Regulation method, device and system of regulator and medium

Abstract

The invention discloses an adjusting method, device and system of a regulator and a medium. The method comprises the steps of obtaining target time to determine a target power load state; selecting a target power factor prediction model according to the target power load state to obtain a target access mode; inputting the target time into the power factor prediction model through a target access mode to obtain a predicted power factor change curve; obtaining a reference deviation threshold value; and according to the predicted power factor change curve, controlling input and cut-out of a power capacitor, so that a deviation value between a real-time power factor change curve of the power system in a target time period after the target time and the predicted power factor change curve is smaller than the reference deviation threshold value. According to the method, the input and cut-out strategies of the external power capacitor can be calculated in advance by taking the predicted power factor change curve as guidance, so that the efficient adjustment of the power system is realized. The method is mainly applied to the technical field of intelligent power systems.

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 regular electricity usage habits. Therefore, the reactive power demand of the system will fluctuate according to this pattern. The existing adjustment of the power factor of the entire system is generally carried out through real-time adjustment, that is, by obtaining the power factor of the current system in real time, and adjusting the power factor in real time. However, this adjustment method takes a long time overall, and it cannot achieve the best effect. 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 choice 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; Determining the current power load state of the power system according to the target time, and recording the power load state as the 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; The target time is input into the power factor prediction model through the target access mode, and a power factor change curve in a target time period after the target time is predicted through the power factor prediction model; the power factor change curve is recorded as a predicted power factor change curve; Acquire a preset power factor deviation threshold value according to the target power load state, and record the deviation threshold value as a reference deviation threshold value; 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.

[0005] Furthermore, obtaining the access method of the target power factor prediction model specifically includes: passing the tag number of the target power load state to the 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, 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.

[0006] Furthermore, obtaining the pre-set power factor deviation threshold according to the target power load state specifically includes: transmitting the tag number of the target power load state to the 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 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.

[0007] Furthermore, the power factor prediction model is an artificial intelligence model built based on the long short-term memory network LSTM.

[0008] 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 a regulating method for the regulator as described in any one of the above technical solutions.

[0009] On the other hand, a regulating system of 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; The first acquisition module is used to: acquire the current time and record the time as the target time; The determination module is used to: determine the power load state of the current power system according to the target time, and record the power load state as the target power load state; 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 mode of the target power factor prediction model, and record the access mode as the target access mode; The prediction module is used to: input the target time into the power factor prediction model through the target access mode, predict the power factor change curve in 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; 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 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.

[0010] Furthermore, obtaining the access method of the target power factor prediction model specifically includes: passing the tag number of the target power load state to the 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, 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.

[0011] Furthermore, obtaining the pre-set power factor deviation threshold according to the target power load state specifically includes: transmitting the tag number of the target power load state to the 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 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.

[0012] 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.

[0013] 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 external power capacitor input and output strategy 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

[0014] 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 on the technical solution of the present invention.

[0015] Figure 1 It is a flow chart of steps of a method for adjusting a regulator; Figure 2 is a schematic diagram of the device structure of the regulating device of the regulator; Figure 3 It is a schematic diagram of the system connection structure of the regulator's regulation system. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 used to limit the present invention.

[0017] It should be noted that, although the functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the system or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0018] refer to Figure 1 , Figure 1 This is a flow chart of the steps of the regulator adjustment method. The regulator adjustment method is mainly operated by an intelligent device. When the intelligent device is operated, the steps that can be executed include: Step 1: Get the current time and record the time as the target time.

[0019] 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.

[0020] Step 2: Determine the current power load state of the power system according to the target time.

[0021] The smart device internally stores a load state table showing the relationship between time and power load state, and the smart device determines the power load state of the power system corresponding to the target time according to the load state table.

[0022] The load status table is obtained in advance through statistics, which records the power load status of the power system in each target time period within a year, and these power load statuses are distinguished by setting different marking numbers. The power system refers to the power network system in a certain closed application scenario, such as a school, a hospital or an office building.

[0023] 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.

[0024] After the smart device determines the power load state, it can select a suitable power factor prediction model according to 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 it can predict the optimal power factor of the power system in a future time period through time.

[0025] In some further specific embodiments, the power factor prediction model is an artificial intelligence model based on a long short-term memory network (LSTM). LSTM (Long Short-Term Memory) is a special recurrent neural network (RNN) architecture, which is mainly used to process and predict important events with very long intervals and delays in time series data.

[0026] The power factor prediction model is trained in advance and stored and run in a specific server. Therefore, when the 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.

[0027] 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 according to the tag number, and feeds back the access method.

[0028] Step 4: input the target time into the power factor prediction model through the target access method, and predict the power factor change curve in 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.

[0029] After the smart device determines the target access mode, it can access the power factor prediction model through the target access mode. 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 in the target time period after the target time. Among them, in some further specific embodiments, the target time period is 90 days. Through research, it is found that in application scenarios such as schools, the demand for reactive power of the power system and the stability of the system change for 90 days.

[0030] Step 5: Acquire a preset power factor deviation threshold value according to the target power load state, and record the deviation threshold value as a reference deviation threshold value.

[0031] 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 will pass 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 deviation threshold of the power factor. The threshold management server pre-stores a comparison table between the tag number of the target power load state and the deviation threshold of the power factor. The threshold management server determines the deviation threshold of the power factor by querying the comparison table according to the tag number of the target power load state, and feeds back the deviation threshold of the power factor.

[0032] Step 6: 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 a 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.

[0033] After the intelligent device obtains the predicted power factor change curve and the reference deviation threshold, it can calculate the adjustment strategy of the external power capacitor through the predicted power factor change curve, and control the input and output of the external power capacitor according to the adjustment strategy. Among them, in the adjustment strategy, the intelligent device will add the reference deviation threshold for consideration while comparing the actual power factor change curve with the predicted power factor change curve, so as to guide the input and output of the external power capacitor. When the external power capacitor is put into operation and cut out, the deviation value between the actual power change curve and the predicted power factor change curve is less than the reference deviation threshold.

[0034] The present invention obtains the target time to determine what kind of power load state (peak power consumption period or low power consumption period, etc.) the current power system is in, 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. Thus, the smart device can know the future power factor change in advance. Thus, relying on the predicted power factor change curve as a guide, the external power capacitor input and output strategy can be calculated in advance to achieve efficient adjustment of the power system.

[0035] 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.

[0036] 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 a regulating method for the regulator as described in any one of the above specific embodiments.

[0037] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium 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 include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store 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.

[0038] 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.

[0039] A regulating system of 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; The first acquisition module is used to: acquire the current time and record the time as the target time.

[0040] 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.

[0041] The determination module is used to determine the power load state of the current power system according to the target time, and record the power load state as the target power load state.

[0042] The determination module 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.

[0043] The load status table is obtained in advance through statistics, which records the power load status of the power system in each target time period within a year, and these power load statuses are distinguished by setting different marking numbers. The power system refers to the power network system in a certain closed application scenario, such as a school, a hospital or an office building.

[0044] 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.

[0045] After the selection module determines the power load state, it can select a suitable power factor prediction model according to 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 it can predict the optimal power factor of the power system in a future time period through time.

[0046] In some further specific embodiments, the power factor prediction model is an artificial intelligence model based on a long short-term memory network (LSTM). LSTM (Long Short-Term Memory) is a special recurrent neural network (RNN) architecture, which is mainly used to process and predict important events with very long intervals and delays in time series data.

[0047] The power factor prediction model is trained in advance and stored and run in a specific server. Therefore, when the 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.

[0048] 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 according to the tag number, and feeds back the access method.

[0049] 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.

[0050] After the prediction module determines the target access mode, it can access the power factor prediction model through the target access mode. 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 in the target time period after the target time. Among them, 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 demand for reactive power of the power system and the stability of the system change in 90 days.

[0051] 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.

[0052] 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 pass 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 deviation threshold of the power factor. In particular, the threshold management server pre-stores a comparison table between the tag number of the target power load state and the deviation threshold of the power factor. The threshold management server determines the deviation threshold of the power factor by querying the comparison table according to the tag number of the target power load state, and feeds back the deviation threshold of the power factor.

[0053] 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.

[0054] After the control module obtains the predicted power factor change curve and the reference deviation threshold, it can calculate the adjustment strategy of the external power capacitor through the predicted power factor change curve, and control the input and output of the external power capacitor according to the adjustment strategy. Among them, in the adjustment strategy, the control module will add the reference deviation threshold for consideration while comparing the actual power factor change curve with the predicted power factor change curve, so as to guide the input and output of the external power capacitor. When the external power capacitor is put into operation and cut out, the deviation value between the actual power change curve and the predicted power factor change curve is less than the reference deviation threshold.

[0055] 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-mentioned specific embodiments.

[0056] An embodiment of the present application also discloses a computer program product, including a computer program or computer instructions, wherein the computer program or computer instructions 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.

[0057] 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 sequence. 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 be implemented in an order other than those illustrated or described herein, for example. In addition, the terms "including" and "having" and any of their variations 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 that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0058] It should be understood that in the present 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 objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single 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.

[0059] In the several embodiments provided in the present 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 only a logical function division. There may be other division methods in actual implementation, 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.

[0060] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0061] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0062] 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 is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0063] Although the description of the present application has been quite detailed and specifically describes several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, but should be regarded as providing a broad possible interpretation of these claims by reference to the attached 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 foreseeable by the inventor, and its purpose is 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 the current power load state of the power system according to the target time, and recording the power load state as the 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; The target time is input into the power factor prediction model through the target access mode, and a power factor change curve in a target time period after the target time is predicted through the power factor prediction model; the power factor change curve is recorded as a predicted power factor change curve; Acquire a preset power factor deviation threshold value according to the target power load state, and record the deviation threshold value as a reference deviation threshold value; 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.

2. The method for adjusting a regulator according to claim 1, characterized in that: Obtaining the access method of the target power factor prediction model specifically includes: passing the tag number of the target power load state to the 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.

3. The method for adjusting a regulator according to claim 1, characterized in that: 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.

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

5. 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 4.

6. 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 determination module is used to: determine the power load state of the current power system according to the target time, and record the power load state as the target power load state; 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 mode of the target power factor prediction model, and record the access mode as the target access mode; The prediction module is used to: input the target time into the power factor prediction model through the target access mode, predict the power factor change curve in 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; 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 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.

7. A regulating system for a regulator according to claim 6, characterized in that: Obtaining the access method of the target power factor prediction model specifically includes: passing the tag number of the target power load state to the 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.

8. The regulating system of a regulator according to claim 6, characterized in that: 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.

9. A regulating system for a regulator according to claim 6, characterized in that: The power factor prediction model is an artificial intelligence model built based on the long short-term memory network LSTM.

10. 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 adjustment method of the regulator as described in any one of claims 1 to 4.

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