Energy storage battery remote awakening method, awakening system, medium and program product

Abstract

The invention relates to the technical field of remote wakeup, in particular to an energy storage battery remote wakeup method, wakeup system, medium and program product, the method comprises the following steps: obtaining power grid operation parameter information, the power grid operation parameter information comprising operation voltage and operation current; the power grid operation parameters are imported into a preset power grid load prediction model to obtain predicted operation parameter information, a predicted power utilization peak period is determined based on the predicted operation parameter information and preset standard operation parameter information, and the predicted operation parameter information comprises predicted operation voltage and predicted operation current; obtaining an operation state of the energy storage battery, and when the operation state is a dormant state, determining wake-up working parameter information based on the predicted operation parameter information and preset standard operation parameter information; and generating a wake-up instruction based on the predicted power consumption peak period and the wake-up working parameter information, and waking up the energy storage battery based on the wake-up instruction. According to the invention, the remote wakeup efficiency of the energy storage battery can be improved.

Description

Technical Field

[0001] The present application relates to the field of remote wake-up technology, and in particular to a remote wake-up method, wake-up system, medium and program product for an energy storage battery. Background Art

[0002] With the growth of global energy demand and the improvement of environmental awareness, the photovoltaic storage system, as a system that utilizes solar energy and energy storage technology, is gaining increasing attention. The photovoltaic storage system consists of photovoltaic equipment and energy storage battery equipment, and is mainly used for power generation and energy storage. Among them, photovoltaic equipment converts solar energy into electrical energy through solar photovoltaic panels, while energy storage battery equipment is used to store excess electrical energy for use when needed. The photovoltaic storage system can not only improve energy utilization efficiency, but also enhance the stability and reliability of the power supply process.

[0003] In the related art, the photovoltaic storage system mostly relies on local trigger conditions to wake up the energy storage battery in a dormant state, such as manual start, etc. This wake-up method requires a long time of manual intervention and cannot achieve a quick response, so it may not be able to respond to changes in power demand in real time. Summary of the invention

[0004] In order to improve the efficiency of remotely waking up an energy storage battery, the present application provides a remote waking up method, a waking up system, a medium and a program product for an energy storage battery.

[0005] In a first aspect, the present application provides a method for remotely waking up an energy storage battery, which adopts the following technical solution: A method for remotely waking up an energy storage battery, comprising: Acquiring power grid operation parameter information, wherein the power grid operation parameter information includes operation voltage and operation current; Importing the power grid operation parameters into a preset power grid load forecasting model to obtain forecast operation parameter information, and determining a forecast peak power consumption period based on the forecast operation parameter information and preset standard operation parameter information, wherein the forecast operation parameter information corresponding to the forecast peak power consumption period is higher than the preset standard operation parameter information, and the forecast operation parameter information includes a forecast operation voltage and a forecast operation current; Acquire the operating state of the energy storage battery, and when the operating state is a dormant state, determine the awakening working parameter information based on the predicted operating parameter information and the preset standard operating parameter information; A wake-up instruction is generated based on the predicted peak power consumption period and the wake-up working parameter information, and the energy storage battery is awakened based on the wake-up instruction.

[0006] By adopting the above technical scheme, by analyzing the grid operation parameter information, it is convenient to understand the real-time operation status of the grid. In addition, the preset grid load prediction model can be used to predict and simulate the operation status of the grid in a period of time in the future based on the grid operation parameter information, so as to timely discover the operation time period with high load hidden dangers, so as to facilitate timely intervention to reduce the impact or loss caused by high load hidden dangers. After determining the operation time period with high load hidden dangers, the energy storage battery in the dormant state is awakened based on the preset standard operation parameter information, so as to improve the immediacy and accuracy of the awakening process, thereby facilitating the improvement of the efficiency of remote awakening of the energy storage battery.

[0007] In a possible implementation manner, after generating the wake-up instruction based on the predicted peak power consumption period and the wake-up working parameter information, the method further includes: Acquire historical execution parameter information of the energy storage battery within a first preset time period, wherein the historical execution parameter information includes average execution voltage, charge and discharge times, average execution temperature, and average execution humidity; Importing the historical execution parameter information into a preset health simulation battery model corresponding to the energy storage battery to obtain health simulation value information, wherein the health simulation value information includes a SOH value and a SOC value; Determine the target optimization strategy corresponding to the energy storage battery based on the mapping relationship between the health simulation value information and the preset optimization strategy, wherein the preset optimization strategy mapping relationship includes target optimization strategies corresponding to different health simulation value information, and the target optimization strategy includes wake-up frequency and wake-up duration; The energy storage battery is awakened based on the target optimization strategy.

[0008] By adopting the above technical solution, by analyzing the historical execution parameter information of the energy storage battery, it is convenient to understand the working status and performance of the energy storage battery in the first preset time period. Since the average execution temperature and the average execution humidity will affect the capacity of the energy storage battery, it is also necessary to analyze the average execution temperature and the average execution humidity of the energy storage battery in the past period of time when performing a health assessment on the energy storage battery. Finally, the energy storage battery is awakened according to the health assessment result to ensure that the energy storage battery works under suitable conditions, thereby extending the service life of the energy storage battery.

[0009] In a possible implementation manner, the determining the wake-up working parameter information based on the predicted operating parameter information and the preset standard operating parameter information includes: Determining wake-up execution parameter information based on the predicted operation parameter information and the preset standard operation parameter information, wherein the wake-up execution parameter information includes a wake-up execution voltage and a wake-up execution current; Acquire startup operation parameter information of the energy storage battery, and determine the wake-up duration based on the startup operation parameter information and the wake-up execution parameter information, wherein the startup operation parameter information includes a discharge rate, a battery capacity, and a battery aging degree; A wake-up time is determined based on the predicted peak power consumption period and the wake-up duration, and wake-up work parameter information is determined based on the wake-up time and the wake-up execution parameter information.

[0010] By adopting the above technical solution, the wake-up time required for the energy storage battery to switch from a sleep state to a wake-up working state is predicted based on the discharge rate, battery capacity and battery aging degree of the energy storage battery before the predicted peak power consumption period arrives, so as to improve the accuracy of determining the wake-up time. In addition, the wake-up time of the energy storage battery is determined based on the wake-up time and the predicted peak power consumption period, and the energy storage battery is awakened in advance based on the wake-up time, so that the energy storage battery can reach the best working state during the predicted peak power consumption period, thereby facilitating the provision of more stable power support.

[0011] In one possible implementation, the method further includes: Obtaining the wake-up instructions and corresponding historical work log information generated within the second preset time period, and identifying the hidden danger electrical equipment corresponding to the predicted peak power consumption period of each wake-up instruction from the historical work log information; Integrate the hidden danger power-consuming equipment corresponding to each predicted peak power consumption period and the continuous working time of each hidden danger power-consuming equipment, and determine the target power-consuming equipment from multiple hidden danger power-consuming equipment, wherein the target power-consuming equipment is the hidden danger power-consuming equipment whose occurrence frequency in the second preset time period is higher than the preset occurrence frequency, or the hidden danger power-consuming equipment whose continuous working time is higher than the preset working time; A directional wake-up instruction is generated based on the continuous working time corresponding to the target electrical device, and the directional wake-up instruction is used to wake up the energy storage battery in the next working cycle of the target electrical device.

[0012] By adopting the above technical solution, by analyzing the wake-up instructions generated within the second preset time period, it is convenient to determine the target electrical equipment that needs attention from multiple hidden danger electrical equipment, and determine the directional wake-up instructions corresponding to the target electrical equipment based on the continuous working time of the target electrical equipment, so as to wake up the energy storage battery in advance before the high load of the power grid is caused by the use of the target electrical equipment in the next working cycle, thereby reducing the loss or damage to the power grid caused by the use of the target electrical equipment.

[0013] In one possible implementation, the method further includes: When a preset condition is met, adjusting the operating state of the energy storage battery to a deep sleep state; When it is detected that the operating state of the energy storage battery is a deep sleep state, a hardware shutdown instruction is generated, wherein the hardware shutdown instruction is used to close the connection between the energy storage battery and all hardware devices except the RTC real-time clock; The pre-conditions include: The power grid operation parameter information includes a preset fault identifier; or, The power grid operation parameter information is lower than the preset basic operation parameter information, and the duration for which the power grid operation parameter information is lower than the preset basic operation parameter information is longer than the preset basic duration.

[0014] By adopting the above technical solution, when the preset fault mark is included in the grid operation parameter information, the operation state of the energy storage battery is quickly adjusted to a deep sleep state, which helps to avoid damage or safety risks caused by the energy storage battery continuing to operate under grid fault conditions. In addition, when the grid parameter information is lower than the preset basic operation parameter and the duration exceeds the preset basic time, the energy storage battery is placed in a deep sleep state, which can prevent the energy storage battery from operating for a long time under low parameter conditions, thereby protecting the performance of the energy storage battery. Finally, by disconnecting the energy storage battery in the deep sleep state, the energy consumption of the energy storage battery during standby can be reduced.

[0015] In one possible implementation, it also includes: Based on the operating state of the energy storage battery and the preset diagnostic strategy mapping relationship, determine the target diagnostic strategy corresponding to the energy storage battery, the preset diagnostic strategy mapping relationship is the corresponding relationship between the operating state and the target diagnostic strategy, and the target diagnostic strategy includes the diagnostic duration and the diagnostic frequency; The energy storage battery is diagnosed based on the target diagnosis strategy. When an abnormal position appears in the battery diagnosis result, a target substitute position is determined from substitute batteries based on the abnormal position, and battery warning information is generated based on the abnormal position and the target substitute position.

[0016] By adopting the above technical solution and setting different diagnostic strategies for energy storage batteries in different operating states, it is convenient to improve the compatibility between the diagnostic strategy and the energy storage battery, thereby facilitating the improvement of the accuracy of the diagnostic result. When there is an abnormality in the battery diagnostic result, the corresponding target substitute position is promptly determined from the substitute battery based on the abnormality, and by generating battery early warning information, it is convenient for relevant staff to respond to and handle the abnormality found during the diagnosis process in a timely manner, thereby facilitating the reduction of the impact of the abnormal position on the normal operation of the energy storage battery.

[0017] In a second aspect, the present application provides a wake-up system, which adopts the following technical solution: A wake-up system, the wake-up system comprising: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute the above-mentioned energy storage battery remote wake-up method.

[0018] In a third aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: a computer program that can be loaded by a processor and execute the above-mentioned energy storage battery remote wake-up method.

[0019] In a fourth aspect, the present application provides a computer program product, which adopts the following technical solution: A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the above-mentioned energy storage battery remote wake-up method is implemented.

[0020] In summary, the present application includes at least one of the following beneficial technical effects: By analyzing the grid operation parameter information, it is convenient to understand the real-time operation status of the grid. In addition, the preset grid load prediction model can be used to predict and simulate the operation status of the grid in the future based on the grid operation parameter information, so as to timely discover the operation time period with high load hidden dangers, so as to facilitate timely intervention to reduce the impact or loss caused by high load hidden dangers. After determining the operation time period with high load hidden dangers, the energy storage battery in the dormant state is awakened based on the preset standard operation parameter information, so as to improve the immediacy and accuracy of the awakening process, thereby improving the efficiency of remote awakening of the energy storage battery.

[0021] By analyzing the historical execution parameter information of the energy storage battery, it is convenient to understand the working status and performance of the energy storage battery in the first preset time period. Since the average execution temperature and the average execution humidity will affect the capacity of the energy storage battery, it is also necessary to analyze the average execution temperature and the average execution humidity of the energy storage battery in the past period of time when performing a health assessment on the energy storage battery. Finally, the energy storage battery is awakened according to the health assessment results to ensure that the energy storage battery works under suitable conditions, thereby extending the service life of the energy storage battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a method for remotely waking up an energy storage battery in an embodiment of the present application; Figure 2 It is a flow chart of a target optimization strategy for determining an embodiment of the present application; Figure 3It is a structural diagram of a wake-up system in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The following is combined with Figures 1 to 3 This application is described in further detail.

[0024] After reading this specification, those skilled in the art may make non-creative modifications to this embodiment as needed, but such modifications are protected by patent law as long as they are within the scope of the claims of this application.

[0025] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] It should be noted that in the optional embodiments of the present application, the object information and other related data involved, when the embodiments in the present application are applied to specific products or technologies, need to obtain the permission or consent of the object, and the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions. In other words, if the embodiments of the present application involve data related to the object, it needs to be obtained with the authorization and consent of the object, the authorization and consent of the relevant departments, and in compliance with the relevant laws, regulations and standards of the country and region. If personal information is involved in the embodiments, the acquisition of all personal information needs to obtain the consent of the individual. If sensitive information is involved, the separate consent of the information subject needs to be obtained. The embodiments also need to be implemented with the authorization and consent of the object.

[0027] Specifically, the embodiment of the present application provides a method for remotely waking up a storage battery, which is executed by a wake-up system, and the wake-up system can be a server or a terminal device, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal device can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The terminal device and the server can be directly or indirectly connected via wired or wireless communication, and the embodiment of the present application does not limit this.

[0028] refer to Figure 1 , Figure 1 1 is a flow chart of a method for remotely waking up an energy storage battery in an embodiment of the present application. The method includes steps S110-S140, wherein: Step S110: Acquire grid operation parameter information, where the grid operation parameter information includes operation voltage and operation current.

[0029] Specifically, the grid operation parameter information is used to characterize the state of the grid in the process of supplying power to electrical equipment. By analyzing the grid operation parameter information, it is convenient to determine whether the grid is in a high-load state. Among them, the operating voltage is one of the most basic parameters in the grid system, which is used to characterize the magnitude of the potential energy in the grid. The stability of the operating voltage is crucial to the reliability and safety of the grid system. Excessive operating voltage may affect the normal operation of the circuit. In addition, the operating current is also an extremely important operating parameter in the grid system. It is the physical manifestation of the movement of electric energy in the grid. The operating current determines the amount of electric energy that can be transmitted in the circuit and is directly related to the transmission capacity of electric energy. Therefore, when judging whether it is necessary to wake up the energy storage battery, it is necessary to analyze the operating voltage and operating current in the grid operation parameter information. Among them, the grid operation parameter information can also include operating power, operating resistance, and operating temperature, which can be adjusted according to actual needs.

[0030] Among them, energy storage batteries are batteries that convert electrical energy into chemical energy and store it, and then release the stored chemical energy into electrical energy when needed. They are an important energy storage and scheduling tool, and play an important role in improving grid stability and reducing carbon emissions.

[0031] Step S120: Import the grid operation parameters into the preset grid load forecasting model to obtain predicted operation parameter information, and determine the predicted peak power consumption period based on the predicted operation parameter information and the preset standard operation parameter information. The predicted operation parameter information corresponding to the predicted peak power consumption period is higher than the preset standard operation parameter information. The predicted operation parameter information includes predicted operation voltage and predicted operation current.

[0032] Specifically, the preset power grid load prediction model is used to simulate and predict the operating status of the current power grid. Different power grid parameters correspond to different preset power grid load prediction models. The preset power grid load prediction model can be obtained by relevant staff after training based on historical operating parameters and uploaded to the wake-up system. The training process of the preset power grid load prediction model may include: obtaining historical operating parameters, controlling the prediction model to be trained to adaptively learn the laws and connections between historical operating parameters, and then using preset evaluation indicators to evaluate the prediction model in the training process until the loss value corresponding to the evaluation result is lower than the preset loss value, wherein the historical operating parameters may be historical power supply data, historical power supply temperature, and historical power supply load, etc. The prediction model to be trained may be a neural network model, a time series model, or a support vector machine model, etc. The specific prediction model to be trained is not specifically limited in the embodiment of the present application, and can be set by relevant technical personnel according to actual needs. The preset evaluation indicator may be a root mean square error RMSE, a mean absolute percentage error MAPE, etc. The specific evaluation indicator is not specifically limited in the embodiment of the present application, as long as the training result of the prediction model can be evaluated.

[0033] The specific preset power grid load prediction model is not specifically limited in the embodiment of the present application, as long as the operating state of the power grid in the future period of time can be predicted. The predicted operating parameter information includes but is not limited to the predicted operating voltage and predicted operating current that characterize the operating state of the power grid in the future period of time. The preset standard operating parameter information corresponds to the predicted operating parameter information, and the preset standard operating parameter information includes the preset standard operating voltage and the preset standard operating current. After determining the predicted operating parameter information, the predicted operating voltage in the predicted operating parameter information is compared with the preset standard operating voltage, and the predicted operating current is compared with the preset standard operating current. When the predicted operating voltage corresponding to a certain time period is higher than the preset standard operating voltage, and the predicted operating current corresponding to the time period is higher than the preset standard operating current, the time period is determined as the predicted peak power consumption period, and the duration corresponding to the predicted peak power consumption period is higher than the preset duration, wherein the preset duration can be 2 minutes or 5 minutes, and the specific preset duration is not specifically limited in the embodiment of the present application. By limiting the duration corresponding to the predicted peak power consumption period, it is convenient to reduce the island abnormal data, thereby facilitating the improvement of the accuracy of the prediction results. The specific preset standard operating parameter information can be determined by relevant staff based on historical experimental data and uploaded to the wake-up system. The specific content is not specifically limited in the embodiments of this application.

[0034] Step S130: Acquire the operating state of the energy storage battery, and when the operating state is a dormant state, determine the awakening working parameter information based on the predicted operating parameter information and the preset standard operating parameter information.

[0035] Specifically, when it is determined that there may be peak periods of electricity consumption during the operation of the power grid in the future, it is necessary to control the energy storage battery and the power grid to jointly supply power to the power consumption equipment. Prior to this, it is necessary to first detect the operating status of the energy storage battery. When the energy storage battery is in a non-dormant state, the energy storage battery can be directly controlled to supply power to the power consumption equipment; when the energy storage battery is in a dormant state, it needs to be awakened first, and then the energy storage battery is controlled to supply power to the power consumption equipment.

[0036] When the energy storage battery is awakened, the awakening working parameter information of the energy storage battery after awakening can be determined, so that the energy storage battery can work directly according to the awakening working parameter information after being awakened, without sending the control instruction again to adjust or set the working parameters of the energy storage battery. The awakening working parameter information includes but is not limited to the awakening working voltage and the awakening working current. When the energy storage battery supplies power to the electrical equipment according to the awakening working parameter information, it is necessary to make the predicted operating parameter information not higher than the preset standard parameter information. Therefore, the awakening working parameter information can be determined based on the predicted operating parameter information and the preset standard operating parameter information.

[0037] Furthermore, the specific manner of determining the wake-up working parameter information based on the predicted operating parameter information and the preset standard operating parameter information may include: Based on the predicted operating parameter information and the preset standard operating parameter information, the wake-up execution parameter information is determined, and the wake-up execution parameter information includes the wake-up execution voltage and the wake-up execution current; the startup operating parameter information of the energy storage battery is obtained, and the wake-up duration is determined based on the startup operating parameter information and the wake-up execution parameter information, and the startup operating parameter information includes the discharge rate, the battery capacity and the battery aging degree; the wake-up time is determined based on the predicted peak power consumption period and the wake-up duration, and the wake-up working parameter information is determined based on the wake-up time and the wake-up execution parameter information.

[0038] Specifically, since the purpose of waking up the energy storage battery is to control the energy storage battery and the power grid to jointly supply power to the electrical equipment so that the predicted operating parameter information is not higher than the preset standard operating parameter information, it is necessary to first determine the wake-up execution parameter information of the energy storage battery based on the predicted operating parameter information and the preset standard operating parameter information, that is, after controlling the energy storage battery to be connected to the power supply network according to the wake-up execution voltage and the wake-up execution current, the load pressure of the power grid can be reduced so that the predicted operating parameter information is not higher than the preset standard operating parameter information. However, since it takes a certain amount of time to wake up the energy storage battery, and when the energy storage battery is connected to the power grid, if it immediately works according to the wake-up execution voltage and the wake-up execution current, it may cause an impact on the power grid. Therefore, it is necessary to predict the time required for the energy storage battery to adjust from the current state to the working state corresponding to the wake-up execution parameter information based on the startup operating parameter information of the energy storage battery, that is, the wake-up time. When determining the wake-up duration based on the startup operation parameter information and the wake-up execution parameter information, the battery model corresponding to the energy storage battery can be optimized based on the startup operation parameter information to obtain an optimized battery model, and then the optimized battery model is driven based on the wake-up execution parameter information as a limiting condition to obtain the drive execution parameter information. When the drive execution parameter information reaches the wake-up execution parameter information, the drive duration is obtained, and the drive duration is determined as the wake-up duration. The method for determining the wake-up duration is not specifically limited in the embodiment of the present application.

[0039] Since the discharge rate, battery capacity and battery aging degree of the energy storage battery will affect the charge and discharge performance of the energy storage battery, the battery model can be optimized according to the discharge rate, battery capacity and battery aging degree. The specific content of the startup operation parameter information is not specifically limited in the embodiment of the present application and can be set by relevant technical personnel according to actual needs.

[0040] After determining the wake-up duration, the wake-up time is determined based on the predicted peak power consumption period and the wake-up duration, and the energy storage battery is awakened at the wake-up time to ensure that the energy storage battery can operate according to the wake-up execution voltage and the wake-up execution current during the predicted peak power consumption period, thereby facilitating the provision of more stable power support. For example, the predicted peak power consumption period is 10:10-10:30, and the wake-up duration is 2 minutes. At this time, the wake-up time can be determined to be 9:58, that is, the energy storage battery is awakened at 9:58, and after a wake-up time of 2 minutes, it can operate according to the wake-up execution voltage and the wake-up execution current on time at 10:10.

[0041] Step S140: Generate a wake-up instruction based on the predicted peak power consumption period and the wake-up working parameter information, and wake up the energy storage battery based on the wake-up instruction.

[0042] Specifically, the generated wake-up instruction can be transmitted to the energy storage battery by the wireless communication module included in the wake-up system to control the energy storage battery to work according to the wake-up working parameter information during the predicted peak power consumption period.

[0043] For the embodiments of the present application, by analyzing the grid operation parameter information, it is convenient to understand the real-time operation status of the grid. In addition, the preset grid load prediction model can be used to predict and simulate the operation status of the grid in a period of time in the future based on the grid operation parameter information, so as to timely discover the operation time period with high load hidden dangers, so as to facilitate timely intervention to reduce the impact or loss caused by high load hidden dangers. After determining the operation time period with high load hidden dangers, the energy storage battery in the dormant state is awakened based on the preset standard operation parameter information, so as to improve the immediacy and accuracy of the awakening process, thereby facilitating the improvement of the efficiency of remote awakening of the energy storage battery.

[0044] Furthermore, since the health status of the energy storage battery may affect its working parameters, after generating the wake-up instruction based on the predicted peak power consumption period and the wake-up working parameter information, the method provided in the embodiment of the present application further includes steps S1 to S4, such as Figure 2 As shown, where: Step S1: Acquire historical execution parameter information of the energy storage battery within a first preset time period, the historical execution parameter information including average execution voltage, charge and discharge times, average execution temperature and average execution humidity.

[0045] Specifically, the first preset time period is a period of time before the current moment, and the duration corresponding to the first preset time period can be 20 hours or 30 hours. The specific duration is not specifically limited in the embodiments of the present application. The historical execution parameter is the operating data of the energy storage battery in the first preset time period, wherein the average execution voltage is used to reflect the voltage state of the energy storage battery when it is running in the first preset time period. By analyzing the average execution voltage, it is convenient to evaluate whether the energy storage battery is in a normal working state in the first preset time period. The number of charge and discharge times is an important indicator for measuring the life and performance stability of the energy storage battery. By analyzing the number of charge and discharge times of the energy storage battery in the first preset time period, its service life and performance stability can be evaluated. The average execution temperature has an important impact on the performance and life of the energy storage battery. Generally, the execution temperature of the energy storage battery for normal operation should be controlled between 20℃-35℃. When the execution temperature is higher than 35℃, the chemical reaction inside the energy storage battery will accelerate, which may shorten the life of the energy storage battery. When the execution temperature is lower than 20℃, the chemical reaction speed inside the energy storage battery will slow down, and the energy output of the energy storage battery will also weaken. Therefore, by analyzing the average execution temperature of the energy storage battery in the first preset time period, it is helpful to understand whether its working environment is suitable, and whether there are problems such as performance degradation or shortened life due to improper execution temperature. The average execution temperature is another important factor affecting the performance of energy storage batteries. The normal humidity range of energy storage batteries is usually between 45% and 65%. The execution humidity within this range can ensure the normal chemical reaction inside the energy storage battery, while preventing corrosion or water condensation inside the energy storage battery. When the execution humidity is too high or too low, it may have an adverse effect on the performance of the energy storage battery, such as causing corrosion inside the energy storage battery or affecting the charge and discharge performance of the energy storage battery. Therefore, analyzing the average execution humidity of the energy storage battery helps to understand whether it is necessary to take corresponding humidity control measures to ensure the normal operation of the energy storage battery.

[0046] Step S2: Import the historical execution parameter information into the preset health simulation battery model corresponding to the energy storage battery to obtain health simulation value information, where the health simulation value information includes SOH value and SOC value.

[0047] Specifically, different energy storage batteries correspond to different preset health simulation battery models. Based on the preset health simulation battery model corresponding to the energy storage battery, the current health status of the energy storage battery can be analyzed or predicted, wherein the preset health simulation battery model can be obtained by training the LSTM neural network model using the deep learning framework TensorFlow. The specific preset health simulation battery model is not specifically limited in the embodiments of the present application. The health simulation value information is used to feedback the health status of the energy storage battery, wherein the SOH value (State of Health) is used to characterize the ability of the energy storage battery to store electrical energy relative to a new battery, that is, the ratio between the full-charge energy of the energy storage battery and the full-charge energy of a new battery. It is used to evaluate the performance aging and remaining life of the energy storage battery, and is often marked in the form of a percentage. For example, the SOH value of a new battery is usually 100%. As the energy storage battery is used and aged, the SOH value will gradually decrease. The SOC value (State of Charge) is used to characterize the charging state and charging rate of the energy storage battery, that is, the available state of the remaining power in the energy storage battery, usually expressed as a percentage. When SOC=0%, it means that the energy storage battery is fully discharged; when SOC=100%, it means that the energy storage battery is fully charged.

[0048] Step S3: Determine the target optimization strategy corresponding to the energy storage battery based on the mapping relationship between the health simulation value information and the preset optimization strategy. The preset optimization strategy mapping relationship includes target optimization strategies corresponding to different health simulation value information. The target optimization strategy includes wake-up frequency and wake-up duration.

[0049] Step S4: Wake up the energy storage battery based on the target optimization strategy.

[0050] Specifically, when determining the target optimization strategy corresponding to the energy storage battery, the total health value of the energy storage battery can be determined first based on the health simulation value information. The total health value is the sum of the SOH value and the SOC value. Different total health values ​​correspond to different target optimization strategies. The target optimization strategy corresponding to any total health value can be determined based on the preset optimization strategy mapping relationship. The higher the total health value, the higher the wake-up frequency and the longer the wake-up time in the corresponding target optimization strategy. The specific content of the preset optimization strategy mapping relationship is not specifically limited in the embodiment of the present application, and can be determined by relevant staff based on historical experimental data and uploaded to the wake-up system. Finally, the energy storage battery is awakened according to the health assessment results to ensure that the energy storage battery works under suitable conditions, thereby extending the service life of the energy storage battery.

[0051] Furthermore, the method provided in the embodiment of the present application also includes: The wake-up instructions generated within the second preset time period and the corresponding historical work log information are obtained, and the hidden danger electrical equipment corresponding to the predicted peak power consumption period of each wake-up instruction is identified from the historical work log information.

[0052] Specifically, the second preset time period is a period of time after the current moment, and the duration corresponding to the second preset time period can be 24 hours or 48 hours. The specific duration is not specifically limited in the embodiment of the present application. The historical work log information includes the actual power-consuming equipment corresponding to each wake-up instruction corresponding to the predicted peak power consumption period, that is, the power-consuming equipment with hidden dangers. The number of power-consuming equipment with hidden dangers corresponding to each wake-up instruction is not specifically limited in the embodiment of the present application, and can be 0, 1, or multiple.

[0053] Integrate the hidden danger electrical equipment corresponding to each predicted peak power consumption period and the continuous working time of each hidden danger electrical equipment, and determine the target electrical equipment from multiple hidden danger electrical equipment. The target electrical equipment is the hidden danger electrical equipment whose occurrence frequency in the second preset time period is higher than the preset occurrence frequency, or the hidden danger electrical equipment whose continuous working time is higher than the preset working time.

[0054] Specifically, the continuous working time of the hidden danger electrical equipment is the working time after the hidden danger electrical equipment is started during the current working cycle. The continuous working time of each hidden danger electrical equipment can also be identified and obtained from the historical work log information. The hidden danger electrical equipment corresponding to different predicted peak power consumption periods may be the same. The predicted peak power consumption period corresponding to each hidden danger electrical equipment can be identified, and the occurrence frequency of each hidden danger electrical equipment in the second preset time period can be determined. For example, the second preset time period is 10:10 on xx-xx-12-20:10 on xx-xx-13, among which there are three predicted peak power consumption periods, namely 12:10-12:30 on xx-xx-12, 18:20-18:50 on xx-xx-12, and 10:10- 10:30, among which, the electrical equipment with hidden dangers corresponding to 12:10-12:30 on xx-12 are equipment a, equipment b and equipment c, the electrical equipment with hidden dangers corresponding to 18:20-18:50 on xx-12 are equipment a and equipment c, and the electrical equipment with hidden dangers corresponding to 10:10-10:30 on xx-13 is equipment a, among which, the frequency of equipment a appearing from 10:10 on xx-12 to 20:10 on xx-13 is 3 times, the frequency of equipment b is 1 time, and the frequency of equipment c is 2 times.

[0055] Hidden danger electrical equipment whose occurrence frequency is higher than the preset occurrence frequency and hidden danger electrical equipment whose continuous working time is higher than the preset working time are determined as target electrical equipment. The number of target electrical equipment is not specifically limited in the embodiment of the present application. By setting the preset occurrence frequency and the preset working time, it is convenient to screen multiple hidden danger electrical equipment, so as to determine the target electrical equipment that needs to be focused on from multiple hidden danger electrical equipment, wherein the preset occurrence frequency can be 5 times or 8 times, and the preset working time can be 18 hours or 20 hours. The specific occurrence frequency and preset working time are not specifically limited in the embodiment of the present application, and can be determined by relevant staff based on historical experimental data and uploaded to the wake-up system.

[0056] A directional wake-up instruction is generated based on the continuous working time corresponding to the target electric device, and the directional wake-up instruction is used to wake up the energy storage battery in the next working cycle of the target electric device.

[0057] Specifically, the directional wake-up instruction is used to wake up the energy storage battery in the next working cycle of the target power consumer. That is, in the next working cycle of the target power consumer, when it is detected that the working time of the target power consumer is consistent with the continuous working time corresponding to the abnormal load situation in the current cycle, the energy storage battery is directly woken up. It is worth noting that in the next working cycle of the target power consumer, the power grid operation parameter information may not be higher than the preset standard operation parameter information. By waking up the energy storage battery in advance before the power grid is highly loaded due to the use of the target power consumer in the next working cycle, it is convenient to reduce the loss or damage to the power grid caused by the use of the target power consumer.

[0058] Furthermore, in order to reduce the energy consumption of the energy storage battery during standby, the method provided in the embodiment of the present application also includes: When the preset conditions are met, the operating state of the energy storage battery is adjusted to a deep sleep state; when it is detected that the operating state of the energy storage battery is in a deep sleep state, a hardware shutdown instruction is generated, and the hardware shutdown instruction is used to shut down the connection between the energy storage battery and all hardware devices except the RTC real-time clock; wherein the preset conditions include: the power grid operation parameter information contains a preset fault identifier; or the power grid operation parameter information is lower than the preset basic operation parameter information, and the duration of the power grid operation parameter information being lower than the preset basic operation parameter information is longer than the preset basic duration.

[0059] Specifically, when the energy storage battery is in a deep sleep state, it indicates that the energy storage battery is currently only connected to the RTC real-time clock, and is disconnected from other hardware devices. The RTC (RealTime Clock) real-time clock can continuously and independently provide accurate time information, including seconds, minutes, hours, days, months, years, etc. Through the timing function of the RTC real-time clock, the energy storage battery can be charged, discharged, or other maintenance and diagnosis operations can be performed after automatic wake-up. Therefore, when the energy storage battery is in a deep sleep state, the connection between the energy storage battery and the RTC real-time clock must still be maintained.

[0060] The trigger condition for generating a shutdown hardware instruction is that the grid operation parameter information contains a preset fault identifier, wherein the preset fault identifier can be a grid fault caused by a natural disaster or equipment damage. When a grid fault occurs, in order to ensure the stability and safety of the grid power supply, the energy storage battery is placed in a deep sleep state to reduce the interference and load of the energy storage battery on the grid, thereby facilitating the reduction of the damage caused to the energy storage battery by the grid fault. The trigger condition can also be that the grid operation parameter information is lower than the preset basic operation parameter information, and the duration of the grid operation parameter information being lower than the preset basic operation parameter information is longer than the preset basic duration. During the low load period of the grid, the demand for electricity from the electrical equipment is low, and the energy storage battery may not need to be frequently charged and discharged. At this time, the energy storage battery can be placed in a deep sleep state to reduce unnecessary energy loss, thereby facilitating the improvement of energy utilization efficiency. The preset basic operation parameters include a basic operation voltage and a basic operation current. When the grid operation parameter information is lower than the preset basic operation parameter information, and the duration of the grid operation parameter information being lower than the preset basic operation parameter information is longer than the preset basic duration, a shutdown hardware instruction can be generated. The preset basic duration can be 40 minutes or 50 minutes. The specific duration is not specifically limited in the embodiment of the present application. By disconnecting the energy storage battery in a deep sleep state, the energy consumption of the energy storage battery during standby can be reduced.

[0061] Furthermore, in order to facilitate relevant staff to promptly respond to and handle abnormal situations found during the diagnosis process, the method provided in the embodiment of the present application also includes: Based on the mapping relationship between the operating status of the energy storage battery and the preset diagnostic strategy, the target diagnostic strategy corresponding to the energy storage battery is determined. The preset diagnostic strategy mapping relationship is the corresponding relationship between the operating status and the target diagnostic strategy. The target diagnostic strategy includes the diagnosis duration and the diagnosis frequency. The energy storage battery is diagnosed based on the target diagnostic strategy. When an abnormal position appears in the battery diagnosis result, the target substitute position is determined from the substitute batteries based on the abnormal position, and the battery warning information is generated based on the abnormal position and the target substitute position.

[0062] Specifically, the purpose of diagnosing the energy storage battery is to troubleshoot abnormal sub-batteries contained in the energy storage battery, which is different from the purpose of determining the health simulation value information of the energy storage battery based on a preset health simulation battery model. The energy storage battery is obtained by connecting multiple sub-batteries in series or in parallel. When an abnormal sub-battery appears in the energy storage battery, it may affect the overall stability of the energy storage battery. Therefore, the energy storage battery needs to be diagnosed for health reasons.

[0063] The operating state of the energy storage battery includes a non-dormant state, a dormant state, and a deep sleep state. Different operating states correspond to different target diagnostic strategies. For example, when the energy storage battery is in a non-dormant state, it indicates that the energy storage battery is connected to the power grid. At this time, the corresponding target diagnostic strategy has a longer diagnostic time and a higher diagnostic frequency. When the energy storage battery is in a deep sleep state, it indicates that the energy storage battery is not connected to the power grid and may not be connected to the power grid for a long period of time in the future. At this time, the corresponding target diagnostic strategy has a shorter diagnostic time and a lower diagnostic frequency. The target diagnostic strategy corresponding to different operating states can be determined according to the preset diagnostic strategy mapping relationship. The specific content of the preset diagnostic strategy mapping relationship is not specifically limited in the embodiment of this application, and can be determined by relevant staff based on historical experimental data and uploaded to the wake-up system.

[0064] The abnormal position is a sub-battery with abnormal temperature, abnormal current or abnormal voltage, wherein different operating states correspond to different abnormal judgment standards, which can be determined by relevant staff based on historical experimental data and uploaded to the wake-up system. The specific content of the abnormal judgment standard is not specifically limited in the embodiment of the present application. For example, when the operating state is a dormant state, if the temperature of a sub-battery in the energy storage battery is diagnosed to be higher than a°C, the position corresponding to the sub-battery in the energy storage battery can be determined as an abnormal position, and when the operating state is a deep sleep state, if the temperature of a sub-battery in the energy storage battery is diagnosed to be higher than b°C, the position corresponding to the sub-battery in the energy storage battery can be determined as an abnormal position, and a°C is higher than b°C.

[0065] In order to ensure that the energy storage battery can be normally connected to the power grid, a replacement battery is generally added to the energy storage battery. The replacement battery contains multiple sub-batteries, which are used to replace the sub-batteries with abnormal conditions in the energy storage battery. After the abnormal position is determined, the abnormal position can be marked in the energy storage battery, which is helpful for the subsequent processing and replacement of the sub-batteries corresponding to the abnormal position. The number of replacement sub-batteries corresponding to the target replacement position is consistent with the number of sub-batteries corresponding to the abnormal position. The generated battery warning information contains the abnormal position and the target replacement position. By feeding back the battery warning information, it is convenient to remind relevant staff to promptly understand the abnormal situation of the energy storage battery and take corresponding treatment measures, such as short-circuiting the sub-battery corresponding to the abnormal position and then connecting the sub-battery corresponding to the target replacement position to the energy storage battery.

[0066] By setting different diagnostic strategies for energy storage batteries in different operating states, it is convenient to improve the compatibility between the diagnostic strategy and the energy storage battery, thereby facilitating the improvement of the accuracy of the diagnostic results. When there are abnormal conditions in the battery diagnostic results, the corresponding target substitute position is promptly determined from the substitute battery based on the abnormal condition, and by generating battery early warning information, it is convenient for relevant personnel to respond to and handle abnormal conditions found during the diagnosis process in a timely manner, thereby facilitating the reduction of the impact of the abnormal position on the normal operation of the energy storage battery.

[0067] In an embodiment of the present application, a wake-up system is provided, such as Figure 3 As shown, Figure 3 The wake-up system 300 shown includes: a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, such as through a bus 302. Optionally, the wake-up system 300 may also include a transceiver 304. It should be noted that in actual applications, the transceiver 304 is not limited to one, and the structure of the wake-up system 300 does not constitute a limitation on the embodiments of the present application.

[0068] The processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. The processor 301 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0069] The bus 302 may include a path to transmit information between the above components. The bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 302 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The fact that only one line is used in the diagram does not mean that there is only one bus or only one type of bus.

[0070] The memory 303 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0071] The memory 303 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the contents shown in the above method embodiment.

[0072] The wake-up system includes, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 3 The wake-up system shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0073] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding content in the aforementioned method embodiment.

[0074] An embodiment of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the method in any of the above embodiments is implemented.

[0075] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0076] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for remotely waking up an energy storage battery, characterized in that: include: Acquiring power grid operation parameter information, wherein the power grid operation parameter information includes operation voltage and operation current; Importing the power grid operation parameters into a preset power grid load forecasting model to obtain forecast operation parameter information, and determining a forecast peak power consumption period based on the forecast operation parameter information and preset standard operation parameter information, wherein the forecast operation parameter information corresponding to the forecast peak power consumption period is higher than the preset standard operation parameter information, and the forecast operation parameter information includes a forecast operation voltage and a forecast operation current; Acquire the operating state of the energy storage battery, and when the operating state is a dormant state, determine the awakening working parameter information based on the predicted operating parameter information and the preset standard operating parameter information; A wake-up instruction is generated based on the predicted peak power consumption period and the wake-up working parameter information, and the energy storage battery is awakened based on the wake-up instruction.

2. A method for remotely waking up an energy storage battery according to claim 1, characterized in that: After generating the wake-up instruction based on the predicted peak power consumption period and the wake-up working parameter information, the method further includes: Acquire historical execution parameter information of the energy storage battery within a first preset time period, wherein the historical execution parameter information includes average execution voltage, charge and discharge times, average execution temperature, and average execution humidity; Importing the historical execution parameter information into a preset health simulation battery model corresponding to the energy storage battery to obtain health simulation value information, wherein the health simulation value information includes a SOH value and a SOC value; Determine the target optimization strategy corresponding to the energy storage battery based on the mapping relationship between the health simulation value information and the preset optimization strategy, wherein the preset optimization strategy mapping relationship includes target optimization strategies corresponding to different health simulation value information, and the target optimization strategy includes wake-up frequency and wake-up duration; The energy storage battery is awakened based on the target optimization strategy.

3. A method for remotely waking up an energy storage battery according to claim 1, characterized in that: The determining the wake-up working parameter information based on the predicted operating parameter information and the preset standard operating parameter information includes: Determining wake-up execution parameter information based on the predicted operation parameter information and the preset standard operation parameter information, wherein the wake-up execution parameter information includes a wake-up execution voltage and a wake-up execution current; Acquire startup operation parameter information of the energy storage battery, and determine the wake-up duration based on the startup operation parameter information and the wake-up execution parameter information, wherein the startup operation parameter information includes a discharge rate, a battery capacity, and a battery aging degree; A wake-up time is determined based on the predicted peak power consumption period and the wake-up duration, and wake-up work parameter information is determined based on the wake-up time and the wake-up execution parameter information.

4. A method for remotely waking up an energy storage battery according to claim 1, characterized in that: Also includes: Obtaining the wake-up instructions and corresponding historical work log information generated within the second preset time period, and identifying the hidden danger electrical equipment corresponding to the predicted peak power consumption period of each wake-up instruction from the historical work log information; Integrate the hidden danger power-consuming equipment corresponding to each predicted peak power consumption period and the continuous working time of each hidden danger power-consuming equipment, and determine the target power-consuming equipment from multiple hidden danger power-consuming equipment, wherein the target power-consuming equipment is the hidden danger power-consuming equipment whose occurrence frequency in the second preset time period is higher than the preset occurrence frequency, or the hidden danger power-consuming equipment whose continuous working time is higher than the preset working time; A directional wake-up instruction is generated based on the continuous working time corresponding to the target electrical device, and the directional wake-up instruction is used to wake up the energy storage battery in the next working cycle of the target electrical device.

5. The method for remotely waking up an energy storage battery according to claim 1, characterized in that: Also includes: When a preset condition is met, adjusting the operating state of the energy storage battery to a deep sleep state; When it is detected that the operating state of the energy storage battery is a deep sleep state, a hardware shutdown instruction is generated, wherein the hardware shutdown instruction is used to close the connection between the energy storage battery and all hardware devices except the RTC real-time clock; The pre-conditions include: The power grid operation parameter information includes a preset fault identifier; or, The power grid operation parameter information is lower than the preset basic operation parameter information, and the duration for which the power grid operation parameter information is lower than the preset basic operation parameter information is longer than the preset basic duration.

6. A method for remotely waking up an energy storage battery according to claim 5, characterized in that: Also includes: Based on the operating state of the energy storage battery and the preset diagnostic strategy mapping relationship, determine the target diagnostic strategy corresponding to the energy storage battery, the preset diagnostic strategy mapping relationship is the corresponding relationship between the operating state and the target diagnostic strategy, and the target diagnostic strategy includes the diagnostic duration and the diagnostic frequency; The energy storage battery is diagnosed based on the target diagnosis strategy. When an abnormal position appears in the battery diagnosis result, a target substitute position is determined from substitute batteries based on the abnormal position, and battery warning information is generated based on the abnormal position and the target substitute position.

7. A wake-up system, characterized in that: The wake-up system includes: at least one processor; Memory; At least one application, wherein the at least one application is stored in a memory and configured to be executed by at least one processor, and the at least one application is configured to: execute a remote wake-up method for an energy storage battery according to any one of claims 1-6.

8. A computer-readable storage medium, characterized in that: include: A computer program is stored which can be loaded by a processor and executes a method for remotely waking up an energy storage battery as claimed in any one of claims 1 to 6.

9. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the steps of a method for remotely waking up an energy storage battery according to any one of claims 1 to 6.

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