Power supply charging data abnormity monitoring system and method based on big data

Abstract

The invention discloses a power supply charging data abnormity monitoring system and method based on big data, and belongs to the technical field of charging management. The system comprises a data acquisition module, an intelligent analysis module, an exception management module and a storage and visualization module. The data acquisition module is used for acquiring historical logs, a protocol comparison table, and a position distribution diagram, a temperature change diagram and operation data of the charging cabinet; the intelligent analysis module calculates the influence index of each idle charging grid when the data center receives a charging request, and opens the idle charging grid with the minimum influence index to respond to the charging request; the abnormity management module is used for calculating the temperature threshold value of each operation charging grid and judging whether the temperature threshold value is abnormal or not, and if yes, the charging protocol is readjusted according to the protocol comparison table till the temperatures of all the operation charging grids are not larger than the corresponding temperature threshold values. And the storage and visualization module is used for generating a use record and putting the use record into a historical log, and displaying the temperature of each charging grid in the charging cabinet through a large visual screen.

Description

Technical Field

[0001] The present invention relates to the technical field of charging management, and in particular to a system and method for monitoring abnormality of power charging data based on big data. Background Art

[0002] As power sources are increasingly used in modern society, people's demand for intelligent and automated charging equipment continues to increase. Charging cabinets can provide convenient charging services while ensuring the charging process is safe and reliable. Research on the design, function and safety of charging cabinets can improve user satisfaction with charging facilities and promote the promotion and use of clean energy.

[0003] At present, the temperature supervision of charging cabinets usually adopts the combination of built-in software and hardware systems and heat dissipation devices to cool down, which has certain disadvantages. For example: 1. The built-in software and hardware systems of the charging cabinet often generate a lot of heat during operation, which will increase the burden on the heat dissipation device. At the same time, due to the limitations of the hardware itself, it cannot process a large amount of data, resulting in performance shortages and insufficient intelligence. 2. Due to the heat dissipation design, there will be heat conduction between the charging grids. The temperature generated by the battery placed on the bottom of the charging grid will have different degrees of temperature impact on the adjacent charging grids in different directions. When multiple adjacent charging grids are running at the same time, a large amount of heat will be stacked in a short time, causing the pressure of the heat dissipation system to double. 3. Different users have different requirements for the charging time of the charging cabinet, but they all need to get a fully charged battery. If the highest power charging protocol supported by the battery is used for charging, it will cause a sudden increase in temperature in a short time and waste of heat dissipation resources for charging grids that do not require high charging time. Therefore, at this stage, a more efficient and intelligent power charging management technology solution is needed to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a power charging data anomaly monitoring system and method based on big data to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for monitoring abnormal power charging data based on big data, the method comprising the following steps:

[0006] S100, collecting historical logs and protocol comparison tables, as well as the location distribution map, temperature change map and operation data inside the charging cabinet, and the data center receives the charging request transmitted by the charging cabinet in real time.

[0007] S200. The data center analyzes the status of all charging grids in the charging cabinet, marks the charging grids in the idle state and sets the directly affected objects, calculates the interference coefficient of each directly affected object and the impact index of the marked charging grid, transmits the information of the marked charging grid with the smallest impact index back to the charging cabinet, and controls the grid door to open in response to the charging request.

[0008] S300. Calculate the temperature threshold of each operating charging grid through the interference coefficient, determine whether the current temperature of each operating charging grid is abnormal, and obtain the debugging protocol according to the protocol comparison table under abnormal conditions, transmit the debugging protocol to the charging cabinet, and the charging cabinet switches the charging protocol of the abnormal charging grid according to the debugging protocol to make the temperature normal.

[0009] S400, after the charging grid is used, the usage record is automatically generated and uploaded to the historical log of the data center; the temperature changes of each charging grid in the charging cabinet are displayed in real time through the large visual screen of the data center.

[0010] After the user scans the QR code of the charging cabinet and fills in the information, a charging request is automatically generated. The data center automatically obtains the historical log, protocol comparison table, location distribution map, temperature change map and operation data of the corresponding charging cabinet based on the charging request information, and makes intelligent recommendations for charging cabinets after analyzing this information.

[0011] In S100, the history log includes all usage records of the charging cabinet, and each usage record includes the identifier and performance parameters of the battery, the charging grid, the charging information and the time period. The charging information includes the charging temperature and the charging protocol. The charging temperature refers to the average temperature during the charging time, and the charging protocol refers to the charging protocol used during the charging time. The time period refers to the time from putting the battery in to taking the battery out when using the charging grid. The protocol comparison table includes all the charging protocols supported by each battery, and each charging protocol has its own power. The location distribution diagram refers to the location distribution diagram of all charging grids in the charging cabinet. The temperature change diagram refers to the relationship diagram of the internal temperature of each charging grid over time. The operating data refers to the real-time temperature and status of each charging grid in the charging cabinet. The temperature refers to the temperature inside the charging grid, and the status includes running and idle.

[0012] The performance parameters of the battery include capacity, voltage, energy density, cycle life, charging efficiency, internal resistance and maximum charging current. When the battery is fully charged in the charging grid, it will automatically stop charging, so the time period in the usage record is not necessarily the same as the charging time period.

[0013] The charging protocol refers to the communication protocol between the charger and the battery during the battery charging process, including the voltage and current values ​​output by the charger. The power of charging is different when using different charging protocols, and the charging protocol ensures the safe charging of the battery. During the charging process of the battery, the charger in the charging grid can automatically switch the charging protocol. If the charging protocol is not switched within the charging time, there is only one charging protocol. If the charging protocol is switched, there are multiple charging protocols, and each charging protocol only includes the time period in which it is used within the charging time.

[0014] In S200, the specific steps are as follows:

[0015] S201. When the data center receives a charging request, it obtains the status of all charging grids under the corresponding charging cabinet, marks all charging grids in idle state, and adds the charging grids BD corresponding to the marked charging grids in the location distribution map. n The charging grids in the running state adjacent to each other in any direction are BD n A usage time period set is established for each charging grid, and the usage records corresponding to each charging grid are retrieved from the historical log, and the time periods in these usage records are placed in the corresponding usage time period set.

[0016] The design of each charging compartment under the charging cabinet is a quadrilateral for daily use, so there are adjacent directions in the top, bottom, left and right. When the charging compartment is at the top or edge of the charging cabinet, there are only two or three adjacent directions.

[0017] S202: Establish an impact time period set for each directly affected object, obtain all time periods in the usage time period set of the directly affected object, filter out time periods that do not overlap with the usage time period set of the corresponding marked charging grid, and put all these time periods into the corresponding impact time period set. After all directly affected objects have been filtered, the time periods that overlap between the directly affected objects under the marked charging grid are deleted from their respective impact time period sets.

[0018] The purpose of filtering time periods is to ensure that the marked charging grid and the directly affected object are not in operation at the same time in any time period in the affected time period set. The purpose of deleting time periods is to ensure that no two directly affected objects are in operation at the same time in any time period in the affected time period set. Ensure that only one directly affected object has an impact on the temperature of the marked charging grid within the time period to avoid the temperature of multiple directly affected objects affecting each other.

[0019] S203. Calculate the interference coefficient of the directly affected object through all time periods in the affected time period set, calculate the influence index of the corresponding marked charging grid according to the interference coefficient of each directly affected object, and transmit the marked charging grid information with the smallest influence index back to the charging cabinet. After receiving the information, the charging cabinet opens the grid door of the corresponding charging cabinet to respond to the charging request.

[0020] In S203, the specific steps are as follows:

[0021] S203-1. Obtain all time periods in the impact time period set, set the interval length L, divide the time period length by L, and round up to obtain the number of sampling points S in the corresponding time period. Mark all time periods in the impact time period set in the temperature change diagram of the directly affected object and the corresponding marked charging grid, and evenly set S sampling points in each marked time period.

[0022] S203-2. Calculate the temperature difference TP between the directly affected object and the corresponding marked charging grid at the corresponding time of the sampling point i , substitute into the formula to calculate the interference coefficient GRX for each marked time period sj , sum up the interference coefficients of all marked time periods under the same direct impact object to obtain the interference coefficient GRX of the corresponding direct impact object dx The formula is as follows:

[0023]

[0024] In the formula, α is a constant, TP ave TP of all sampling points in the marked time period i The average value of .

[0025] The interference coefficient indicates the degree of influence of the object directly affecting the temperature of the marked charging grid during the marked time period.

[0026] S203-3. Analyze the charging grid BD in the position distribution map n The status of all charging grids in the up, down, left, and right directions, and the BD n Non-adjacent charging grids in operation are used as BD n Calculate the number of charging grids JG between each indirectly affected object and the corresponding marked charging grid p , substitute into the formula to calculate the impact index of the marked charging grid:

[0027]

[0028] In the formula, YXZ is the influence index of the marked charging grid, f, γ and β are constants, r is the number of indirectly affected objects, m is the number of directly affected objects, is the interference coefficient of the kth directly affecting object.

[0029] Indirectly affected objects refer to the charging grids that are closest to the marked charging grids in the four directions of up, down, left, and right except for the directly affected objects. The number of indirect affected objects is at most four and at least zero, and the same applies to directly affected objects.

[0030] In S300, the specific steps are as follows:

[0031] S301, the data center obtains the identifier BSF of the battery inside the charging grid in the running state e , retrieve BSF in the history log e Corresponding to all usage records, the charging temperatures in these usage records are summed up and the average value is calculated to obtain the basic temperature T base Identify the direct influencing objects of the running state charging grid and obtain the interference coefficient, substitute it into the formula to calculate the temperature threshold of each running state charging grid, obtain the current temperature of each running state charging grid, and mark the battery inside the running state charging grid whose current temperature is greater than the corresponding temperature threshold as abnormal. The formula is as follows:

[0032]

[0033] Where, T yz is the temperature threshold, x and v are constants, b is the number of directly affected objects, is the interference coefficient of the hth directly affecting object.

[0034] When the state of the charging grid changes, the directly affected objects of the running state charging grid also change, and the latest temperature threshold is recalculated.

[0035] S302: Establish a protocol set for each abnormal battery and obtain the identifier BSF of the abnormal battery u And the current charging protocol CX now , search for BSF in the protocol comparison table u All supported charging protocols, filter out those with power lower than CX now The charging protocol is put into the corresponding protocol set.

[0036] S303. The protocol set is adjusted according to the performance parameters of the abnormal battery, and a debugging protocol is obtained according to the adjusted protocol set analysis. The debugging protocol is transmitted to the charging cabinet. The charging cabinet switches the charging protocol of the abnormal charging grid according to the debugging protocol and continues to upload the temperature information of each charging grid. The data center continuously adjusts the charging protocol so that the temperature of each charging grid in the operating state under the charging cabinet is not greater than the corresponding temperature threshold.

[0037] In S303, the specific steps are as follows:

[0038] S303-1. Establish an electrochemical model in MATLAB based on the performance parameters of the abnormal battery to obtain the current power DL of the abnormal battery. now , the electrochemical model is based on DL now Calculate the time SC required for full charging after switching to each charging protocol in the corresponding protocol set cm , the current time T now Subtract the start time T of the time period in the latest usage record of each abnormal battery start Get the charging time SC ed , S.C. ed Add SC cm Get the estimated duration SC of each charging agreement yj .

[0039] S303-2, obtain all the usage records of the abnormal battery in the historical log, sum up the duration of the time periods in these usage records and calculate the average value to obtain the usage duration SC of the corresponding abnormal battery sy , delete SC from the protocol set of abnormal battery yj Greater than SC sy The charging protocol for each abnormal battery selects SC in the protocol set yj The minimum charging protocol is used as a debugging protocol.

[0040] S303-3, adjust the charging protocol of each abnormal battery corresponding to the charging grid to the debugging protocol, and set the waiting time T wait , using the abnormality judgment method in step S301, every time length T wait Obtain the temperature information of each charging grid in the running state under the primary charging cabinet and determine whether it is abnormal. If the result is no, cancel the abnormal mark of the corresponding battery. If the result is yes, enter step S302 to continue adjusting the charging protocol; when the abnormal battery DC am When the protocol set is empty, no charging protocol adjustment is performed and the generation adjustment set is established. am The directly affected objects of the corresponding charging grid are put into the generation adjustment set, and the directly affected objects with the largest interference coefficient in the generation adjustment set are selected as the objects to be adjusted. The charging protocol is adjusted for the objects to be adjusted until the temperature of the charging grids in all operating states is no greater than the corresponding temperature threshold.

[0041] The charging protocol is adjusted in a dynamic recursive manner. In the initial stage, only the abnormal battery A1 establishes a protocol set, and the temperature is adjusted by switching the charging protocol through the protocol set. If the protocol set of A1 is an empty set, it will continue to be adjusted through the directly affected object B1 with the largest interference coefficient. B1 will also establish a protocol set to switch the charging protocol to achieve temperature adjustment for A1. When the protocol set of B1 is also an empty set, and A1 is still abnormal, it will continue to establish a protocol set to switch the charging protocol through the directly affected object B2 with the second largest interference coefficient to achieve adjustment. If the protocol sets of all directly affected objects are empty sets and A1 is still abnormal, the directly affected object C1 with the largest interference coefficient of B1 will be called to establish a protocol set to switch the charging protocol to achieve temperature adjustment for A1. And so on, until the temperature of A1 is normal.

[0042] If the temperature of the charging grid corresponding to the abnormal battery still cannot be restored to normal, an early warning will be issued through the visual large screen and the problem will be handled manually. This technical solution can be applied on the basis of the existing heat dissipation system, saving the resources consumed by the heat dissipation system and improving the heat dissipation efficiency.

[0043] A power charging data anomaly monitoring system based on big data, the system comprises a data acquisition module, an intelligent analysis module, an anomaly management module and a storage and visualization module.

[0044] The data acquisition module is used to collect historical logs, protocol comparison tables, and location distribution maps, temperature change maps, and operating data of charging cabinets. When the intelligent analysis module receives a charging request in the data center, it calculates the impact index of each idle charging grid and controls the corresponding charging cabinet to open the idle charging grid with the smallest impact index to respond to the charging request. The abnormal management module is used to calculate the temperature threshold of each running charging grid and determine whether it is abnormal. If it is abnormal, the charging protocol is readjusted according to the protocol comparison table until the temperature of all running charging grids is no greater than the corresponding temperature threshold. The storage and visualization module is used to generate usage records and upload them to the historical log of the data center, and display the temperature of each charging grid in the charging cabinet through a large visual screen.

[0045] The data collection module includes a history log collection unit, a chart information collection unit and an operation data collection unit.

[0046] The history log collection unit is used to collect all usage records of the charging cabinet. Each usage record includes the battery identifier and performance parameters, charging grid, charging information and time period; the charging information includes charging temperature and charging protocol.

[0047] The chart information collection unit is used to collect the location distribution map, temperature change map and protocol comparison table. The location distribution map refers to the location distribution map of all charging grids in the charging cabinet, the temperature change map refers to the relationship between the internal temperature of each charging grid and the time change, and the protocol comparison table includes all the charging protocols supported by each battery, and each charging protocol has its own power.

[0048] The operation data acquisition unit is used to collect the real-time temperature and status of each charging grid in the charging cabinet. The temperature refers to the temperature inside the charging grid, and the status includes operation and idle.

[0049] The intelligent analysis module includes an impact analysis unit and an intelligent recommendation unit.

[0050] The impact analysis unit is used to calculate the impact index.

[0051] First, when the data center receives a charging request, it obtains the status of all charging grids under the corresponding charging cabinet, marks all idle charging grids, and takes the charging grids in the running state adjacent to the marked charging grid in the location distribution map as its direct impact object. Each charging grid establishes a usage period set, and all time periods of the same charging grid usage records in the historical log are placed in the corresponding usage period set.

[0052] Secondly, each directly affected object establishes an impact time period set, obtains all time periods in the usage time period set of the directly affected object, filters out time periods that do not overlap with the usage time period set of the corresponding marked charging grid, and puts them all into the corresponding impact time period set, and deletes time periods that overlap between the impact time period sets of each directly affected object under each marked charging grid.

[0053] Finally, the interference coefficient of the directly affected object is calculated through all the time periods in the affected time period set, and the influence index of the corresponding marked charging grid is calculated according to the interference coefficient of each directly affected object.

[0054] The intelligent recommendation unit is used to respond to charging requests. The marked charging grid information with the smallest impact index is transmitted back to the charging cabinet, and the charging cabinet opens the grid door of the corresponding charging cabinet to respond to the charging request after receiving the information.

[0055] The abnormality management module includes an abnormality judgment unit and a protocol adjustment unit.

[0056] The abnormality judgment unit is used to identify abnormal situations.

[0057] First, the data center obtains the identifier BSF of the battery inside the charging grid in the running state e , BSF in the history log e The basic temperature T is obtained by summing up the charging temperatures in all corresponding usage records and calculating the average value. baseSecondly, identify the direct influencing objects of the running state charging grid and obtain the interference coefficient, and substitute it into the formula: Calculate the temperature threshold T of each charging grid in each operating state yz Finally, the battery in the operating state charging grid whose current temperature is greater than the corresponding temperature threshold is marked as abnormal. Among them, x and v are constants, b is the number of directly affected objects, is the interference coefficient of the hth directly affecting object.

[0058] The protocol adaptation unit is used to identify the debugging protocol.

[0059] First, establish a protocol set for each abnormal battery and obtain the identifier BSF of the abnormal battery. u And the current charging protocol CX now , search for BSF in the protocol comparison table u All supported charging protocols, filter out those with power lower than CX now The charging protocol is put into the corresponding protocol set.

[0060] Secondly, the protocol set is adjusted according to the performance parameters of the abnormal battery, and the debugging protocol is obtained according to the analysis of the adjusted protocol set.

[0061] Finally, the debugging protocol is transmitted to the charging cabinet. The charging cabinet switches the charging protocol of the abnormal charging grid according to the debugging protocol and continues to upload the temperature information of each charging grid. The data center continuously adjusts the charging protocol so that the temperature of each charging grid in the operating state under the charging cabinet is no greater than the corresponding temperature threshold.

[0062] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0063] 1. Rationalization setting: All data processing in this application is carried out in the data center. The charging cabinet does not need to be configured with software and hardware systems. It only needs to upload operating data and receive instructions transmitted from the data center. Compared with the traditional technology of installing a large number of hardware devices in the charging cabinet, it not only severely limits the data processing capacity, but also puts pressure on the heat dissipation of the charging cabinet.

[0064] 2. Intelligent recommendation: In this application, after receiving a charging request, all idle charging grids are analyzed. The impact index is calculated by analyzing the degree of impact caused by the directly affected objects and the distance to the indirectly affected objects in all directions. The corresponding charging grid is opened according to the impact index to respond to the charging request. Compared with the random or sequential recommendation of traditional technology, it is more intelligent.

[0065] 3. Efficient adjustment: In this application, the temperature threshold is dynamically calculated through the interference index that directly affects the object and determines whether it is abnormal; the appropriate charging protocol is screened out according to the average usage time of the abnormal battery in the historical log, and the cooling is achieved by adjusting its own charging protocol or the charging protocol of the directly affected object, which is more efficient than traditional technology.

[0066] In summary, compared with traditional technologies, the present invention has the advantages of rational setting, intelligent recommendation and efficient adjustment, and can improve the temperature management efficiency during the power charging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0068] Figure 1 It is a flow chart of a method for monitoring abnormal power charging data based on big data of the present invention;

[0069] Figure 2 It is a structural schematic diagram of a power charging data anomaly monitoring system based on big data of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0071] See also Figure 1 The present invention provides a method for monitoring abnormal power charging data based on big data, the method comprising the following steps:

[0072] S100, collecting historical logs and protocol comparison tables, as well as the location distribution map, temperature change map and operation data inside the charging cabinet, and the data center receives the charging request transmitted by the charging cabinet in real time.

[0073] S200. The data center analyzes the status of all charging grids in the charging cabinet, marks the charging grids in the idle state and sets the directly affected objects, calculates the interference coefficient of each directly affected object and the impact index of the marked charging grid, transmits the information of the marked charging grid with the smallest impact index back to the charging cabinet, and controls the grid door to open in response to the charging request.

[0074] S300. Calculate the temperature threshold of each operating charging grid through the interference coefficient, determine whether the current temperature of each operating charging grid is abnormal, and obtain the debugging protocol according to the protocol comparison table under abnormal conditions, transmit the debugging protocol to the charging cabinet, and the charging cabinet switches the charging protocol of the abnormal charging grid according to the debugging protocol to make the temperature normal.

[0075] S400, after the charging grid is used, the usage record is automatically generated and uploaded to the historical log of the data center; the temperature changes of each charging grid in the charging cabinet are displayed in real time through the large visual screen of the data center.

[0076] After the user scans the QR code of the charging cabinet and fills in the information, a charging request is automatically generated. The data center automatically obtains the historical log, protocol comparison table, location distribution map, temperature change map and operation data of the corresponding charging cabinet based on the charging request information, and makes intelligent recommendations for charging cabinets after analyzing this information.

[0077] In S100, the history log includes all usage records of the charging cabinet, and each usage record includes the identifier and performance parameters of the battery, the charging grid, the charging information and the time period. The charging information includes the charging temperature and the charging protocol. The charging temperature refers to the average temperature during the charging time, and the charging protocol refers to the charging protocol used during the charging time. The time period refers to the time from putting the battery in to taking the battery out when using the charging grid. The protocol comparison table includes all the charging protocols supported by each battery, and each charging protocol has its own power. The location distribution diagram refers to the location distribution diagram of all charging grids in the charging cabinet. The temperature change diagram refers to the relationship diagram of the internal temperature of each charging grid over time. The operating data refers to the real-time temperature and status of each charging grid in the charging cabinet. The temperature refers to the temperature inside the charging grid, and the status includes running and idle.

[0078] The performance parameters of the battery include capacity, voltage, energy density, cycle life, charging efficiency, internal resistance and maximum charging current. When the battery is fully charged in the charging grid, it will automatically stop charging, so the time period in the usage record is not necessarily the same as the charging time period.

[0079] The charging protocol refers to the communication protocol between the charger and the battery during the battery charging process, including the voltage and current values ​​output by the charger. The power of charging is different when using different charging protocols, and the charging protocol ensures the safe charging of the battery. During the charging process of the battery, the charger in the charging grid can automatically switch the charging protocol. If the charging protocol is not switched within the charging time, there is only one charging protocol. If the charging protocol is switched, there are multiple charging protocols, and each charging protocol only includes the time period in which it is used within the charging time.

[0080] In S200, the specific steps are as follows:

[0081] S201. When the data center receives a charging request, it obtains the status of all charging grids under the corresponding charging cabinet, marks all charging grids in idle state, and adds the charging grids BD corresponding to the marked charging grids in the location distribution map. n The charging grids in the running state adjacent to each other in any direction are BD n A usage time period set is established for each charging grid, and the usage records corresponding to each charging grid are retrieved from the historical log, and the time periods in these usage records are placed in the corresponding usage time period set.

[0082] The design of each charging compartment under the charging cabinet is a quadrilateral for daily use, so there are adjacent directions in the top, bottom, left and right. When the charging compartment is at the top or edge of the charging cabinet, there are only two or three adjacent directions.

[0083] S202: Establish an impact time period set for each directly affected object, obtain all time periods in the usage time period set of the directly affected object, filter out time periods that do not overlap with the usage time period set of the corresponding marked charging grid, and put all these time periods into the corresponding impact time period set. After all directly affected objects have been filtered, the time periods that overlap between the directly affected objects under the marked charging grid are deleted from their respective impact time period sets.

[0084] The purpose of filtering time periods is to ensure that the marked charging grid and the directly affected object are not in operation at the same time in any time period in the affected time period set. The purpose of deleting time periods is to ensure that no two directly affected objects are in operation at the same time in any time period in the affected time period set. Ensure that only one directly affected object has an impact on the temperature of the marked charging grid within the time period to avoid the temperature of multiple directly affected objects affecting each other.

[0085] S203. Calculate the interference coefficient of the directly affected object through all time periods in the affected time period set, calculate the influence index of the corresponding marked charging grid according to the interference coefficient of each directly affected object, and transmit the marked charging grid information with the smallest influence index back to the charging cabinet. After receiving the information, the charging cabinet opens the grid door of the corresponding charging cabinet to respond to the charging request.

[0086] In S203, the specific steps are as follows:

[0087] S203-1. Obtain all time periods in the impact time period set, set the interval length L, divide the time period length by L, and round up to obtain the number of sampling points S in the corresponding time period. Mark all time periods in the impact time period set in the temperature change diagram of the directly affected object and the corresponding marked charging grid, and evenly set S sampling points in each marked time period.

[0088] S203-2. Calculate the temperature difference TP between the directly affected object and the corresponding marked charging grid at the corresponding time of the sampling point i , substitute into the formula to calculate the interference coefficient GRX for each marked time period sj , sum up the interference coefficients of all marked time periods under the same direct impact object to obtain the interference coefficient GRX of the corresponding direct impact object dx The formula is as follows:

[0089]

[0090] In the formula, α is a constant, TP ave TP of all sampling points in the marked time period i The average value of .

[0091] The interference coefficient indicates the degree of influence of the object directly affecting the temperature of the marked charging grid during the marked time period.

[0092] S203-3. Analyze the charging grid BD in the position distribution map n The status of all charging grids in the up, down, left, and right directions, and the BD n Non-adjacent charging grids in operation are used as BD n Calculate the number of charging grids JG between each indirectly affected object and the corresponding marked charging grid p , substitute into the formula to calculate the impact index of the marked charging grid:

[0093]

[0094] In the formula, YXZ is the influence index of the marked charging grid, f, γ and β are constants, r is the number of indirectly affected objects, m is the number of directly affected objects, is the interference coefficient of the kth directly affecting object.

[0095] Indirectly affected objects refer to the charging grids that are closest to the marked charging grids in the four directions of up, down, left, and right except for the directly affected objects. The number of indirect affected objects is at most four and at least zero, and the same applies to directly affected objects.

[0096] In S300, the specific steps are as follows:

[0097] S301, the data center obtains the identifier BSF of the battery inside the charging grid in the running state e , retrieve BSF in the history log e Corresponding to all usage records, the charging temperatures in these usage records are summed up and the average value is calculated to obtain the basic temperature T baseIdentify the direct influencing objects of the running state charging grid and obtain the interference coefficient, substitute it into the formula to calculate the temperature threshold of each running state charging grid, obtain the current temperature of each running state charging grid, and mark the battery inside the running state charging grid whose current temperature is greater than the corresponding temperature threshold as abnormal. The formula is as follows:

[0098]

[0099] Where, T yz is the temperature threshold, x and v are constants, b is the number of directly affected objects, GRX h dx is the interference coefficient of the hth directly affecting object.

[0100] When the state of the charging grid changes, the directly affected objects of the running state charging grid also change, and the latest temperature threshold is recalculated.

[0101] S302: Establish a protocol set for each abnormal battery and obtain the identifier BSF of the abnormal battery u And the current charging protocol CX now , search for BSF in the protocol comparison table u All supported charging protocols, filter out those with power lower than CX now The charging protocol is put into the corresponding protocol set.

[0102] S303. The protocol set is adjusted according to the performance parameters of the abnormal battery, and a debugging protocol is obtained according to the adjusted protocol set analysis. The debugging protocol is transmitted to the charging cabinet. The charging cabinet switches the charging protocol of the abnormal charging grid according to the debugging protocol and continues to upload the temperature information of each charging grid. The data center continuously adjusts the charging protocol so that the temperature of each charging grid in the operating state under the charging cabinet is not greater than the corresponding temperature threshold.

[0103] In S303, the specific steps are as follows:

[0104] S303-1. Establish an electrochemical model in MATLAB based on the performance parameters of the abnormal battery to obtain the current power DL of the abnormal battery. now , the electrochemical model is based on DL now Calculate the time SC required for full charging after switching to each charging protocol in the corresponding protocol set cm , the current time T now Subtract the start time T of the time period in the latest usage record of each abnormal battery start Get the charging time SC ed , S.C. ed Add SC cm Get the estimated duration SC of each charging agreement yj.

[0105] S303-2, obtain all the usage records of the abnormal battery in the historical log, sum up the duration of the time periods in these usage records and calculate the average value to obtain the usage duration SC of the corresponding abnormal battery sy , delete SC from the protocol set of abnormal battery yj Greater than SC sy The charging protocol for each abnormal battery selects SC in the protocol set yj The minimum charging protocol is used as a debugging protocol.

[0106] S303-3, adjust the charging protocol of each abnormal battery corresponding to the charging grid to the debugging protocol, and set the waiting time T wait , using the abnormality judgment method in step S301, every time length T wait Obtain the temperature information of each charging grid in the running state under the primary charging cabinet and determine whether it is abnormal. If the result is no, cancel the abnormal mark of the corresponding battery. If the result is yes, enter step S302 to continue adjusting the charging protocol; when the abnormal battery DC am When the protocol set is empty, no charging protocol adjustment is performed and the generation adjustment set is established. am The directly affected objects of the corresponding charging grid are put into the generation adjustment set, and the directly affected objects with the largest interference coefficient in the generation adjustment set are selected as the objects to be adjusted. The charging protocol is adjusted for the objects to be adjusted until the temperature of the charging grids in all operating states is no greater than the corresponding temperature threshold.

[0107] The charging protocol is adjusted in a dynamic recursive manner. In the initial stage, only the abnormal battery A1 establishes a protocol set, and the temperature is adjusted by switching the charging protocol through the protocol set. If the protocol set of A1 is an empty set, it will continue to be adjusted through the directly affected object B1 with the largest interference coefficient. B1 will also establish a protocol set to switch the charging protocol to achieve temperature adjustment for A1. When the protocol set of B1 is also an empty set, and A1 is still abnormal, it will continue to establish a protocol set to switch the charging protocol through the directly affected object B2 with the second largest interference coefficient to achieve adjustment. If the protocol sets of all directly affected objects are empty sets and A1 is still abnormal, the directly affected object C1 with the largest interference coefficient of B1 will be called to establish a protocol set to switch the charging protocol to achieve temperature adjustment for A1. And so on, until the temperature of A1 is normal.

[0108] If the temperature of the charging grid corresponding to the abnormal battery still cannot be restored to normal, an early warning will be issued through the visual large screen and the problem will be handled manually. This technical solution can be applied on the basis of the existing heat dissipation system, saving the resources consumed by the heat dissipation system and improving the heat dissipation efficiency.

[0109] See also Figure 2The present invention provides a power charging data anomaly monitoring system based on big data, which includes a data acquisition module, an intelligent analysis module, an anomaly management module and a storage and visualization module.

[0110] The data acquisition module is used to collect historical logs, protocol comparison tables, and location distribution maps, temperature change maps, and operating data of charging cabinets. When the intelligent analysis module receives a charging request in the data center, it calculates the impact index of each idle charging grid and controls the corresponding charging cabinet to open the idle charging grid with the smallest impact index to respond to the charging request. The abnormal management module is used to calculate the temperature threshold of each running charging grid and determine whether it is abnormal. If it is abnormal, the charging protocol is readjusted according to the protocol comparison table until the temperature of all running charging grids is no greater than the corresponding temperature threshold. The storage and visualization module is used to generate usage records and upload them to the historical log of the data center, and display the temperature of each charging grid in the charging cabinet through a large visual screen.

[0111] The data collection module includes a history log collection unit, a chart information collection unit and an operation data collection unit.

[0112] The history log collection unit is used to collect all usage records of the charging cabinet. Each usage record includes the battery identifier and performance parameters, charging grid, charging information and time period; the charging information includes charging temperature and charging protocol.

[0113] The chart information collection unit is used to collect the location distribution map, temperature change map and protocol comparison table. The location distribution map refers to the location distribution map of all charging grids in the charging cabinet, the temperature change map refers to the relationship between the internal temperature of each charging grid and the time change, and the protocol comparison table includes all the charging protocols supported by each battery, and each charging protocol has its own power.

[0114] The operation data acquisition unit is used to collect the real-time temperature and status of each charging grid in the charging cabinet. The temperature refers to the temperature inside the charging grid, and the status includes operation and idle.

[0115] The intelligent analysis module includes an impact analysis unit and an intelligent recommendation unit.

[0116] The impact analysis unit is used to calculate the impact index.

[0117] First, when the data center receives a charging request, it obtains the status of all charging grids under the corresponding charging cabinet, marks all idle charging grids, and takes the charging grids in the running state adjacent to the marked charging grid in the location distribution map as its direct impact object. Each charging grid establishes a usage period set, and all time periods of the same charging grid usage records in the historical log are placed in the corresponding usage period set.

[0118] Secondly, each directly affected object establishes an impact time period set, obtains all time periods in the usage time period set of the directly affected object, filters out time periods that do not overlap with the usage time period set of the corresponding marked charging grid, and puts them all into the corresponding impact time period set, and deletes time periods that overlap between the impact time period sets of each directly affected object under each marked charging grid.

[0119] Finally, the interference coefficient of the directly affected object is calculated through all the time periods in the affected time period set, and the influence index of the corresponding marked charging grid is calculated according to the interference coefficient of each directly affected object.

[0120] The intelligent recommendation unit is used to respond to charging requests. The marked charging grid information with the smallest impact index is transmitted back to the charging cabinet, and the charging cabinet opens the grid door of the corresponding charging cabinet to respond to the charging request after receiving the information.

[0121] The abnormality management module includes an abnormality judgment unit and a protocol adjustment unit.

[0122] The abnormality judgment unit is used to identify abnormal situations.

[0123] First, the data center obtains the identifier BSF of the battery inside the charging grid in the running state e , BSF in the history log e The basic temperature T is obtained by summing up the charging temperatures in all corresponding usage records and calculating the average value. base Secondly, identify the direct influencing objects of the running state charging grid and obtain the interference coefficient, and substitute it into the formula: Calculate the temperature threshold T of each charging grid in each operating state yz Finally, the battery in the operating state charging grid whose current temperature is greater than the corresponding temperature threshold is marked as abnormal. Among them, x and v are constants, b is the number of directly affected objects, is the interference coefficient of the hth directly affecting object.

[0124] The protocol adaptation unit is used to identify the debugging protocol.

[0125] First, establish a protocol set for each abnormal battery and obtain the identifier BSF of the abnormal battery. u And the current charging protocol CX now , search for BSF in the protocol comparison table u All supported charging protocols, filter out those with power lower than CX now The charging protocol is put into the corresponding protocol set.

[0126] Secondly, the protocol set is adjusted according to the performance parameters of the abnormal battery, and the debugging protocol is obtained according to the analysis of the adjusted protocol set.

[0127] Finally, the debugging protocol is transmitted to the charging cabinet. The charging cabinet switches the charging protocol of the abnormal charging grid according to the debugging protocol and continues to upload the temperature information of each charging grid. The data center continuously adjusts the charging protocol so that the temperature of each charging grid in the operating state under the charging cabinet is no greater than the corresponding temperature threshold.

[0128] Example 1: Assume that there are 5 sampling points in a certain marked time period, and the temperature of the object and the corresponding marked charging grid directly affected by each sampling point at the corresponding time is as follows:

[0129] Sampling point 1: directly affecting the object 25℃, marking the charging grid 18℃, temperature difference: 7℃;

[0130] Sampling point 2: directly affecting the object 30℃, marking the charging grid 22℃, temperature difference: 8℃;

[0131] Sampling point 3: directly affected object 33℃, marked charging grid 24℃, temperature difference: 9℃;

[0132] Sampling point 4: directly affected object 35℃, marked charging grid 26℃, temperature difference: 9℃;

[0133] Sampling point 5: directly affected object 36℃, marked charging grid 26℃, temperature difference: 10℃;

[0134] The average temperature difference of all sampling points is 8.6°C. When α is 1, substitute it into the formula to calculate the interference coefficient of the marked time period:

[0135] Interference coefficient:

[0136] Then the interference coefficient of the marked time period is 21.29.

[0137] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0138] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for monitoring abnormal power charging data based on big data, characterized in that: The method comprises the following steps: S100, collecting historical logs and protocol comparison tables, as well as the location distribution map, temperature change map and operation data inside the charging cabinet, and the data center receives the charging request transmitted by the charging cabinet in real time; S200, the data center analyzes the status of all charging grids in the charging cabinet, marks the charging grids in the idle state and sets the directly affected objects, calculates the interference coefficient of each directly affected object and the impact index of the marked charging grid, transmits the information of the marked charging grid with the smallest impact index back to the charging cabinet, and controls the grid door to open in response to the charging request; S300, calculate the temperature threshold of each running charging grid respectively through the interference coefficient, determine whether the current temperature of each running charging grid is abnormal, and obtain the debugging protocol according to the protocol comparison table in the abnormal case, transmit the debugging protocol to the charging cabinet, and the charging cabinet switches the charging protocol of the abnormal charging grid according to the debugging protocol to make the temperature normal; S400, after the charging grid is used, the usage record is automatically generated and uploaded to the historical log of the data center; the temperature changes of each charging grid in the charging cabinet are displayed in real time through the large visual screen of the data center.

2. According to the method for monitoring abnormal power charging data based on big data in claim 1, it is characterized by: In S100, the history log includes all usage records of the charging cabinet, each usage record includes the identifier and performance parameters of the battery, the charging grid, the charging information and the time period; the charging information includes the charging temperature and the charging protocol, the charging temperature refers to the average temperature during the charging time, and the charging protocol refers to the charging protocol adopted during the charging time; the time period refers to the time from putting the battery in to taking the battery out when using the charging grid; the protocol comparison table includes all the charging protocols supported by each battery, and each charging protocol has its own power; the location distribution map refers to the location distribution map of all the charging grids in the charging cabinet; the temperature change map refers to the relationship map of the internal temperature of each charging grid over time; the operation data refers to the real-time temperature and status of each charging grid in the charging cabinet, the temperature refers to the temperature inside the charging grid, and the status includes operation and idle.

3. The method for monitoring abnormal power charging data based on big data according to claim 2, characterized in that: In S200, the specific steps are as follows: S201. When the data center receives a charging request, it obtains the status of all charging grids under the corresponding charging cabinet, marks all charging grids in idle state, and adds the charging grids BD corresponding to the marked charging grids in the location distribution map. n The charging grids in the running state adjacent to each other in any direction are BD n directly affected objects; establish a usage time period set for each charging grid, retrieve the usage records corresponding to each charging grid in the historical log, and put the time periods in these usage records into the corresponding usage time period set; S202, establishing an impact time period set for each directly affected object, obtaining all time periods in the usage time period set of the directly affected object, screening out time periods that do not overlap with the usage time period set of the corresponding marked charging grid, and putting all these time periods into the corresponding impact time period set; After all directly affected objects have been screened, the time periods with overlapping time between the directly affected objects under the marked charging grid are deleted from the respective affected time period sets; S203. Calculate the interference coefficient of the directly affected object through all time periods in the affected time period set, calculate the influence index of the corresponding marked charging grid according to the interference coefficient of each directly affected object, and transmit the marked charging grid information with the smallest influence index back to the charging cabinet. After receiving the information, the charging cabinet opens the grid door of the corresponding charging cabinet to respond to the charging request.

4. The method for monitoring abnormal power charging data based on big data according to claim 3 is characterized in that: In S203, the specific steps are as follows: S203-1. Obtain all time periods in the impact time period set, set the interval length L, divide the time period by L and round up to obtain the number of sampling points S for the corresponding time period; mark all time periods in the impact time period set in the temperature change diagram of the directly affected object and the corresponding marked charging grid, and evenly set S sampling points in each marked time period; S203-2. Calculate the temperature difference TP between the directly affected object and the corresponding marked charging grid at the corresponding time of the sampling point i , substitute into the formula to calculate the interference coefficient GRX for each marked time period sj , sum up the interference coefficients of all marked time periods under the same direct impact object to obtain the interference coefficient GRX of the corresponding direct impact object dx ; The formula is as follows: In the formula, α is a constant, TP ave TP of all sampling points in the marked time period i The average value of S203-3. Analyze the charging grid BD in the position distribution map n The status of all charging grids in the up, down, left, and right directions, and the BD n Non-adjacent charging grids in operation are used as BD n Indirectly affected objects; calculate the number of charging grids JG between each indirectly affected object and the corresponding marked charging grid p , substitute into the formula to calculate the impact index of the marked charging grid: In the formula, YXZ is the influence index of the marked charging grid, f, γ and β are constants, r is the number of indirectly affected objects, m is the number of directly affected objects, is the interference coefficient of the kth directly affecting object.

5. The method for monitoring abnormal power charging data based on big data according to claim 3 is characterized in that: In S300, the specific steps are as follows: S301, the data center obtains the identifier BSF of the battery inside the charging grid in the running state e , retrieve BSF in the history log e Corresponding to all usage records, the charging temperatures in these usage records are summed up and the average value is calculated to obtain the basic temperature T base ; Identify the direct influencing objects of the running state charging grid and obtain the interference coefficient, substitute it into the formula to calculate the temperature threshold of each running state charging grid, obtain the current temperature of each running state charging grid, and mark the battery inside the running state charging grid whose current temperature is greater than the corresponding temperature threshold as abnormal; the formula is as follows: Where, T yz is the temperature threshold, x and v are constants, b is the number of directly affected objects, is the interference coefficient of the hth directly affecting object; S302: Establish a protocol set for each abnormal battery and obtain the identifier BSF of the abnormal battery u And the current charging protocol CX now , search for BSF in the protocol comparison table u All supported charging protocols, filter out those with power lower than CX now The charging protocol is put into the corresponding protocol set; S303. The protocol set is adjusted according to the performance parameters of the abnormal battery, and a debugging protocol is obtained according to the adjusted protocol set analysis. The debugging protocol is transmitted to the charging cabinet. The charging cabinet switches the charging protocol of the abnormal charging grid according to the debugging protocol and continues to upload the temperature information of each charging grid. The data center continuously adjusts the charging protocol so that the temperature of each charging grid in the operating state under the charging cabinet is not greater than the corresponding temperature threshold.

6. The method for monitoring abnormal power charging data based on big data according to claim 5, characterized in that: In S303, the specific steps are as follows: S303-1. Establish an electrochemical model in MATLAB based on the performance parameters of the abnormal battery to obtain the current power DL of the abnormal battery. now , the electrochemical model is based on DL now Calculate the time SC required for full charging after switching to each charging protocol in the corresponding protocol set cm , the current time T now Subtract the start time T of the time period in the latest usage record of each abnormal battery start Get the charging time SC ed , S.C. ed Add SC cm Get the estimated duration SC of each charging agreement yj ; S303-2, obtain all the usage records of the abnormal battery in the historical log, sum up the duration of the time periods in these usage records and calculate the average value to obtain the usage duration SC of the corresponding abnormal battery sy , delete SC from the protocol set of abnormal battery yj Greater than SC sy The charging protocol for each abnormal battery selects SC in the protocol set yj The minimum charging protocol is used as a debugging protocol; S303-3, adjust the charging protocol of each abnormal battery corresponding to the charging grid to the debugging protocol, and set the waiting time T wait , using the abnormality judgment method in step S301, every time length T wait Obtain the temperature information of each charging grid in the running state under the primary charging cabinet and determine whether it is abnormal. If the result is no, cancel the abnormal mark of the corresponding battery. If the result is yes, enter step S302 to continue adjusting the charging protocol; when the abnormal battery DC am When the protocol set is empty, no charging protocol adjustment is performed and the generation adjustment set is established. am The directly affected objects of the corresponding charging grid are put into the generation adjustment set, and the directly affected objects with the largest interference coefficient in the generation adjustment set are selected as the objects to be adjusted. The charging protocol is adjusted for the objects to be adjusted until the temperature of the charging grids in all operating states is no greater than the corresponding temperature threshold.

7. A power charging data anomaly monitoring system based on big data, characterized by: The system includes a data acquisition module, an intelligent analysis module, an abnormality management module, and a storage and visualization module; The data acquisition module is used to collect historical logs, protocol comparison tables, and location distribution maps, temperature change maps, and operating data of charging cabinets; When the data center receives a charging request, the intelligent analysis module calculates the impact index of each idle charging grid and controls the corresponding charging cabinet to open the idle charging grid with the smallest impact index to respond to the charging request; the abnormal management module is used to calculate the temperature threshold of each running charging grid and determine whether it is abnormal. If it is abnormal, the charging protocol is readjusted according to the protocol comparison table until the temperature of all running charging grids is no greater than the corresponding temperature threshold; the storage and visualization module is used to generate usage records and upload them to the historical log of the data center, and display the temperature of each charging grid in the charging cabinet through a large visual screen.

8. The power charging data abnormality monitoring system based on big data according to claim 7 is characterized by: The data collection module includes a history log collection unit, a chart information collection unit and an operation data collection unit; The history log collection unit is used to collect all usage records of the charging cabinet. Each usage record includes the battery identifier and performance parameters, charging grid, charging information and time period; the charging information includes charging temperature and charging protocol; The chart information collection unit is used to collect the position distribution map, temperature change map and protocol comparison table; the position distribution map refers to the position distribution map of all charging grids in the charging cabinet, the temperature change map refers to the relationship between the internal temperature of each charging grid and the time, and the protocol comparison table includes all the charging protocols supported by each battery, and each charging protocol has its own power; The operation data acquisition unit is used to collect the real-time temperature and status of each charging grid in the charging cabinet. The temperature refers to the temperature inside the charging grid, and the status includes operation and idle.

9. The power charging data abnormality monitoring system based on big data according to claim 8 is characterized by: The intelligent analysis module includes an impact analysis unit and an intelligent recommendation unit; The impact analysis unit is used to calculate the impact index; First, when the data center receives a charging request, it obtains the status of all charging grids under the corresponding charging cabinet, marks all idle charging grids, and takes the charging grids in the operating state adjacent to the marked charging grid in the location distribution map as its directly affected objects; each charging grid establishes a usage period set, and puts all the time periods of the same charging grid usage records in the historical log into the corresponding usage period set; secondly, each directly affected object establishes an impact period set, obtains all the time periods in the usage period set of the directly affected object, filters out the time periods that do not overlap with the usage period set of the corresponding marked charging grid, and puts all of them into the corresponding impact period set, and deletes the time periods that overlap between the impact period sets of each directly affected object under each marked charging grid; finally, the interference coefficient of the corresponding directly affected object is calculated through all the time periods in the impact period set, and the impact index of the corresponding marked charging grid is calculated according to the interference coefficient of each directly affected object; The intelligent recommendation unit is used to respond to charging requests; the marked charging grid information with the smallest impact index is transmitted back to the charging cabinet, and after receiving the information, the charging cabinet opens the grid door of the corresponding charging cabinet to respond to the charging request.

10. The power charging data abnormality monitoring system based on big data according to claim 9, characterized in that: The abnormality management module includes an abnormality judgment unit and a protocol adjustment unit; The abnormality judgment unit is used to identify abnormal situations; First, the data center obtains the identifier BSF of the battery inside the charging grid in the running state e , BSF in the history log e The basic temperature T is obtained by summing up the charging temperatures in all corresponding usage records and calculating the average value. base ; Secondly, identify the direct influencing objects of the running state charging grid and obtain the interference coefficient, and substitute it into the formula: Calculate the temperature threshold T of each charging grid in each operating state yz ; Finally, the battery in the operating state charging grid whose current temperature is greater than the corresponding temperature threshold is marked as abnormal; where x and v are constants, b is the number of directly affected objects, is the interference coefficient of the hth directly affecting object; The protocol adjustment unit is used to identify the debugging protocol; First, establish a protocol set for each abnormal battery and obtain the identifier BSF of the abnormal battery. u And the current charging protocol CX now , search for BSF in the protocol comparison table u All supported charging protocols, filter out those with power lower than CX now The charging protocol of the abnormal battery is placed in the corresponding protocol set; secondly, the protocol set is adjusted according to the performance parameters of the abnormal battery, and the debugging protocol is obtained according to the adjusted protocol set analysis; finally, the debugging protocol is transmitted to the charging cabinet, and the charging cabinet switches the charging protocol of the abnormal charging grid according to the debugging protocol and continues to upload the temperature information of each charging grid. The data center continuously adjusts the charging protocol so that the temperature of each charging grid in the operating state of the charging cabinet is not greater than the corresponding temperature threshold.

Images