A method, apparatus, system, and medium for conditioning of a conditioner

Abstract

The application discloses a kind of regulator's adjusting method, device, system and medium, method includes: obtaining target time to determine target power load state;According to target power load state selection target power factor prediction model, obtain target access mode;By target access mode the target time is input into the power factor prediction model and obtains predicted power factor variation curve;Obtain reference deviation threshold value;According to predicted power factor variation curve control power capacitor is put in and cut out, so that the deviation value between the real-time power factor variation curve of power system in the target time period after the target time and the predicted power factor variation curve is less than the reference deviation threshold value.The application relies on predicted power factor variation curve as guidance, can calculate the input and cut out strategy of external power capacitor in advance, to realize the efficient adjustment to power system.The application is mainly used in the technical field of intelligent power system.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power systems, and in particular to a regulating method, device, system and medium for a regulator. Background Art

[0002] For some local power systems, such as schools, there are generally relatively regular electricity usage habits. Therefore, the system's reactive power demand will fluctuate according to this pattern. The existing method of adjusting the power factor of the entire system is generally through real-time adjustment. That is, by obtaining the current system power factor in real time and adjusting it in real time. However, this adjustment method takes a long time overall and cannot achieve the best results. Therefore, how to improve the adjustment efficiency of the regulator is a technical problem that urgently needs to be studied in the industry. Summary of the Invention

[0003] The present invention provides a method, device, system and medium for adjusting a regulator to solve one or more technical problems existing in the prior art and at least provide a beneficial option or create conditions.

[0004] The present invention provides a method for adjusting a regulator, comprising: obtaining a current time and recording the time as a target time;

[0005] determining a current power load state of the power system according to the target time, and recording the power load state as a target power load state;

[0006] Selecting a suitable power factor prediction model according to the target power load state, recording the power factor prediction model as a target power factor prediction model, obtaining an access mode of the target power factor prediction model, and recording the access mode as a target access mode;

[0007] Inputting the target time into the power factor prediction model through the target access method, and predicting a power factor change curve within a target time period after the target time through the power factor prediction model; recording the power factor change curve as a predicted power factor change curve;

[0008] Obtaining a preset power factor deviation threshold according to the target power load state, and recording the deviation threshold as a reference deviation threshold;

[0009] An adjustment strategy for an external power capacitor is calculated based on the predicted power factor change curve, and the external power capacitor is controlled to be switched on and off based on the adjustment strategy so that a deviation value between a real-time power factor change curve of the power system within a target time period after the target time and the predicted power factor change curve is less than the reference deviation threshold.

[0010] Furthermore, obtaining the access method of the target power factor prediction model specifically includes: transmitting the tag number of the target power load state to a model management server, and receiving feedback from the model management server to obtain the access method of the target power factor prediction model. The model management server pre-stores a mapping table between the tag number and the access method of the target power factor prediction model. The model management server determines the access method of the target power factor prediction model by querying the mapping table based on the tag number, and provides feedback on the access method.

[0011] Furthermore, obtaining a preset power factor deviation threshold based on the target power load state specifically includes: transmitting the target power load state tag number to a threshold management server, and receiving feedback from the threshold management server to obtain the power factor deviation threshold. The threshold management server pre-stores a comparison table between the target power load state tag number and the power factor deviation threshold. The threshold management server determines the power factor deviation threshold by querying the comparison table based on the target power load state tag number, and provides feedback on the power factor deviation threshold.

[0012] Furthermore, the power factor prediction model is an artificial intelligence model constructed based on the long short-term memory network LSTM.

[0013] On the other hand, a regulating device for a regulator is provided, comprising: a processor and a memory, wherein the memory is used to store a computer-readable program; when the computer-readable program is executed by the processor, the processor implements the regulating method for the regulator as described in any one of the above technical solutions.

[0014] In another aspect, a regulating system for a regulator is provided, comprising: a first acquisition module, a determination module, a selection module, a prediction module, a second acquisition module, and a control module;

[0015] The first acquisition module is used to: acquire the current time and record the time as the target time;

[0016] The determining module is configured to: determine the current power load state of the power system according to the target time, and record the power load state as the target power load state;

[0017] The selection module is configured to: select an appropriate power factor prediction model according to the target power load state, record the power factor prediction model as a target power factor prediction model, obtain an access mode of the target power factor prediction model, and record the access mode as a target access mode;

[0018] The prediction module is configured to: input the target time into the power factor prediction model through the target access method, predict a power factor change curve within a target time period after the target time through the power factor prediction model; and record the power factor change curve as a predicted power factor change curve;

[0019] The second acquisition module is used to: acquire a preset power factor deviation threshold according to the target power load state, and record the deviation threshold as a reference deviation threshold;

[0020] The control module is used to calculate an adjustment strategy for an external power capacitor based on the predicted power factor change curve, and control the switching on and off of the external power capacitor based on the adjustment strategy, so that the deviation value between the real-time power factor change curve of the power system within a target time period after the target time and the predicted power factor change curve is less than the reference deviation threshold.

[0021] Furthermore, obtaining the access method of the target power factor prediction model specifically includes: transmitting the tag number of the target power load state to a model management server, and receiving feedback from the model management server to obtain the access method of the target power factor prediction model. The model management server pre-stores a mapping table between the tag number and the access method of the target power factor prediction model. The model management server determines the access method of the target power factor prediction model by querying the mapping table based on the tag number, and provides feedback on the access method.

[0022] Furthermore, obtaining a preset power factor deviation threshold based on the target power load state specifically includes: transmitting the target power load state tag number to a threshold management server, and receiving feedback from the threshold management server to obtain the power factor deviation threshold. The threshold management server pre-stores a comparison table between the target power load state tag number and the power factor deviation threshold. The threshold management server determines the power factor deviation threshold by querying the comparison table based on the target power load state tag number, and provides feedback on the power factor deviation threshold.

[0023] On the other hand, a computer-readable storage medium is provided, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to implement the adjustment method of the regulator as described in any one of the above technical solutions.

[0024] The present invention has at least the following beneficial effects: the method of the present invention determines the power load state (peak power consumption period or low power consumption period, etc.) of the current power system by obtaining the target time, and selects a suitable power factor prediction model according to the power load state. The power factor prediction model is used to predict the predicted power factor change curve in the future target time period under this power load state. This allows the smart device to know the future power factor changes in advance. Therefore, relying on the predicted power factor change curve as a guide, the input and output strategies of external power capacitors can be calculated in advance to achieve efficient adjustment of the power system. The present invention also provides corresponding devices, systems and media. The beneficial effects of the devices, systems and media are similar to those of the method and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings are used to provide a further understanding of the technical solution of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the technical solution of the present invention and do not constitute a limitation to the technical solution of the present invention.

[0026] Figure 1 It is a flow chart of steps of a method for adjusting a regulator;

[0027] Figure 2 It is a schematic diagram of the device structure of the regulating device of the regulator;

[0028] Figure 3 It is a schematic diagram of the system connection structure of the regulator's regulation system. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0030] It should be noted that although the system diagrams illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the system or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0031] refer to Figure 1 , Figure 1 This is a flowchart of the steps of the regulator adjustment method. This regulator adjustment method is mainly operated through a smart device. When the smart device is running, the steps that can be performed include:

[0032] Step 1: Get the current time and record it as the target time.

[0033] The smart device obtains the current time through the clock device. For the convenience of description, the obtained current time is recorded as the target time.

[0034] Step 2: Determine the current power load state of the power system according to the target time.

[0035] The smart device internally stores a load state table showing the relationship between time and power load state. The smart device determines the power load state of the power system corresponding to the target time according to the load state table.

[0036] The load status table is pre-obtained through statistical analysis and records the power load status of the power system during each target time period over a year. These power load statuses are distinguished by different tags. The power system refers to a power network system in a specific closed application scenario, such as a school, hospital, or office building.

[0037] Step 3: Select a suitable power factor prediction model according to the target power load state, record the power factor prediction model as the target power factor prediction model, obtain an access method of the target power factor prediction model, and record the access method as the target access method.

[0038] After determining the power load state, the smart device can select an appropriate power factor prediction model based on the tag number. The power factor prediction model is pre-trained for the power load state of the power system corresponding to the tag number, and can predict the optimal power factor of the power system in a future time period based on time.

[0039] In some further specific embodiments, the power factor prediction model is an artificial intelligence model based on a long short-term memory (LSTM) network. LSTM (Long Short-Term Memory) is a special recurrent neural network (RNN) architecture primarily used to process and predict important events with very long intervals and delays in time series data.

[0040] The power factor prediction model is pre-trained, stored, and run in a specific server. Therefore, when a power factor prediction model needs to be requested for prediction, the smart device can request the power factor prediction model for prediction by knowing a certain access method and using the access method.

[0041] In some further specific embodiments, obtaining the access method of the target power factor prediction model specifically includes: the smart device transmits the tag number of the target power load state to the model management server, and receives feedback from the model management server to obtain the access method of the target power factor prediction model. The model management server pre-stores a mapping table between the tag number and the access method of the target power factor prediction model, and the model management server determines the access method of the target power factor prediction model by querying the mapping table based on the tag number, and provides feedback on the access method.

[0042] Step 4: input the target time into the power factor prediction model through the target access method, and predict the power factor change curve within the target time period after the target time through the power factor prediction model; record the power factor change curve as the predicted power factor change curve.

[0043] After the smart device determines the target access method, it can access the power factor prediction model through the target access method. And, request the power factor prediction model to make a prediction. The smart device inputs the target time into the power factor prediction model, and requests the power factor prediction model to make a prediction based on the target time to obtain a predicted power factor change curve. The predicted power factor change curve refers to the predicted relationship between the optimal power factor and time within the target time period after the target time. In some further specific embodiments, the target time period is 90 days. Through research, it was found that in application scenarios such as schools, the reactive power demand of the power system and the system stability change based on 90 days.

[0044] Step 5: Acquire a preset power factor deviation threshold according to the target power load state, and record the deviation threshold as a reference deviation threshold.

[0045] In addition to obtaining the predicted power factor change curve, the smart device also needs to obtain a reference deviation threshold. In some further specific embodiments, the smart device transmits the tag number of the target power load state to the threshold management server, and receives feedback from the threshold management server to obtain the power factor deviation threshold. The threshold management server pre-stores a comparison table between the tag number of the target power load state and the power factor deviation threshold. The threshold management server determines the power factor deviation threshold by querying the comparison table based on the tag number of the target power load state, and feeds back the power factor deviation threshold.

[0046] Step 6: Calculate an adjustment strategy for the external power capacitor based on the predicted power factor change curve, and control the switching on and off of the external power capacitor based on the adjustment strategy so that the deviation value between the real-time power factor change curve of the power system in the target time period after the target time and the predicted power factor change curve is less than the reference deviation threshold.

[0047] After the intelligent device obtains the predicted power factor change curve and the reference deviation threshold, it can calculate the adjustment strategy for the external power capacitor based on the predicted power factor change curve and control the switching on and off of the external power capacitor according to the adjustment strategy. Specifically, in the adjustment strategy, the intelligent device will compare the actual power factor change curve with the predicted power factor change curve while adding the reference deviation threshold for consideration, thereby guiding the switching on and off of the external power capacitor. When the external power capacitor is switched on and off, the deviation value between the actual power change curve and the predicted power factor change curve is less than the reference deviation threshold.

[0048] The present invention determines the current power system load state (peak or off-peak) by acquiring a target time. This load state is then used to select an appropriate power factor prediction model. The power factor prediction model then generates a predicted power factor curve for a target time period in the future under this load state. This allows smart devices to predict future power factor changes. Using the predicted power factor curve as a guide, strategies for switching external power capacitors in place can be calculated in advance, achieving efficient adjustment of the power system.

[0049] On the other hand, reference Figure 2 , Figure 2 It is a schematic diagram of the device structure of the regulating device of the regulator.

[0050] A regulator adjustment device is provided, comprising: a processor and a memory; wherein the memory is configured to store a computer-readable program. When the computer-readable program is executed by the processor, the processor implements the regulator adjustment method described in any one of the above-mentioned specific embodiments.

[0051] Those skilled in the art will appreciate that all or some of the steps and systems disclosed above can be implemented as software, firmware, hardware, or any suitable combination thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on computer-readable media, which may include computer storage media (or non-transitory media) and communication media (or transient media). As is well known to those skilled in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVDs) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. As is well known to those skilled in the art, communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0052] On the other hand, reference Figure 3 , Figure 3 It is a schematic diagram of the system connection structure of the regulator's regulation system.

[0053] A regulating system for a regulator is provided, comprising: a first acquisition module, a determination module, a selection module, a prediction module, a second acquisition module, and a control module;

[0054] The first acquisition module is used to obtain the current time and record the time as the target time.

[0055] The first acquisition module acquires the current time through a clock device. For the convenience of description, the acquired current time is recorded as the target time.

[0056] The determining module is configured to determine the current power load state of the power system according to the target time, and record the power load state as the target power load state.

[0057] The determination module internally stores a load state table showing the relationship between time and power load state, and the determination module determines the power load state of the power system corresponding to the target time according to the load state table.

[0058] The load status table is pre-obtained through statistical analysis and records the power load status of the power system during each target time period over a year. These power load statuses are distinguished by different tags. The power system refers to a power network system in a specific closed application scenario, such as a school, hospital, or office building.

[0059] The selection module is used to: select a suitable power factor prediction model according to the target power load state, record the power factor prediction model as the target power factor prediction model, obtain an access method of the target power factor prediction model, and record the access method as the target access method.

[0060] After determining the power load state, the selection module can select an appropriate power factor prediction model based on the tag number. The power factor prediction model is pre-trained for the power load state of the power system corresponding to the tag number, and can predict the optimal power factor of the power system in a future time period based on time.

[0061] In some further specific embodiments, the power factor prediction model is an artificial intelligence model based on a long short-term memory (LSTM) network. LSTM (Long Short-Term Memory) is a special recurrent neural network (RNN) architecture primarily used to process and predict important events with very long intervals and delays in time series data.

[0062] The power factor prediction model is pre-trained, stored, and run in a specific server. Therefore, when a power factor prediction model needs to be requested for prediction, the smart device can request the power factor prediction model for prediction by knowing a certain access method and using the access method.

[0063] In some further specific embodiments, obtaining the access method of the target power factor prediction model specifically includes: the selection module transmits the tag number of the target power load state to the model management server, and receives feedback from the model management server to obtain the access method of the target power factor prediction model. The model management server pre-stores a mapping table between the tag number and the access method of the target power factor prediction model, and the model management server determines the access method of the target power factor prediction model by querying the mapping table based on the tag number, and provides feedback on the access method.

[0064] The prediction module is used to: input the target time into the power factor prediction model through the target access method, predict the power factor change curve within the target time period after the target time through the power factor prediction model; and record the power factor change curve as the predicted power factor change curve.

[0065] After the prediction module determines the target access method, it can access the power factor prediction model through the target access method. And, request the power factor prediction model to make a prediction. The prediction module inputs the target time into the power factor prediction model, and requests the power factor prediction model to make a prediction based on the target time to obtain a predicted power factor change curve. The predicted power factor change curve refers to the predicted relationship between the optimal power factor and time within the target time period after the target time. In some further specific embodiments, the target time period is 90 days. Through research, it was found that in application scenarios such as schools, the reactive power demand of the power system and the system stability change based on 90 days.

[0066] The second acquisition module is configured to acquire a preset power factor deviation threshold according to the target power load state, and record the deviation threshold as a reference deviation threshold.

[0067] In addition to obtaining the predicted power factor change curve, the second acquisition module also needs to obtain a reference deviation threshold. In some further specific embodiments, the second acquisition module will transmit the tag number of the target power load state to the threshold management server, and receive feedback from the threshold management server to obtain the power factor deviation threshold. In some further specific embodiments, the threshold management server pre-stores a comparison table between the tag number of the target power load state and the power factor deviation threshold. The threshold management server determines the power factor deviation threshold by querying the comparison table based on the tag number of the target power load state, and feeds back the power factor deviation threshold.

[0068] The control module is used to calculate an adjustment strategy for an external power capacitor based on the predicted power factor change curve, and control the switching on and off of the external power capacitor based on the adjustment strategy, so that the deviation value between the real-time power factor change curve of the power system within a target time period after the target time and the predicted power factor change curve is less than the reference deviation threshold.

[0069] After the control module obtains the predicted power factor change curve and the reference deviation threshold, it can calculate the adjustment strategy for the external power capacitor based on the predicted power factor change curve and control the switching on and off of the external power capacitor according to the adjustment strategy. Specifically, in the adjustment strategy, the control module will compare the actual power factor change curve with the predicted power factor change curve while adding the reference deviation threshold for consideration, thereby guiding the switching on and off of the external power capacitor. When the external power capacitor is switched on and off, the deviation value between the actual power change curve and the predicted power factor change curve is less than the reference deviation threshold.

[0070] On the other hand, a computer-readable storage medium is provided, in which a program executable by a processor is stored. When the program executable by the processor is executed by the processor, it is used to implement the adjustment method of the regulator as described in any one of the above specific embodiments.

[0071] An embodiment of the present application also discloses a computer program product, including a computer program or computer instructions, which are stored in a computer-readable storage medium. A processor of a computer device reads the computer program or computer instructions from the computer-readable storage medium, and the processor executes the computer program or computer instructions, so that the computer device executes the adjustment method of the regulator as described in any of the previous embodiments.

[0072] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0073] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0075] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0076] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0077] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0078] Although the description of the present application has been quite detailed and specifically describes several embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but should be considered to provide a broad possible interpretation of these claims by reference to the appended claims, taking into account the prior art, so as to effectively cover the intended scope of the present application. In addition, the above description of the present application is based on the embodiments foreseen by the inventors, which is intended to provide a useful description, and those non-substantial changes to the present application that have not yet been foreseen may still represent equivalent changes to the present application.

Claims

1. A method for adjusting a regulator, characterized in that: include: Obtain the current time and record the time as the target time; determining a current power load state of the power system according to the target time, and recording the power load state as a target power load state; Selecting a suitable power factor prediction model according to the target power load state, recording the power factor prediction model as a target power factor prediction model, obtaining an access mode of the target power factor prediction model, and recording the access mode as a target access mode; Inputting the target time into the power factor prediction model through the target access method, and predicting a power factor change curve within a target time period after the target time through the power factor prediction model; recording the power factor change curve as a predicted power factor change curve; Obtaining a preset power factor deviation threshold according to the target power load state, and recording the deviation threshold as a reference deviation threshold; Calculating an adjustment strategy for an external power capacitor according to the predicted power factor variation curve, and controlling the switching on and off of the external power capacitor according to the adjustment strategy so that a deviation value between a real-time power factor variation curve of the power system and the predicted power factor variation curve within a target time period after the target time is less than a reference deviation threshold; Obtaining the access method of the target power factor prediction model specifically includes: transmitting the tag number of the target power load state to a model management server, and receiving feedback from the model management server to obtain the access method of the target power factor prediction model; wherein the model management server pre-stores a mapping table between the tag number and the access method of the target power factor prediction model, and the model management server determines the access method of the target power factor prediction model by querying the mapping table according to the tag number, and feeds back the access method; Obtaining a preset power factor deviation threshold according to the target power load state specifically includes: transmitting the tag number of the target power load state to a threshold management server, and receiving feedback from the threshold management server to obtain the power factor deviation threshold; wherein the threshold management server pre-stores a comparison table between the tag number of the target power load state and the power factor deviation threshold, and the threshold management server determines the power factor deviation threshold by querying the comparison table according to the tag number of the target power load state, and feeds back the power factor deviation threshold.

2. The method for adjusting a regulator according to claim 1, wherein: The power factor prediction model is an artificial intelligence model built based on the long short-term memory network LSTM.

3. A regulating device for a regulator, characterized in that: include: processor; a memory for storing a computer-readable program; When the computer-readable program is executed by the processor, the processor is enabled to implement the regulating method of the regulator according to any one of claims 1 to 2.

4. A regulating system for a regulator, characterized in that: include: a first acquisition module, a determination module, a selection module, a prediction module, a second acquisition module, and a control module; The first acquisition module is used to: acquire the current time and record the time as the target time; The determining module is configured to: determine the current power load state of the power system according to the target time, and record the power load state as the target power load state; The selection module is configured to: select an appropriate power factor prediction model according to the target power load state, record the power factor prediction model as a target power factor prediction model, obtain an access mode of the target power factor prediction model, and record the access mode as a target access mode; The prediction module is configured to: input the target time into the power factor prediction model through the target access method, predict a power factor change curve within a target time period after the target time through the power factor prediction model; and record the power factor change curve as a predicted power factor change curve; The second acquisition module is used to: acquire a preset power factor deviation threshold according to the target power load state, and record the deviation threshold as a reference deviation threshold; The control module is configured to calculate an adjustment strategy for an external power capacitor according to the predicted power factor change curve, and control the switching on and off of the external power capacitor according to the adjustment strategy, so that a deviation value between a real-time power factor change curve of the power system and the predicted power factor change curve within a target time period after the target time is less than a reference deviation threshold; Obtaining the access method of the target power factor prediction model specifically includes: transmitting the tag number of the target power load state to a model management server, and receiving feedback from the model management server to obtain the access method of the target power factor prediction model; wherein the model management server pre-stores a mapping table between the tag number and the access method of the target power factor prediction model, and the model management server determines the access method of the target power factor prediction model by querying the mapping table according to the tag number, and feeds back the access method; Obtaining a preset power factor deviation threshold according to the target power load state specifically includes: transmitting the tag number of the target power load state to a threshold management server, and receiving feedback from the threshold management server to obtain the power factor deviation threshold; wherein the threshold management server pre-stores a comparison table between the tag number of the target power load state and the power factor deviation threshold, and the threshold management server determines the power factor deviation threshold by querying the comparison table according to the tag number of the target power load state, and feeds back the power factor deviation threshold.

5. The regulating system of a regulator according to claim 4, characterized in that: The power factor prediction model is an artificial intelligence model built based on the long short-term memory network LSTM.

6. A computer-readable storage medium, characterized in that A program executable by a processor is stored therein, and when the program executable by the processor is executed by the processor, it is used to implement the regulating method of the regulator according to any one of claims 1 to 2.

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