saving mode, Kn is the control difficulty analysis result of the control device in the current energy-saving mode, and Yw is the operation stability analysis result of the distribution switch in the current energy-saving mode.

[0032] For example, this embodiment evaluates energy-saving benefits by combining the ratio of operational stability to control difficulty with the hyperbolic tangent function. The output value of the hyperbolic tangent function can be controlled between -1 and 1. Furthermore, in this embodiment, since both operational stability and control difficulty are non-negative values, the calculated energy-saving benefit can be between 0 and 1. A larger calculated value indicates a better energy-saving benefit. The ratio of operational stability to control difficulty reflects the trade-off between operational stability and control difficulty when evaluating energy-saving benefits. When operational stability is high and control difficulty is low, the ratio is large, indicating good energy-saving benefits; otherwise, it is poor. This embodiment selects the hyperbolic tangent function, leveraging its nonlinear compression characteristics on the input. It can map ratios of different magnitudes to a reasonable range, thereby highlighting the differences in energy-saving benefits under different energy-saving modes while avoiding the excessive impact of extreme values ​​on energy-saving benefits. This allows for a concise and effective evaluation of the energy-saving benefits of the control device under the current energy-saving mode, reflecting the comprehensive benefits of the energy-saving mode while ensuring operational stability and control difficulty.

[0033] In this embodiment, the energy-saving mode is adjusted according to the energy-saving benefit evaluation result of the control device in step S5, which includes the following specific steps: S51. Obtain energy-saving benefit evaluation results of control devices in all energy-saving modes; S52. Obtain the energy-saving mode corresponding to the maximum energy-saving benefit evaluation result of the control device in all energy-saving modes as the optimal energy-saving mode, and adjust the energy-saving mode of the distribution switch control device to the optimal energy-saving mode.

[0034] Example 2:

[0035] like Figure 4As shown, this embodiment provides a power distribution switch control equipment energy-saving optimization system based on data fusion, including: The data acquisition module is used to obtain the switch operation data and current operation status data of the distribution switch, and the control record data and device parameter data of the control device; A control difficulty analysis module is used to import the control record data and device parameter data of the control device into the device control difficulty analysis model to analyze the control difficulty of the control device; An operation stability analysis module is used to import the switch operation data and current operation status data of the distribution switch into the operation stability analysis model to analyze the operation stability of the distribution switch; The energy-saving benefit evaluation module is used to build an energy-saving benefit evaluation model for control equipment. The control difficulty analysis results of the control equipment and the operation stability analysis results of the distribution switch are imported into the energy-saving benefit evaluation model to evaluate the energy-saving benefits of the control equipment. Energy-saving mode adjustment module, used to adjust the energy-saving mode according to the energy-saving benefit evaluation results of the control equipment; The control module is used to control the operation of the data acquisition module, the control difficulty analysis module, the operation stability analysis module, the energy-saving benefit evaluation module and the energy-saving mode adjustment module.

[0036] The above-mentioned parameters and steps for each unit module to implement corresponding functions in the energy-saving optimization system of distribution switch control equipment based on data fusion of the present invention can refer to the parameters and steps in the embodiment of the energy-saving optimization method of distribution switch control equipment based on data fusion above, and will not be repeated here.

[0037] The various embodiments of the present invention are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the IoT device and medium embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0038] The system and medium provided in the embodiments of the present invention correspond one-to-one to the method. Therefore, the system and medium also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the system and medium will not be repeated here.

[0039] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0040] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0041] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0042] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0043] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0044] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0045] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0046] The above are merely embodiments of the present invention and are not intended to limit the present invention. It will be apparent to those skilled in the art that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are intended to be included within the scope of the claims of the present invention.

Claims

1. A method for energy-saving optimization of distribution switch control equipment based on data fusion, characterized in that: The steps include: S1. Obtain the switch operation data and current operation status data of the distribution switch, and simultaneously obtain the control record data and device parameter data of the control device; S2. Importing the control record data and device parameter data of the control device into the device control difficulty analysis model to analyze the control difficulty of the control device; S3. Importing the switch operation data and current operation status data of the distribution switch into the operation stability analysis model to analyze the operation stability of the distribution switch; S4. Construct a control equipment energy-saving benefit evaluation model, import the control difficulty analysis results of the control equipment and the operation stability analysis results of the distribution switch into the control equipment energy-saving benefit evaluation model, and evaluate the energy-saving benefits of the control equipment; S5. Adjust the energy-saving mode according to the energy-saving benefit evaluation results of the control equipment.

2. The energy-saving optimization method for distribution switch control equipment based on data fusion according to claim 1 is characterized in that: The step S2 of analyzing the control difficulty of the control device includes the following specific steps: S21, extracting control record data and device parameter data of the control device in the current energy-saving mode; S22. Based on the control record data and device parameter data of the control device in the current energy-saving mode, construct a device control difficulty analysis model, analyze the control difficulty of the control device in the current energy-saving mode, and obtain a control difficulty analysis result of the control device in the current energy-saving mode; The calculation formula for control difficulty is: ； Where Kn is the control difficulty of the control device in the current energy-saving mode, fz is the analysis result of the control operation complexity of the control device in the current energy-saving mode, fb is the analysis result of the control frequency fluctuation level of the control device in the current energy-saving mode, ps is the number of manual controls of the control device in the current energy-saving mode, and pt is the total number of controls of the control device in the current energy-saving mode.

3. The energy-saving optimization method for distribution switch control equipment based on data fusion according to claim 2 is characterized in that: The process of constructing the equipment control difficulty analysis model in step S22 includes the following specific steps: S221. Analyze the control operation complexity of the control device in the current energy-saving mode based on the control record data and device parameter data of the control device in the current energy-saving mode to obtain a control operation complexity analysis result of the control device in the current energy-saving mode; S222: Analyze the control frequency fluctuation level of the control device in the current energy-saving mode based on the control record data of the control device in the current energy-saving mode to obtain an analysis result of the control frequency fluctuation level of the control device in the current energy-saving mode.

4. The energy-saving optimization method for distribution switch control equipment based on data fusion according to claim 3 is characterized in that: The analysis of the operating stability of the distribution switch in step S3 includes the following specific steps: S31, extracting switch operation data and current operation status data of the power distribution switch in the current energy-saving mode; S32. Based on the switch operation data and current operation status data of the power distribution switch in the current energy-saving mode, construct an operation stability analysis model, analyze the operation stability of the power distribution switch in the current energy-saving mode, and obtain an operation stability analysis result of the power distribution switch in the current energy-saving mode; The calculation formula for the operating stability of the distribution switch is: ； Where Yw is the operating stability of the distribution switch in the current energy-saving mode, gr is the analysis result of the control behavior interference degree generated by the control equipment during the operation of the distribution switch in the current energy-saving mode, and sh is the analysis result of the operating loss degree of the distribution switch in the current energy-saving mode.

5. The energy-saving optimization method for distribution switch control equipment based on data fusion according to claim 4 is characterized in that: The construction process of the running stability analysis model in step S32 includes the following specific steps: S321. Analyze, based on the switch operation data and current operation status data of the power distribution switch in the current energy-saving mode, the control behavior interference degree generated by the control device during the operation of the power distribution switch in the current energy-saving mode, to obtain an analysis result of the control behavior interference degree generated by the control device during the operation of the power distribution switch in the current energy-saving mode; S322. Based on the switch operation data and current operation status data of the distribution switch in the current energy-saving mode, analyze the operation loss degree of the distribution switch in the current energy-saving mode to obtain the analysis result of the operation loss degree of the distribution switch in the current energy-saving mode.

6. The energy-saving optimization method for distribution switch control equipment based on data fusion according to claim 5 is characterized in that: The step S4 constructs a control equipment energy-saving benefit evaluation model, including the following specific steps: S41. Extracting and analyzing the control difficulty analysis results of the control device in the current energy-saving mode and the operation stability analysis results of the distribution switch; S42. Evaluate the energy-saving benefits of the control device in the current energy-saving mode based on the control difficulty analysis results of the control device in the current energy-saving mode and the operation stability analysis results of the distribution switch; Among them, the evaluation formula for the energy-saving benefit of the control equipment is: ； Where NX is the energy-saving benefit of the control device in the current energy-saving mode, Kn is the control difficulty analysis result of the control device in the current energy-saving mode, and Yw is the operation stability analysis result of the distribution switch in the current energy-saving mode.

7. The energy-saving optimization method for distribution switch control equipment based on data fusion according to claim 6 is characterized in that: In step S5, the energy-saving mode is adjusted according to the energy-saving benefit evaluation result of the control device, including the following specific steps: S51. Obtain energy-saving benefit evaluation results of control devices in all energy-saving modes; S52. Obtain the energy-saving mode corresponding to the maximum energy-saving benefit evaluation result of the control device in all energy-saving modes as the optimal energy-saving mode, and adjust the energy-saving mode of the distribution switch control device to the optimal energy-saving mode.

8. A power distribution switch control equipment energy-saving optimization system based on data fusion, which is implemented based on the power distribution switch control equipment energy-saving optimization method based on data fusion according to any one of claims 1 to 7, and is characterized in that: The system comprises: The data acquisition module is used to obtain the switch operation data and current operation status data of the distribution switch, and the control record data and device parameter data of the control device; A control difficulty analysis module is used to import the control record data and device parameter data of the control device into the device control difficulty analysis model to analyze the control difficulty of the control device; An operation stability analysis module is used to import the switch operation data and current operation status data of the distribution switch into the operation stability analysis model to analyze the operation stability of the distribution switch; The energy-saving benefit evaluation module is used to build an energy-saving benefit evaluation model for control equipment. The control difficulty analysis results of the control equipment and the operation stability analysis results of the distribution switch are imported into the energy-saving benefit evaluation model to evaluate the energy-saving benefits of the control equipment. Energy-saving mode adjustment module, used to adjust the energy-saving mode according to the energy-saving benefit evaluation results of the control equipment; A control module is used to control the operation of the data acquisition module, the control difficulty analysis module, the operation stability analysis module, the energy-saving benefit evaluation module and the energy-saving mode adjustment module.

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