Photovoltaic energy storage charging pile monitoring method and system and electronic equipment

Abstract

The invention provides a photovoltaic energy storage charging pile monitoring method and system and electronic equipment, and relates to the technical field of intelligent power grid and new energy automobile charging facility management, and the method comprises the steps: collecting real-time operation state data based on a sensor network; constructing a charging demand prediction model, and predicting the charging demand electric quantity of the target charging pile according to the real-time operation state data; according to the real-time operation state data and the charging demand electric quantity, the electric energy distribution quantity of the target charging pile is predicted; acquiring an electric energy distribution priority score of the target charging pile, and performing electric energy distribution on the target charging pile in combination with the electric energy distribution quantity; the charging state data of the target charging pile is monitored, and fault judgment is performed on the target charging pile based on the charging state data, so that the energy distribution strategy of the charging pile can be automatically adjusted according to factors such as different seasons, time and vehicle demands, and the phenomenon of insufficient energy in the peak power consumption period is avoided; therefore, fine management and accurate scheduling of energy can be realized, and the energy utilization rate is obviously improved.

Description

Technical Field

[0001] The present invention relates to the field of smart grid and new energy vehicle charging facility management technology, and in particular to a photovoltaic energy storage charging pile monitoring method, system and electronic equipment. Background Art

[0002] As the popularity of new energy vehicles continues to increase, photovoltaic energy storage charging piles have become a key component of green travel. Currently, the operation of photovoltaic energy storage charging piles depends on stable power supply and efficient energy management system.

[0003] However, traditional photovoltaic energy storage charging piles mainly use fixed-rule energy distribution mechanisms, such as evenly distributing energy over time or distributing energy according to preset priorities, which often ignores dynamic factors in actual use. Therefore, existing technologies often lack intelligent energy scheduling strategies and fail to effectively adapt to the differences in actual electricity demand in different seasons and time periods, which may lead to unreasonable energy scheduling and the inability to accurately match the charging needs of electric vehicles in different time periods, which can easily lead to resource waste or supply shortages. For example, there may be energy shortages during peak charging periods, while there may be a large amount of surplus energy that cannot be effectively utilized during low-peak periods.

[0004] Therefore, it is necessary to provide a photovoltaic energy storage charging pile monitoring method, system and electronic equipment to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a photovoltaic energy storage charging pile monitoring method, system and electronic equipment, which are used to solve the problem that the existing technology cannot achieve refined energy management of photovoltaic energy storage charging piles, and cannot automatically adjust the energy allocation strategy of the charging piles according to factors such as different seasons, time and vehicle demand, which leads to low energy utilization and insufficient energy during peak power consumption.

[0006] The photovoltaic energy storage charging pile monitoring method provided by the present invention comprises: Collect real-time operation status data based on sensor networks; Constructing a charging demand prediction model, and predicting the charging demand power of the target charging pile according to the real-time operating status data; Predicting the electric energy distribution amount of the target charging pile according to the real-time operating status data and the charging demand power; Obtaining the power allocation priority score of the target charging pile, and allocating power to the target charging pile in combination with the power allocation amount; The charging status data of the target charging pile is monitored, and a fault judgment is made on the target charging pile based on the charging status data.

[0007] Preferably, the collecting of real-time operation status data based on the sensor network specifically includes: Measuring solar power generation from solar panels based on power sensors; Monitoring the current battery power of the energy storage battery based on a power sensor, monitoring the current charge and discharge current of the energy storage battery based on a current sensor, monitoring the current charge and discharge voltage of the energy storage battery based on a voltage sensor, and summarizing the current battery power, the current charge and discharge current, and the current charge and discharge voltage to generate battery charge and discharge data; Acquire the charging load of the target charging pile based on a status monitoring sensor; The solar power generation, the battery charging and discharging data and the charging load are summarized to generate the real-time operation status data.

[0008] Preferably, the building of a charging demand prediction model and predicting the charging demand power of a target charging pile according to the real-time operating status data specifically includes: Extracting features from the real-time running status data to obtain corresponding real-time running status features; The real-time operating status feature is input into the forget gate of the charging demand prediction model to determine the real-time operating status feature that needs to be discarded from the memory unit. The corresponding calculation formula is as follows: In the formula, Represents the output of the forget gate at the current time t, which is used to determine the real-time running state features that need to be discarded from the memory unit; represents the activation function of the forget gate; Represents the weight of the forget gate; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of Represents the bias term of the forget gate; Based on the input gate of the charging demand prediction model, the real-time operating status feature to be stored in the memory unit is determined, and the corresponding calculation formula is as follows: In the formula, The output of the input gate at the current time t is used to determine the real-time operating status characteristics that need to be stored in the memory unit; represents the activation function of the input gate; Represents the weight of the input gate; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of represents the bias term of the input gate; The candidate required power is determined based on the candidate memory unit of the charging demand prediction model, and the corresponding calculation formula is as follows: In the formula, Represents the candidate memory unit at the current time t, which is used to determine the candidate required power; represents the hyperbolic tangent function; Represents the weight of the candidate memory unit; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of Represents the bias term of the candidate memory unit.

[0009] Preferably, the calculation formula for updating the memory unit is as follows: In the formula, Represents the memory unit at the current time t; Represents the output of the forget gate at the current time t; Represents the memory unit at the previous time t-1; Represents the output of the input gate at the current time t; Represents the candidate memory unit at the current time t; Based on the output gate of the charging demand prediction model, the candidate required power that needs to be output to the hidden state is determined, and the corresponding calculation formula is as follows: In the formula, represents the output of the output gate at the current time t, which is used to determine the candidate demand power that needs to be output to the hidden state; represents the activation function of the output gate; Represents the weight of the output gate; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of Represents the bias term of the output gate.

[0010] Preferably, the charging demand power is predicted according to the output of the memory unit and the output gate at the current time t, and the corresponding calculation formula is as follows: In the formula, represents the hidden state at the current time t, that is, the predicted charging demand power; represents the hyperbolic tangent function; represents the output of the output gate at the current time t; Represents the memory unit at the current time t.

[0011] Preferably, predicting the electric energy distribution amount of the target charging pile according to the real-time operating status data and the charging demand power specifically includes: At the current time t, the charge and discharge efficiency of the energy storage battery is obtained, and combined with the current battery power, the current released power of the energy storage battery is calculated as follows: In the formula, Indicates the current released power of the energy storage battery; Indicates the current battery level; Indicates the charge and discharge efficiency; According to the current released power and the solar power generation, the total power supply of all the target charging piles is calculated as follows: In the formula, Indicates the total power supply of all target charging piles; Indicates the current released power of the energy storage battery; represents the solar power generation; t represents the current time; According to the charging demand power and the total supply power, the power distribution amount of the target charging pile is calculated as follows: In the formula, represents the electric energy distribution of the i-th target charging pile at the current time t; represents the charging demand of the i-th target charging pile at the current time t; n represents the total number of target charging piles; Indicates the total power supplied by all target charging piles.

[0012] Preferably, monitoring the charging status data of the target charging pile and performing fault judgment on the target charging pile based on the charging status data specifically includes: Monitor and collect charging status data of the target charging pile, namely, charging current, charging voltage and charging progress change rate; The normal charging current range of the target charging pile is obtained as follows: , and They represent the lower and upper limits of the current when the target charging pile is normally charged; the normal charging voltage range is , and They represent the lower and upper limits of the voltage of the target charging pile during normal charging, and the range of the normal charging progress rate is , and They respectively represent the lower limit and upper limit of the progress change rate when the target charging pile is charging normally; The abnormality judgment formula of the charging current of the target charging pile is as follows: In the formula, Indicates the current status of the target charging pile. Indicates that the charging current of the target charging pile is in normal state. Indicates that the charging current of the target charging pile is in an abnormal state; I indicates the charging current of the target charging pile; and They respectively represent the lower and upper limits of the current when the target charging pile is charging normally; The abnormality judgment formula of the charging voltage of the energy storage battery is as follows: In the formula, Indicates the voltage status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; V indicates the charging voltage of the target charging pile; and They respectively represent the lower and upper limits of the voltage of the target charging pile during normal charging; The abnormality judgment formula of the charging progress of the target charging pile is as follows: In the formula, Indicates the charging progress status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; R indicates the charging progress change rate of the target charging pile; and They respectively represent the lower limit and upper limit of the progress change rate when the target charging pile is charging normally.

[0013] Preferably, based on the current state identifier, the voltage state identifier and the charging progress state identifier, the comprehensive state identifier of the target charging pile is determined as follows: In the formula, A represents the comprehensive status identifier of the target charging pile, A=1 means that the target charging pile is in a normal state and there is no fault, and A=0 means that the target charging pile is in an abnormal state and there is a fault; Indicates the current status of the target charging pile. Indicates that the charging current of the target charging pile is in normal state. Indicates that the charging current of the target charging pile is in an abnormal state; I indicates the charging current of the target charging pile; Indicates the voltage status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; V indicates the charging voltage of the target charging pile; Indicates the charging progress status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; R indicates the charging progress change rate of the target charging pile; Indicates the maximum value operation.

[0014] Photovoltaic energy storage charging pile monitoring system, the monitoring system includes: An operation status data collection module is used to collect real-time operation status data based on a sensor network; A charging demand power prediction module is used to construct a charging demand prediction model and predict the charging demand power of the target charging pile according to the real-time operating status data; An electric energy distribution prediction model is used to predict the electric energy distribution of the target charging pile according to the real-time operating status data and the charging demand power; A charging pile power distribution module, used to obtain the power distribution priority score of the target charging pile, and distribute power to the target charging pile in combination with the power distribution amount; The charging pile fault judgment module is used to monitor the charging status data of the target charging pile and perform fault judgment on the target charging pile based on the charging status data.

[0015] An electronic device comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor runs the computer program stored in the memory, the processor executes the photovoltaic energy storage charging pile monitoring method as described in any one of the above items.

[0016] Compared with related technologies, the photovoltaic energy storage charging pile monitoring method, system and electronic equipment provided by the present invention have the following beneficial effects: The present invention can collect real-time operating status data based on a sensor network; construct a charging demand prediction model, and predict the charging demand power of a target charging pile according to the real-time operating status data; predict the electric energy allocation of the target charging pile according to the real-time operating status data and the charging demand power; obtain the electric energy allocation priority score of the target charging pile, and allocate electric energy to the target charging pile in combination with the electric energy allocation amount; monitor the charging status data of the target charging pile, and make a fault judgment on the target charging pile based on the charging status data, so that the energy allocation strategy of the charging pile can be automatically adjusted according to factors such as different seasons, time and vehicle demand, avoiding the phenomenon of energy shortage during peak power consumption period, and then realizing the refined energy management of the photovoltaic energy storage charging pile system, significantly improving the energy utilization rate, and realizing accurate energy scheduling.

[0017] The present invention adopts a dynamic energy allocation algorithm, which significantly improves energy utilization and realizes accurate energy scheduling. The present invention effectively alleviates the energy shortage during peak power consumption periods by accurately matching charging demand, while avoiding energy waste during low periods. The present invention integrates a charging demand prediction model, which can accurately predict charging demand in different seasons and times, making energy scheduling more intelligent, which not only improves the flexibility of the photovoltaic energy storage system, but also enhances user satisfaction. The photovoltaic energy storage charging pile monitoring system of the present invention adopts redundant design and distributed architecture, which greatly improves the stability, reliability and response speed of the system. It can still maintain normal operation in the face of extreme weather or unexpected events, significantly reducing the system maintenance cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of the photovoltaic energy storage charging pile monitoring method of the present invention; Figure 2 This is a system block diagram of the photovoltaic energy storage charging pile monitoring system of the present invention; Figure 3 A schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1 FIG. 1 is a flow chart of a photovoltaic energy storage charging pile monitoring method provided by an embodiment of the present invention. Figure 1The execution subject of the method shown may be a software and / or hardware device. The execution subject of the present application may include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, user equipment may include but is not limited to computers, smart phones, personal digital assistants (PDA) and the electronic devices mentioned above. Network equipment may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers, wherein cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. It includes steps S1 to S5, as follows: S1, collects real-time operation status data based on sensor network; The sensor network is a network composed of multiple sensors used to obtain real-time information. The sensor network includes power sensors for measuring solar power generation, power sensors for monitoring the power of energy storage batteries, current sensors for monitoring battery charge and discharge currents, voltage sensors for monitoring battery charge and discharge voltages, and status monitoring sensors for obtaining charging loads of charging piles.

[0021] It is understandable that through the deployed sensor network, the status information of the photovoltaic energy storage system and the charging pile network, that is, the real-time operating status data, can be fully collected.

[0022] S2, constructing a charging demand prediction model, and predicting the charging demand power of the target charging pile according to the real-time operating status data; It should be noted that the charging demand prediction model refers to a model used to predict the charging power required by the charging pile in the future. The charging demand power refers to the charging power required by the charging pile in a specific time period in the future. The target charging pile refers to the charging pile that needs to be allocated power.

[0023] In practical applications, based on the status information of the photovoltaic energy storage system and the charging pile network, combined with factors such as vehicle charging habits and seasonal characteristics, a charging demand prediction model can be constructed, which can predict the charging power required by the charging pile in the future, providing a key basis for the subsequent rational allocation of electricity.

[0024] S3, predicting the electric energy distribution amount of the target charging pile according to the real-time operating status data and the charging demand power; It is understood that the power allocation refers to the power allocated to the charging pile. Based on the real-time operating status data and the predicted charging demand power, the power allocation required by the charging pile can be predicted, and then the allocated power can be ensured to meet its actual needs.

[0025] S4, obtaining the power allocation priority score of the target charging pile, and allocating power to the target charging pile in combination with the power allocation amount; In actual applications, high-performance electronic devices can be configured as central controllers, and monitoring software with big data processing capabilities and self-learning functions can be installed to accurately control the power distribution of charging piles.

[0026] The power allocation priority score refers to the priority score used to determine the order of power allocation. The power allocation priority score of the charging pile can be determined based on factors such as the location importance of the charging pile and the type of service vehicle. Combining this score with the predicted power allocation amount can accurately allocate power to the charging pile, thereby ensuring that important charging piles get power first and achieving efficient energy utilization.

[0027] Specifically, sufficient electric energy can be allocated in advance to charging piles with higher priority scores, and the energy supply to charging piles with lower priority scores can be appropriately reduced, thereby ensuring that the entire system maintains efficient and smooth operation.

[0028] In addition, the power distribution strategy of the charging pile can be adjusted in real time. When sudden high-power demand or system abnormality is detected, power can be quickly redistributed to ensure stable power supply.

[0029] S5, monitoring the charging status data of the target charging pile, and performing fault judgment on the target charging pile based on the charging status data.

[0030] It can be understood that the charging status data refers to various status data during the charging process of the charging pile, which is used to determine whether the charging pile is working normally.

[0031] During the charging process of the charging pile, its charging status data can be continuously monitored, including charging current, voltage, charging progress, etc. According to the normal status data range and abnormal judgment rules of the charging pile, the charging pile can be judged for faults. In addition, when an abnormal condition is found in the charging pile, timely response measures are taken to ensure the safety and stability of the charging process.

[0032] For example, at noon on a sunny spring day, the system can monitor a large amount of solar power generation and less charging demand. At this time, the system can store excess solar energy to reduce its dependence on electricity. On the contrary, due to the decrease in solar energy in the evening and the peak period after get off work, a large number of electric vehicles start charging. Therefore, the system will automatically call on the power of the energy storage battery to supplement the power required by the charging pile. At the same time, it can dynamically adjust the charging speed of each charging pile according to the real-time charging demand to ensure the balance between supply and demand.

[0033] In the specific implementation process, the real-time operation status data is collected based on the sensor network, specifically including: Measuring solar power generation from solar panels based on power sensors; Monitoring the current battery power of the energy storage battery based on a power sensor, monitoring the current charge and discharge current of the energy storage battery based on a current sensor, monitoring the current charge and discharge voltage of the energy storage battery based on a voltage sensor, and summarizing the current battery power, the current charge and discharge current, and the current charge and discharge voltage to generate battery charge and discharge data; Acquire the charging load of the target charging pile based on a status monitoring sensor; The solar power generation, the battery charging and discharging data and the charging load are summarized to generate the real-time operation status data.

[0034] Among them, the power sensor refers to a device used to measure the power generated by the solar panel. The power sensor refers to a sensor used to obtain the remaining power information of the energy storage battery in real time. The current sensor refers to a sensor used to monitor the charge and discharge current of the energy storage battery. The voltage sensor refers to a sensor used to monitor the charge and discharge voltage of the energy storage battery. The state monitoring sensor refers to a sensor used to obtain the charging load data of the charging pile.

[0035] In practical applications, power sensors can be used to accurately measure the solar power generated by solar panels. This data reflects the energy input of the photovoltaic energy storage system. At the same time, power sensors can be used to monitor the current battery power of the energy storage battery in real time, current sensors can monitor its current charge and discharge current, and voltage sensors can monitor its current charge and discharge voltage. These data can be summarized to generate comprehensive battery charge and discharge data, which can clearly present the working status and energy changes of the energy storage battery. In addition, the charging load of the charging pile can be obtained through the status monitoring sensor. This data reflects the real-time power demand of the charging pile.

[0036] Finally, the measured solar power generation, the generated battery charge and discharge data, and the acquired charging load can be integrated and summarized to form complete real-time operating status data. These data cover the key operating information of the photovoltaic energy storage system and the charging pile network, providing a data basis for subsequent system analysis, decision-making, and optimization control.

[0037] The step of constructing a charging demand prediction model and predicting the charging demand power of a target charging pile according to the real-time operating status data specifically includes: Extracting features from the real-time running status data to obtain corresponding real-time running status features; The real-time operating status feature is input into the forget gate of the charging demand prediction model to determine the real-time operating status feature that needs to be discarded from the memory unit. The corresponding calculation formula is as follows: In the formula, Represents the output of the forget gate at the current time t, which is used to determine the real-time running state features that need to be discarded from the memory unit; represents the activation function of the forget gate; Represents the weight of the forget gate; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of Represents the bias term of the forget gate; Based on the input gate of the charging demand prediction model, the real-time operating status feature to be stored in the memory unit is determined, and the corresponding calculation formula is as follows: In the formula, The output of the input gate at the current time t is used to determine the real-time operating status characteristics that need to be stored in the memory unit; represents the activation function of the input gate; Represents the weight of the input gate; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of represents the bias term of the input gate; The candidate required power is determined based on the candidate memory unit of the charging demand prediction model, and the corresponding calculation formula is as follows: In the formula, Represents the candidate memory unit at the current time t, which is used to determine the candidate required power; represents the hyperbolic tangent function; Represents the weight of the candidate memory unit; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of Represents the bias term of the candidate memory unit.

[0038] The calculation formula for updating the memory unit is as follows: In the formula, Represents the memory unit at the current time t; Represents the output of the forget gate at the current time t; Represents the memory unit at the previous time t-1; Represents the output of the input gate at the current time t; Represents the candidate memory unit at the current time t; Based on the output gate of the charging demand prediction model, the candidate required power that needs to be output to the hidden state is determined, and the corresponding calculation formula is as follows: In the formula, represents the output of the output gate at the current time t, which is used to determine the candidate demand power that needs to be output to the hidden state; represents the activation function of the output gate; Represents the weight of the output gate; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of Represents the bias term of the output gate.

[0039] According to the output of the memory unit and the output gate at the current time t, the charging demand power is predicted, and the corresponding calculation formula is as follows: In the formula, represents the hidden state at the current time t, that is, the predicted charging demand power; represents the hyperbolic tangent function; represents the output of the output gate at the current time t; Represents the memory unit at the current time t.

[0040] Among them, the collected real-time operation status data can be feature extracted, that is, representative and relevant information can be extracted from data such as solar power generation, battery charging and discharging data, and charging pile charging load to obtain the corresponding real-time operation status features. These features can effectively reflect the actual operation status of the system and provide strong support for subsequent predictions.

[0041] Next, the real-time operating status features can be input into the charging demand prediction model. The forget gate of the model can combine the hidden state of the previous moment, the real-time operating status features of the current input, and other factors to determine the features that need to be discarded from the memory unit to avoid the memory unit storing too much redundant information. The input gate can determine the real-time operating status features that need to be stored in the memory unit, and then continuously update the memory unit content.

[0042] At the same time, the candidate memory unit can be used to determine the candidate required power, and the candidate memory unit comprehensively considers the impact of multiple factors on future charging demand. Then, the memory unit can be updated according to the results of the forget gate and the input gate, so that the memory unit can dynamically adapt to system changes. Furthermore, the output gate can determine the candidate required power that needs to be output to the hidden state. Finally, based on the memory unit at the current moment and the output of the output gate, the charging required power can be predicted, which provides a key basis for subsequent power distribution.

[0043] In addition, the weights of the charging demand prediction model can be continuously updated, and an online learning mechanism can be introduced to instantly reflect the latest changing trends in charging behavior. At the same time, the accuracy of the charging demand prediction model can be regularly evaluated. When the prediction error is found to increase, the model optimization process is actively started to adjust the model parameters until the ideal prediction performance is restored.

[0044] The predicting the electric energy distribution amount of the target charging pile according to the real-time operating status data and the charging demand electric power specifically includes: At the current time t, the charge and discharge efficiency of the energy storage battery is obtained, and combined with the current battery power, the current released power of the energy storage battery is calculated as follows: In the formula, Indicates the current released power of the energy storage battery; Indicates the current battery level; Indicates the charge and discharge efficiency; According to the current released power and the solar power generation, the total power supply of all the target charging piles is calculated as follows: In the formula, Indicates the total power supply of all target charging piles; Indicates the current released power of the energy storage battery; represents the solar power generation; t represents the current time; According to the charging demand power and the total supply power, the power distribution amount of the target charging pile is calculated as follows: In the formula, represents the electric energy distribution of the i-th target charging pile at the current time t; represents the charging demand of the i-th target charging pile at the current time t; n represents the total number of target charging piles; Indicates the total power supplied by all target charging piles.

[0045] It is understood that the charge and discharge efficiency refers to an indicator used to measure the energy conversion capacity of the energy storage battery during the charge and discharge process, which reflects the energy loss of the battery during charge and discharge. The current released power refers to the power actually released by the energy storage battery at the current moment. The total power supply includes the current released power of the energy storage battery and the solar power generation at the current moment, which is used to provide the total distributable power for all charging piles.

[0046] At the current moment, the charging and discharging efficiency of the energy storage battery can be obtained through the monitoring equipment, and the current released power of the energy storage battery can be calculated in combination with the current battery power monitored in real time.

[0047] Next, the calculated current released power can be integrated with the measured solar power generation to calculate the total power supply that all charging piles can obtain at the current moment.

[0048] Finally, the amount of power allocated to each charging pile can be determined based on the charging power demand of each charging pile at the current moment and combined with the total power supply. Through this allocation method, the total power can be reasonably allocated according to the actual needs of each charging pile, thereby improving energy utilization efficiency.

[0049] The monitoring of the charging status data of the target charging pile and performing fault judgment on the target charging pile based on the charging status data specifically includes: Monitor and collect charging status data of the target charging pile, namely, charging current, charging voltage and charging progress change rate; The normal charging current range of the target charging pile is obtained as follows: , and They represent the lower and upper limits of the current when the target charging pile is normally charged; the normal charging voltage range is , and They represent the lower and upper limits of the voltage of the target charging pile during normal charging, and the range of the normal charging progress rate is , and They respectively represent the lower limit and upper limit of the progress change rate when the target charging pile is charging normally; The abnormality judgment formula of the charging current of the target charging pile is as follows: In the formula, Indicates the current status of the target charging pile. Indicates that the charging current of the target charging pile is in normal state. Indicates that the charging current of the target charging pile is in an abnormal state; I indicates the charging current of the target charging pile; and They respectively represent the lower and upper limits of the current when the target charging pile is charging normally; The abnormality judgment formula of the charging voltage of the energy storage battery is as follows: In the formula, Indicates the voltage status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; V indicates the charging voltage of the target charging pile; and They respectively represent the lower and upper limits of the voltage of the target charging pile during normal charging; The abnormality judgment formula of the charging progress of the target charging pile is as follows: In the formula, Indicates the charging progress status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; R indicates the charging progress change rate of the target charging pile; and They respectively represent the lower limit and upper limit of the progress change rate when the target charging pile is charging normally.

[0050] Based on the current state identifier, the voltage state identifier and the charging progress state identifier, the comprehensive state identifier of the target charging pile is determined as follows: In the formula, A represents the comprehensive status identifier of the target charging pile, A=1 means that the target charging pile is in a normal state and there is no fault, and A=0 means that the target charging pile is in an abnormal state and there is a fault; Indicates the current status of the target charging pile. Indicates that the charging current of the target charging pile is in normal state. Indicates that the charging current of the target charging pile is in an abnormal state; I indicates the charging current of the target charging pile; Indicates the voltage status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; V indicates the charging voltage of the target charging pile; Indicates the charging progress status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; R indicates the charging progress change rate of the target charging pile; Indicates the maximum value operation.

[0051] Among them, the normal charging current range refers to the current range when the charging pile is working normally. When the charging current of the charging pile exceeds this range, it may affect its charging effect and even cause battery failure. The normal charging voltage range refers to the voltage range when the charging pile is working normally. The normal charging progress change rate range refers to the normal rate range of the charging progress changing over time during the normal charging process of the charging pile. The comprehensive status identification refers to the identification obtained by comprehensively analyzing the status of various charging parameters, which is used to intuitively judge whether the charging pile is working normally.

[0052] First of all, the charging status data of the charging pile can be continuously monitored and collected, including the charging current, charging voltage and charging progress change rate. These data can intuitively reflect the working status of the charging pile.

[0053] Then, the parameter ranges of the charging pile when it is working normally can be obtained. For example, the lower limit of the normal charging current range is the minimum current value allowed during normal charging, and the upper limit is the maximum current value. Similarly, the normal charging voltage range and the normal charging progress change rate range also have corresponding lower limits and upper limits, respectively.

[0054] Furthermore, various parameters can be judged according to the abnormal judgment formula. When the charging current exceeds the normal range, the current status indicator will show abnormality; the abnormal judgment process of charging voltage and charging progress change rate is the same as that of charging current.

[0055] Finally, the status indicators of current, voltage and charging progress can be combined to determine the comprehensive status indicator of the charging pile by taking the maximum value. If the comprehensive status indicator is 1, it means that the charging pile is in normal working condition; if the comprehensive status indicator is 0, it means that the charging pile has a fault and needs to be inspected and repaired in time.

[0056] In practical applications, in order to enhance the robustness and response speed of the system, redundant design principles and distributed computing architecture can be adopted to maintain the normal operation of the system even if some components fail, thereby ensuring the high availability and stability of the system and improving the user experience.

[0057] Specifically, multiple sensors can be deployed in different locations of the photovoltaic energy storage system and charging piles. Once a sensor fails, other sensors can still ensure the stability of the system. The present invention sets up two main and backup systems. Once the main system detects an error, the backup system will seamlessly take over the task to ensure uninterrupted service.

[0058] In addition, the cloud platform can be used to build an elastically scalable computing resource pool, and the number of virtual machines can be dynamically adjusted according to the computing load in different time periods, thereby improving computing efficiency and reducing operating costs.

[0059] like Figure 2 FIG. 1 is a system block diagram of a photovoltaic energy storage charging pile monitoring system provided by an embodiment of the present invention, wherein the monitoring system comprises: An operation status data collection module is used to collect real-time operation status data based on a sensor network; A charging demand power prediction module is used to construct a charging demand prediction model and predict the charging demand power of the target charging pile according to the real-time operating status data; An electric energy distribution prediction model is used to predict the electric energy distribution of the target charging pile according to the real-time operating status data and the charging demand power; A charging pile power distribution module, used to obtain the power distribution priority score of the target charging pile, and distribute power to the target charging pile in combination with the power distribution amount; The charging pile fault judgment module is used to monitor the charging status data of the target charging pile and perform fault judgment on the target charging pile based on the charging status data.

[0060] Figure 2 The apparatus of the embodiment shown can be used to perform Figure 1 The implementation principles and technical effects of the steps in the method embodiment shown are similar and will not be repeated here.

[0061] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the photovoltaic energy storage charging pile monitoring method as described in any one of the above items.

[0062] like Figure 3 FIG. 1 is a schematic diagram of the hardware structure of an electronic device provided by an embodiment of the present invention. The electronic device 30 includes: a processor 31, a memory 32 and a computer program; wherein The memory 32 is used to store the computer program, which may also be a flash memory. The computer program is, for example, an application program, a functional module, etc. for implementing the above method.

[0063] The processor 31 is used to execute the computer program stored in the memory to implement each step performed by the device in the above method. For details, please refer to the relevant description in the above method embodiment.

[0064] Optionally, the memory 32 may be independent or integrated with the processor 31 .

[0065] When the memory 32 is a device independent of the processor 31, the device may further include: The bus 33 is used to connect the memory 32 and the processor 31 .

[0066] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0067] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0068] The present invention also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of a device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0069] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0070] Through the introduction of the above embodiments, the present invention can collect real-time operating status data based on a sensor network through a photovoltaic energy storage charging pile monitoring method, system and electronic equipment; construct a charging demand prediction model, and predict the charging demand power of the target charging pile according to the real-time operating status data; predict the power distribution amount of the target charging pile according to the real-time operating status data and the charging demand power; obtain the power distribution priority score of the target charging pile, and distribute power to the target charging pile in combination with the power distribution amount; monitor the charging status data of the target charging pile, and make a fault judgment on the target charging pile based on the charging status data, so that the energy distribution strategy of the charging pile can be automatically adjusted according to factors such as different seasons, time and vehicle demand, avoiding the phenomenon of energy shortage during peak power consumption period, and then realizing the refined energy management of the photovoltaic energy storage charging pile system, significantly improving energy utilization and realizing accurate energy scheduling.

[0071] The present invention adopts a dynamic energy allocation algorithm, which significantly improves energy utilization and realizes accurate energy scheduling. The present invention effectively alleviates the energy shortage during peak power consumption periods by accurately matching charging demand, while avoiding energy waste during low periods. The present invention integrates a charging demand prediction model, which can accurately predict charging demand in different seasons and times, making energy scheduling more intelligent, which not only improves the flexibility of the photovoltaic energy storage system, but also enhances user satisfaction. The photovoltaic energy storage charging pile monitoring system of the present invention adopts redundant design and distributed architecture, which greatly improves the stability, reliability and response speed of the system. It can still maintain normal operation in the face of extreme weather or unexpected events, significantly reducing operation and maintenance costs.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A photovoltaic energy storage charging pile monitoring method, characterized in that: The monitoring method comprises: Collect real-time operation status data based on sensor networks; Constructing a charging demand prediction model, and predicting the charging demand power of the target charging pile according to the real-time operating status data; Predicting the electric energy distribution amount of the target charging pile according to the real-time operating status data and the charging demand power; Obtaining the power allocation priority score of the target charging pile, and allocating power to the target charging pile in combination with the power allocation amount; The charging status data of the target charging pile is monitored, and a fault judgment is made on the target charging pile based on the charging status data.

2. The photovoltaic energy storage charging pile monitoring method according to claim 1, characterized in that: The real-time operation status data collection based on the sensor network specifically includes: Measuring solar power generation from solar panels based on power sensors; Monitoring the current battery power of the energy storage battery based on a power sensor, monitoring the current charge and discharge current of the energy storage battery based on a current sensor, monitoring the current charge and discharge voltage of the energy storage battery based on a voltage sensor, and summarizing the current battery power, the current charge and discharge current, and the current charge and discharge voltage to generate battery charge and discharge data; Acquire the charging load of the target charging pile based on a status monitoring sensor; The solar power generation, the battery charging and discharging data and the charging load are summarized to generate the real-time operation status data.

3. The photovoltaic energy storage charging pile monitoring method according to claim 1, characterized in that: The step of constructing a charging demand prediction model and predicting the charging demand power of a target charging pile according to the real-time operating status data specifically includes: Extracting features from the real-time running status data to obtain corresponding real-time running status features; The real-time operating status feature is input into the forget gate of the charging demand prediction model to determine the real-time operating status feature that needs to be discarded from the memory unit. The corresponding calculation formula is as follows: In the formula, Represents the output of the forget gate at the current time t, which is used to determine the real-time running state features that need to be discarded from the memory unit; represents the activation function of the forget gate; Represents the weight of the forget gate; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of Represents the bias term of the forget gate; Based on the input gate of the charging demand prediction model, the real-time operating status feature to be stored in the memory unit is determined, and the corresponding calculation formula is as follows: In the formula, The output of the input gate at the current time t is used to determine the real-time operating status characteristics that need to be stored in the memory unit; represents the activation function of the input gate; Represents the weight of the input gate; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of represents the bias term of the input gate; The candidate required power is determined based on the candidate memory unit of the charging demand prediction model, and the corresponding calculation formula is as follows: In the formula, Represents the candidate memory unit at the current time t, which is used to determine the candidate required power; represents the hyperbolic tangent function; Represents the weight of the candidate memory unit; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of Represents the bias term of the candidate memory unit.

4. The photovoltaic energy storage charging pile monitoring method according to claim 3 is characterized in that: The calculation formula for updating the memory unit is as follows: In the formula, Represents the memory unit at the current time t; Represents the output of the forget gate at the current time t; Represents the memory unit at the previous time t-1; Represents the output of the input gate at the current time t; Represents the candidate memory unit at the current time t; Based on the output gate of the charging demand prediction model, the candidate required power that needs to be output to the hidden state is determined, and the corresponding calculation formula is as follows: In the formula, represents the output of the output gate at the current time t, which is used to determine the candidate demand power that needs to be output to the hidden state; represents the activation function of the output gate; Represents the weight of the output gate; Represents the hidden state at the previous moment t-1; Represents the real-time operating status characteristics of the input at the current time t; Indicates hidden state and real-time operating status features The vector composed of Represents the bias term of the output gate.

5. The photovoltaic energy storage charging pile monitoring method according to claim 4 is characterized in that: According to the output of the memory unit and the output gate at the current time t, the charging demand power is predicted, and the corresponding calculation formula is as follows: In the formula, represents the hidden state at the current time t, that is, the predicted charging demand power; represents the hyperbolic tangent function; represents the output of the output gate at the current time t; Represents the memory unit at the current time t.

6. The photovoltaic energy storage charging pile monitoring method according to claim 1 or 2, characterized in that: The predicting the electric energy distribution amount of the target charging pile according to the real-time operating status data and the charging demand electric power specifically includes: At the current time t, the charge and discharge efficiency of the energy storage battery is obtained, and combined with the current battery power, the current released power of the energy storage battery is calculated as follows: In the formula, Indicates the current released power of the energy storage battery; Indicates the current battery level; Indicates the charge and discharge efficiency; According to the current released power and the solar power generation, the total power supply of all the target charging piles is calculated as follows: In the formula, Indicates the total power supply of all target charging piles; Indicates the current released power of the energy storage battery; represents the solar power generation; t represents the current time; According to the charging demand power and the total supply power, the power distribution amount of the target charging pile is calculated as follows: In the formula, represents the electric energy distribution of the i-th target charging pile at the current time t; represents the charging demand of the i-th target charging pile at the current time t; n represents the total number of target charging piles; Indicates the total power supplied by all target charging piles.

7. The photovoltaic energy storage charging pile monitoring method according to claim 1, characterized in that: The monitoring of the charging status data of the target charging pile and performing fault judgment on the target charging pile based on the charging status data specifically includes: Monitor and collect charging status data of the target charging pile, namely, charging current, charging voltage and charging progress change rate; The normal charging current range of the target charging pile is obtained as follows: , and They represent the lower and upper limits of the current when the target charging pile is normally charged; the normal charging voltage range is , and They represent the lower and upper limits of the voltage of the target charging pile during normal charging, and the range of the normal charging progress rate is , and They respectively represent the lower limit and upper limit of the progress change rate when the target charging pile is charging normally; The abnormality judgment formula of the charging current of the target charging pile is as follows: In the formula, Indicates the current status of the target charging pile. Indicates that the charging current of the target charging pile is in normal state. Indicates that the charging current of the target charging pile is in an abnormal state; I indicates the charging current of the target charging pile; and They respectively represent the lower and upper limits of the current when the target charging pile is charging normally; The abnormality judgment formula of the charging voltage of the energy storage battery is as follows: In the formula, Indicates the voltage status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; V indicates the charging voltage of the target charging pile; and They respectively represent the lower and upper limits of the voltage of the target charging pile during normal charging; The abnormality judgment formula of the charging progress of the target charging pile is as follows: In the formula, Indicates the charging progress status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; R indicates the charging progress change rate of the target charging pile; and They respectively represent the lower limit and upper limit of the progress change rate when the target charging pile is charging normally.

8. The photovoltaic energy storage charging pile monitoring method according to claim 7, characterized in that: Based on the current state identifier, the voltage state identifier and the charging progress state identifier, the comprehensive state identifier of the target charging pile is determined as follows: In the formula, A represents the comprehensive status identifier of the target charging pile, A=1 means that the target charging pile is in a normal state and there is no fault, and A=0 means that the target charging pile is in an abnormal state and there is a fault; Indicates the current status of the target charging pile. Indicates that the charging current of the target charging pile is in normal state. Indicates that the charging current of the target charging pile is in an abnormal state; I indicates the charging current of the target charging pile; Indicates the voltage status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; V indicates the charging voltage of the target charging pile; Indicates the charging progress status of the target charging pile. Indicates that the charging voltage of the target charging pile is in normal state. Indicates that the charging voltage of the target charging pile is in an abnormal state; R indicates the charging progress change rate of the target charging pile; Indicates the maximum value operation.

9. A photovoltaic energy storage charging pile monitoring system, applied to the photovoltaic energy storage charging pile monitoring method according to any one of claims 1 to 8, the monitoring system comprising: An operation status data collection module is used to collect real-time operation status data based on a sensor network; A charging demand power prediction module is used to construct a charging demand prediction model and predict the charging demand power of the target charging pile according to the real-time operating status data; An electric energy distribution prediction model is used to predict the electric energy distribution of the target charging pile according to the real-time operating status data and the charging demand power; A charging pile power distribution module, used to obtain the power distribution priority score of the target charging pile, and distribute power to the target charging pile in combination with the power distribution amount; The charging pile fault judgment module is used to monitor the charging status data of the target charging pile and perform fault judgment on the target charging pile based on the charging status data.

10. An electronic device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes the photovoltaic energy storage charging pile monitoring method according to any one of claims 1 to 8.

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