A method, system and related devices for updating a parameter model of an energy storage device

Abstract

The present application relates to the technical field of energy storage, and discloses an energy storage device parameter model updating method, system and related device. The energy storage device parameter model updating method comprises: obtaining target parameters based on power grid environment information and parameter information, wherein the target parameters comprise power grid parameters and related abnormal parameters, the power grid parameters are parameters of a model of a current power grid environment obtained when the power grid environment changes, and the related abnormal parameters are abnormal parameters obtained when the power grid environment does not change and parameters are abnormal; collecting related data based on the target parameters to obtain collected data; filtering the collected data according to the data content of the target parameters to obtain target data corresponding to the target parameters; determining a corresponding updating strategy according to the type of the target data and the target parameters, and updating the corresponding energy storage device parameter model by using the updating strategy. The present application can solve the problem of the influence of the power grid environment on the prediction accuracy of the energy storage device parameter model, and improve the efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and specifically to a method, system, and related apparatus for updating parameter models of energy storage devices. Background Technology

[0002] As fossil fuels continue to deplete, new energy sources, especially photovoltaic energy, are becoming increasingly important worldwide. However, photovoltaic energy is easily affected by environmental factors such as sunlight, temperature, and humidity, making its trading and dispatch extremely challenging. Therefore, the participation of photovoltaic power plants in trading inevitably depends on the prediction of future photovoltaic power plant output.

[0003] With the continuous development of computer and artificial intelligence algorithms, the prediction of future photovoltaic power is realized through energy storage prediction models. In the existing technology, the model is usually trained with the prediction accuracy as the goal, and the energy storage equipment is monitored and regulated through the obtained energy storage equipment model.

[0004] When using existing technologies to make predictions with the goal of accuracy, changes in the power grid environment cannot be taken into account. This means that using fixed models for calculation and prediction often fails to maximize the benefits of photovoltaic energy storage equipment. Summary of the Invention

[0005] In view of this, the present invention provides a method, system and related apparatus for updating the parameter model of energy storage equipment, so as to solve the problem of the prediction accuracy of the parameter model of energy storage equipment affected by the power grid environment and improve efficiency.

[0006] In a first aspect, the present invention provides a method for updating a parameter model of an energy storage device. This method includes: acquiring target parameters based on grid environment information and parameter information; the target parameters include grid parameters and related abnormal parameters; the grid parameters are the parameters of the current grid environment model obtained when the grid environment changes, and the related abnormal parameters are the abnormal parameters obtained when the grid environment does not change and the parameters are abnormal; collecting relevant data based on the target parameters to obtain collected data, including historical and future collected data of the target parameters; filtering the collected data according to the data content of the target parameters to obtain target data corresponding to the target parameters; determining a corresponding update strategy based on the type of target data and target parameters, and updating the corresponding energy storage device parameter model using the update strategy.

[0007] In this implementation, different target parameters are collected depending on whether the power grid environment changes. When the power grid environment changes, all parameters are collected; when the power grid environment remains unchanged but parameter anomalies exist, only the abnormal parameters are collected. Historical and future parameters are determined based on the collected target parameters, and the energy storage device parameter model is updated according to parameter data changes. This approach considers parameter changes under different conditions, solves the problem of the power grid environment affecting the prediction accuracy of the energy storage device parameter model, and improves efficiency.

[0008] In one optional implementation, obtaining the target parameters based on power grid environment information and parameter information includes: detecting whether the power grid environment information has changed; when the power grid environment information changes, obtaining the power grid parameters of the current power grid environment; when the power grid environment information has not changed and the energy storage device parameter information is abnormal, obtaining the device-related abnormal parameters; when the power grid environment information has not changed and the power grid parameter information is abnormal, obtaining the power grid-related abnormal parameters.

[0009] In this implementation, different parameters are obtained for different power grid environments and different abnormal situations, which can cope with a variety of real-world problems and improve the accuracy of subsequent model updates.

[0010] In one optional implementation, relevant data is collected based on target parameters, and the collected data includes: collecting key parameters of the current energy storage device when the power grid environment information changes; comparing the key parameters with historical key parameters; when the key parameters change, obtaining the changes in the current power grid parameters and historical power grid parameters; and collecting relevant data based on the changes in the parameters to obtain the collected data.

[0011] In one optional implementation, relevant data is collected based on target parameters to obtain the collected data, including: when the power grid environment information has not changed and the energy storage device parameter information is abnormal, determining the corresponding power grid environment parameters based on the relevant abnormal parameters of the device; and collecting relevant data based on the power grid environment parameters to obtain the collected data.

[0012] In one optional implementation, relevant data is collected based on target parameters, and the collected data includes: when the power grid environment information does not change and the power grid parameter information is abnormal, the key parameters of the current energy storage device are collected; when the key parameters change within a first preset time period, the power grid parameters of the current power grid environment and the change parameters of historical power grid parameters are obtained; and relevant data is collected based on the change parameters to obtain the collected data.

[0013] In this implementation, different judgments are made and data is collected separately for different power grid environments and different abnormal situations, which can cope with a variety of real-world problems and improve the accuracy of subsequent model updates.

[0014] In one optional implementation, the collected data is filtered according to the data content of the target parameter to obtain the target data corresponding to the target parameter, including: when the power grid environment information changes, future collected data is used as the target data; when the power grid environment information does not change and the energy storage device parameter information is abnormal, the collected data is used as the target data; when the power grid environment information does not change and the power grid parameter information is abnormal, historical collected data is used as the target data.

[0015] In this implementation, different data are selected as target data to determine subsequent update strategies based on the characteristics of different power grid environments and different abnormal situations, which can improve data utilization.

[0016] In one optional implementation, determining the corresponding update strategy based on the type of target data and target parameters, and updating the corresponding energy storage device parameter model using the update strategy includes: obtaining the type of target parameters, including fixed values, regularly changing values, and irregularly changing values; when the target parameter is a fixed value, updating the parameter value to obtain the updated energy storage device parameter model; when the target parameter is a regularly changing value, updating the logical parameters in the parameter mapping relationship based on the new pattern to obtain the updated energy storage device parameter model; when the target parameter is an irregularly changing value, updating the probability model in the parameter mapping relationship based on the new probability value to obtain the updated energy storage device parameter model.

[0017] In this implementation, a corresponding update strategy is determined for different types of target parameters. The strategy that best suits the data characteristics can be selected, which can improve the accuracy of model updates and increase efficiency.

[0018] Secondly, this invention provides an energy storage device parameter model update system. This system includes a parameter monitoring module for acquiring target parameters based on grid environment information and parameter information. The target parameters include grid parameters and related abnormal parameters. Grid parameters are the parameters of the current grid environment model obtained when the grid environment changes. Related abnormal parameters are the abnormal parameters obtained when the grid environment remains unchanged but the parameters are abnormal. A parameter acquisition module is used to collect relevant data based on the target parameters, obtaining collected data including historical and future collected data of the target parameters. A parameter analysis module is used to filter the collected data according to the data content of the target parameters to obtain target data corresponding to the target parameters. A corresponding update strategy is determined based on the type of target data and target parameters. A model update module is used to update the corresponding energy storage device parameter model using the update strategy.

[0019] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the energy storage device parameter model update method of the first aspect or any corresponding embodiment described above.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the energy storage device parameter model update method of the first aspect or any corresponding embodiment thereof.

[0021] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause a computer to execute the energy storage device parameter model update method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of an energy storage device parameter model update system according to an embodiment of the present invention;

[0024] Figure 2 This is a flowchart of a method for updating the parameter model of an energy storage device according to an embodiment of the present invention;

[0025] Figure 3 This is a flowchart of another method for updating the parameter model of an energy storage device according to an embodiment of the present invention;

[0026] Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] According to an embodiment of the present invention, a parameter model update system for energy storage devices is provided. Please refer to [link / reference]. Figure 1 , Figure 1 This is a schematic diagram of a parameter model update system for an energy storage device according to an embodiment of the present invention. The energy storage device parameter model update system includes a parameter monitoring module, a parameter acquisition module, a parameter analysis module, and a parameter update module.

[0029] The parameter monitoring module is used to acquire target parameters based on power grid environment information and parameter information. Target parameters include power grid parameters and related abnormal parameters. Power grid parameters are the parameters of the current power grid environment model obtained when the power grid environment changes. Related abnormal parameters are the abnormal parameters obtained when the power grid environment has not changed but the parameters are abnormal. The parameter monitoring module is also used to send the target parameters to the parameter analysis module.

[0030] The target parameters are the grid parameters and environmental parameters used when constructing the parameter model of the energy storage device, such as voltage, current, and temperature.

[0031] Specifically, it detects whether the power grid environment information has changed, such as whether other power grids have been switched or whether the environment has changed.

[0032] In one implementation, when the power grid environment information changes, the parameter monitoring module detects and obtains all parameters related to the current power grid environment model in real time, thus acquiring the power grid parameters for the current power grid environment. These power grid parameters are then sent to the parameter analysis module.

[0033] In one implementation, when the power grid environment information remains unchanged, but one or more parameters of the energy storage device are abnormal, the parameter monitoring module detects and acquires the abnormal parameters related to the current device's model in real time, and sends these abnormal parameters to the parameter analysis module.

[0034] In one implementation, when the power grid environment information remains unchanged, but one or more power grid parameters are abnormal, the parameter monitoring module detects and acquires abnormal parameters related to the current power grid environment model in real time, obtaining relevant abnormal parameters. These abnormal parameters are then sent to the parameter analysis module.

[0035] In one implementation, the parameter monitoring module acquires the target parameter at preset intervals and sends the target parameter to the parameter analysis module.

[0036] In this implementation, different parameters are obtained for different power grid environments and different abnormal situations, which can cope with a variety of real-world problems and improve the accuracy of subsequent model updates.

[0037] The parameter analysis module performs comparative analysis on the received target parameters to determine if there are any parameter changes or anomalies. Based on the judgment result, it generates a data acquisition command and sends it to the parameter acquisition module. The parameter acquisition module collects historical and future data of the target parameters according to the acquisition command and sends the collected data to the parameter detection module.

[0038] In one implementation, when the power grid environment information changes, the key parameters of the current energy storage device are collected, and the key parameters are compared with historical key parameters. When the key parameters change, the changes in the current power grid parameters and historical power grid parameters are obtained, and relevant data are collected based on the changes in the parameters to obtain the collected data.

[0039] Specifically, when the power grid environment changes, the parameter analysis module receives the model-related power grid parameters sent by the parameter monitoring module and compares them one by one with the corresponding power grid parameters before the change to determine all the changed parameters. At the same time, it collects the key parameters of the current energy storage device, such as efficiency and storage capacity. If any key parameter changes, a collection command is generated based on the changed parameter information and sent to the parameter collection module; if no key parameter changes, no collection command is generated or sent.

[0040] In one implementation, when the power grid environment information remains unchanged but the energy storage device parameter information is abnormal, the corresponding power grid environment parameters are determined based on the relevant abnormal parameters of the device; relevant data are collected based on the power grid environment parameters to obtain the collected data.

[0041] Specifically, when the power grid environment information remains unchanged, but one or more of the energy storage device parameter information becomes abnormal, the parameter analysis module receives the device-related abnormal parameters of the energy storage device model sent by the parameter monitoring module, determines the power grid environment parameters corresponding to the device-related abnormal parameters based on the energy storage device model, and generates a collection command based on the power grid environment parameter information corresponding to the device-related abnormal parameters, and sends it to the parameter collection module.

[0042] In one implementation, when the power grid environment information remains unchanged but the power grid parameter information is abnormal, the key parameters of the current energy storage device are collected; when the key parameters change within a first preset time period, the changes in the current power grid parameters and historical power grid parameters are obtained; and relevant data are collected based on the changes in the parameters to obtain the collected data.

[0043] Specifically, when the power grid environment information remains unchanged, but one or more power grid parameters become abnormal, the parameter analysis module receives the power grid-related abnormal parameters of the energy storage device model sent by the parameter monitoring module, and monitors the key parameters of the energy storage device in real time for a certain period of time. If no changes are made within the specified time, no acquisition command is generated or sent; if changes are made within the specified time, the changed parameter information generates an acquisition command and sends it to the parameter acquisition module.

[0044] In this implementation, different judgments are made and data is collected separately for different power grid environments and different abnormal situations, which can cope with a variety of real-world problems and improve the accuracy of subsequent model updates.

[0045] The parameter acquisition module is used to collect relevant data based on the target parameters, obtaining the acquired data. This acquired data includes historical and future data for the target parameters.

[0046] Specifically, the acquisition command includes the target parameter, the historical data time range, and the future data time range. The parameter acquisition module obtains the historical acquisition data of the target parameter within the historical data time range based on the target parameter and the time range, and obtains the future acquisition data of the target parameter within the future data time range based on the target parameter and the time range.

[0047] The parameter analysis module is used to filter the collected data based on the data content of the target parameters to obtain the target data corresponding to the target parameters.

[0048] Specifically, the target parameters include grid parameters, equipment-related abnormal parameters, and grid-related abnormal parameters.

[0049] In one implementation, when the power grid environment changes and historical data on the power grid change parameters becomes unusable, future data is selected as the target data for subsequent analysis and model updates.

[0050] In one implementation, when the power grid environment information remains unchanged, but one or more of the energy storage device parameter information becomes abnormal, both historical and future data collected from the power grid environment parameters can be used. In this case, all collected data will be used as target data for subsequent analysis and model updates.

[0051] In one implementation, when the power grid environment information remains unchanged but the power grid parameter information is abnormal, and the future data collected for the power grid environment parameters is incorrect, historical data is selected as the target data for subsequent analysis and model updates.

[0052] In this implementation, different data are selected as target data to determine subsequent update strategies based on the characteristics of different power grid environments and different abnormal situations, which can improve data utilization.

[0053] The parameter analysis module is used to determine the corresponding update strategy based on the type of target data and target parameters.

[0054] Specifically, the types of target parameters include fixed values, regularly changing values, and irregularly changing values.

[0055] In one implementation, when the target parameter is a fixed value, the parameter value is updated to obtain the updated energy storage device parameter model.

[0056] Specifically, for parameter mappings corresponding to fixed values, the previous parameter mapping relationship remains unchanged; only the parameter values ​​are changed to obtain the corresponding energy storage parameters.

[0057] In one implementation, when the target parameter is a regularly changing value, the logical parameters in the parameter mapping relationship are updated based on the new pattern to obtain the updated energy storage device parameter model.

[0058] Specifically, for the parameter mapping corresponding to the regular change value, the logical coefficients in the parameter mapping relationship are updated based on the new regularity.

[0059] In one implementation, when the target parameter is an irregularly changing value, the probability model in the parameter mapping relationship is updated based on the new probability value to obtain the updated energy storage device parameter model.

[0060] Specifically, for parameter mappings corresponding to irregularly changing values, the probability model in the parameter mapping relationship is updated based on the new probability values.

[0061] In this implementation, a corresponding update strategy is determined for different types of target parameters. The strategy that best suits the data characteristics can be selected, which can improve the accuracy of model updates and increase efficiency.

[0062] The model update module is used to update the corresponding energy storage device parameter model using an update strategy.

[0063] Specifically, the model update module updates the energy storage parameter model according to the update strategy, thereby forming a new energy storage device parameter model. This can be done by updating only one type of parameter mapping information, or by updating two or all types of parameter mapping information simultaneously, resulting in an updated energy storage parameter model.

[0064] According to an embodiment of the present invention, an embodiment of a method for updating a parameter model of an energy storage device is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0065] This embodiment provides a method for updating the parameter model of an energy storage device, which can be used in the aforementioned energy storage device parameter model updating system. Figure 2 This is a flowchart of a parameter model update method for an energy storage device according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily reflect that result. Figure 2 The illustrated process sequence is limited. For example... Figure 2 As shown, the process includes the following steps:

[0066] Step S201: Obtain the target parameters based on the power grid environment information and parameter information.

[0067] The target parameters include power grid parameters and related abnormal parameters. Power grid parameters are the parameters of the current power grid environment model obtained when the power grid environment changes. Related abnormal parameters are the abnormal parameters obtained when the power grid environment does not change and the parameters are abnormal.

[0068] The target parameters are the grid parameters and environmental parameters used when constructing the parameter model of the energy storage device, such as voltage, current, and temperature.

[0069] Step S202: Collect relevant data based on the target parameters to obtain the collected data.

[0070] The collected data includes historical and future data for the target parameters.

[0071] Step S203: Filter the collected data according to the data content of the target parameters to obtain the target data corresponding to the target parameters.

[0072] Step S204: Determine the corresponding update strategy based on the type of target data and target parameters, and use the update strategy to update the corresponding energy storage device parameter model.

[0073] In this implementation, different target parameters are collected depending on whether the power grid environment changes. When the power grid environment changes, all parameters are collected; when the power grid environment remains unchanged but parameter anomalies exist, only the abnormal parameters are collected. Historical and future parameters are determined based on the collected target parameters, and the energy storage device parameter model is updated according to parameter data changes. This approach considers parameter changes under different conditions, solves the problem of the power grid environment affecting the prediction accuracy of the energy storage device parameter model, and improves efficiency.

[0074] This embodiment provides a method for updating the parameter model of an energy storage device, which can be used in the aforementioned energy storage device parameter model updating system. Figure 3 This is a flowchart of another method for updating the parameter model of an energy storage device according to an embodiment of the present invention. It should be noted that if substantially the same result is obtained, this embodiment does not necessarily follow the original method. Figure 3 The illustrated process sequence is limited. For example... Figure 3 As shown, the process includes the following steps:

[0075] Step S301: Obtain the target parameters based on the power grid environment information and parameter information.

[0076] Specifically, step S301 includes:

[0077] Step S3011: Detect whether the power grid environment has changed.

[0078] Specifically, whether to switch to another power grid or whether the environment changes.

[0079] Step S3012: When the power grid environment changes, obtain the power grid parameters of the current power grid environment.

[0080] Step S3013: When the power grid environment has not changed and the energy storage device parameters are abnormal, obtain the relevant abnormal parameters of the device.

[0081] Step S3014: When the power grid environment has not changed and the power grid parameters are abnormal, obtain the relevant abnormal parameters of the power grid.

[0082] Step S302: Collect relevant data based on the target parameters to obtain the collected data.

[0083] The collected data includes historical and future data for the target parameters.

[0084] Specifically, based on the target parameters and time range, historical data of the target parameters within the historical data time range is obtained, and future data of the target parameters within the future data time range is obtained, based on the target parameters and time range.

[0085] In one implementation, the power grid environment changes.

[0086] Specifically, step S302 includes:

[0087] Step S3021: Collect key parameters of the current energy storage device.

[0088] Step S3022: Compare the key parameters with historical key parameters. When the key parameters change, obtain the changes in the current power grid parameters and historical power grid parameters.

[0089] Step S3023: Collect relevant data based on the changing parameters to obtain the collected data.

[0090] In one implementation, the power grid environment remains unchanged, and the parameters of the energy storage device are abnormal.

[0091] Specifically, step S302 includes:

[0092] Step S3021: Determine the corresponding power grid environment parameters based on the relevant abnormal parameters of the equipment.

[0093] Step S3022: Collect relevant data based on network environment parameters to obtain the collected data.

[0094] In one implementation, the power grid environment remains unchanged, and the power grid parameters are abnormal.

[0095] Specifically, step S302 includes:

[0096] Step S3021: Collect key parameters of the current energy storage device.

[0097] Step S3022: When the key parameters change within the first preset time period, obtain the change parameters of the current power grid environment and the historical power grid parameters.

[0098] Step S3023: Collect relevant data based on the changing parameters to obtain the collected data.

[0099] Step S303: Filter the collected data according to the data content of the target parameters to obtain the target data corresponding to the target parameters.

[0100] In one implementation, future data collection is used as the target data when the power grid environment changes.

[0101] In one implementation, when the power grid environment remains unchanged and the energy storage device parameters are abnormal, the collected data is used as the target data.

[0102] In one implementation, when the power grid environment remains unchanged but the power grid parameters are abnormal, historically collected data is used as the target data.

[0103] Step S304: Determine the corresponding update strategy based on the type of target data and target parameters, and use the update strategy to update the corresponding energy storage device parameter model.

[0104] Specifically, step S304 includes:

[0105] Step S3041: Obtain the type of the target parameter.

[0106] Step S3042: Determine the corresponding update strategy based on the type of the target parameter, and use the update strategy to update the corresponding energy storage device parameter model.

[0107] When the target parameter is a fixed value, update the parameter value to obtain the updated energy storage device parameter model;

[0108] When the target parameter is a regularly changing value, the logical parameters in the parameter mapping relationship are updated based on the new pattern to obtain the updated energy storage device parameter model;

[0109] When the target parameter is an irregularly changing value, the probability model in the parameter mapping relationship is updated based on the new probability value to obtain the updated energy storage device parameter model.

[0110] In this embodiment, the energy storage device parameter model update system is presented in the form of functional units. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0111] This invention also provides a computer device having the above-described features. Figure 1 The energy storage device parameter model update system shown.

[0112] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0113] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0114] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0115] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0116] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0117] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0118] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0119] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0120] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0121] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for updating parameter models of energy storage devices, characterized in that, The method includes: Based on power grid environment information and parameter information, target parameters are obtained. These target parameters are the power grid parameters and environmental parameters used when constructing the parameter model of the energy storage device. Based on the target parameters, relevant data are collected to obtain collected data, which includes historical and future collected data of the target parameters. The collected data is filtered according to the data content of the target parameter to obtain the target data corresponding to the target parameter; The corresponding update strategy is determined based on the target data and the type of the target parameters, and the corresponding energy storage device parameter model is updated using the update strategy. The process of obtaining target parameters based on power grid environment information and parameter information includes: detecting whether the power grid environment information has changed; when the power grid environment information changes, obtaining the power grid parameters of the current power grid environment; when the power grid environment information has not changed, but the energy storage device parameter information is abnormal, obtaining the device-related abnormal parameters; and when the power grid environment information has not changed, but the power grid parameter information is abnormal, obtaining the power grid-related abnormal parameters.

2. The method for updating the parameter model of an energy storage device according to claim 1, characterized in that, The data collected based on the target parameters includes: When the power grid environment information changes, collect key parameters of the current energy storage device; The key parameters are compared with historical key parameters. When the key parameters change, the changes in the current power grid parameters and historical power grid parameters are obtained. Based on the changing parameters, relevant data are collected to obtain the collected data.

3. The method for updating the parameter model of an energy storage device according to claim 1, characterized in that, The data collected based on the target parameters includes: When the power grid environment information remains unchanged, but the energy storage device parameter information is abnormal, the corresponding power grid environment parameters are determined based on the relevant abnormal parameters of the device. Based on the aforementioned power grid environmental parameters, relevant data are collected to obtain the collected data.

4. The method for updating the parameter model of an energy storage device according to claim 1, characterized in that, The data collected based on the target parameters includes: When the power grid environment information remains unchanged, but the power grid parameter information is abnormal, collect the key parameters of the current energy storage device; When the key parameter changes within a first preset time period, the change parameters of the current power grid environment and the historical power grid parameters are obtained; Based on the changing parameters, relevant data are collected to obtain the collected data.

5. The method for updating the parameter model of an energy storage device according to claim 1, characterized in that, The step of filtering the collected data according to the data content of the target parameter to obtain the target data corresponding to the target parameter includes: When the power grid environment information changes, the future collected data will be used as the target data; When the power grid environment information remains unchanged and the energy storage device parameter information is abnormal, the collected data will be used as the target data. When the power grid environment information remains unchanged, but the power grid parameter information is abnormal, the historically collected data will be used as the target data.

6. The method for updating the parameter model of an energy storage device according to claim 1, characterized in that, The step of determining the corresponding update strategy based on the target data and the type of the target parameters, and updating the corresponding energy storage device parameter model using the update strategy, includes: Obtain the type of the target parameter, which includes fixed value, regularly changing value, and irregularly changing value; When the target parameter is the fixed value, the parameter value is updated to obtain the updated energy storage device parameter model; When the target parameter is the value of the regular change, the logical parameters in the parameter mapping relationship are updated based on the new regularity to obtain the updated energy storage device parameter model; When the target parameter is the irregularly changing value, the probability model in the parameter mapping relationship is updated based on the new probability value to obtain the updated energy storage device parameter model.

7. A parameter model update system for energy storage devices, characterized in that, The system includes: The parameter monitoring module is used to acquire target parameters based on power grid environmental information and parameter information. These target parameters are the power grid parameters and environmental parameters used when constructing the energy storage device parameter model. Acquiring the target parameters based on the power grid environmental information and parameter information includes: detecting whether the power grid environmental information has changed; when the power grid environmental information has changed, acquiring the current power grid parameters; when the power grid environmental information has not changed but the energy storage device parameter information is abnormal, acquiring relevant abnormal parameters of the device; and when the power grid environmental information has not changed but the power grid parameter information is abnormal, acquiring relevant abnormal parameters of the power grid. The parameter acquisition module is used to collect relevant data based on the target parameter to obtain the acquired data, which includes historical and future acquired data of the target parameter. The parameter analysis module is used to filter the collected data according to the data content of the target parameter to obtain the target data corresponding to the target parameter; and to determine the corresponding update strategy according to the type of the target data and the target parameter. The model update module is used to update the corresponding energy storage device parameter model using the update strategy.

8. A computer device, characterized in that, include: A memory and a processor are interconnected, the memory stores computer instructions, and the processor executes the computer instructions to perform the energy storage device parameter model update method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the energy storage device parameter model update method according to any one of claims 1 to 6.

Images