A system and method for monitoring abnormality of power charging data based on big data

Abstract

The present invention discloses a power charging data anomaly monitoring system and method based on big data, belonging to the field of charging management technology. The system includes a data acquisition module, an intelligent analysis module, an anomaly management module, and a storage and visualization module; the data acquisition module is used to collect historical logs, protocol comparison tables, and charging cabinet location distribution maps, temperature change maps, and operating data; the intelligent analysis module calculates the impact index of each idle charging grid when a charging request is received at the data center, and opens the idle charging grid with the smallest impact index to respond to the charging request; the anomaly management module is used to calculate the temperature threshold of each operating charging grid and determine whether it is abnormal. If an abnormality is found, the charging protocol is readjusted according to the protocol comparison table until the temperature of all operating charging grids is no greater than the corresponding temperature threshold; the storage and visualization module is used to generate usage records and store them in the historical log, and display the temperature of each charging grid in the charging cabinet on 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 supplies become increasingly widespread in modern society, demand for intelligent and automated charging equipment continues to grow. Charging cabinets can provide convenient charging services while ensuring a safe and reliable charging process. Research into the design, functionality, and safety of charging cabinets can improve user satisfaction with charging facilities and promote the widespread use of clean energy.

[0003] Currently, temperature regulation in charging cabinets typically uses built-in hardware and software systems combined with heat dissipation devices to reduce heat. This approach has certain drawbacks. For example: 1. The built-in hardware and software systems in charging cabinets often generate a large amount of heat during operation, which places a heavy burden on the heat dissipation devices. Furthermore, due to inherent hardware limitations, the systems cannot process large amounts of data, resulting in performance shortfalls and insufficient intelligence. 2. Due to the heat dissipation design, heat conduction occurs between charging compartments. The temperature generated by the batteries placed on the bottom of the compartments will have varying degrees of impact on the temperatures of adjacent compartments in different directions. When multiple adjacent compartments are operating simultaneously, a large amount of heat will accumulate in a short period of time, doubling the pressure on the heat dissipation system. 3. Different users have different charging time requirements for the charging cabinets, but all require fully charged batteries. If all users charge using the highest power protocol supported by the batteries, this will cause a short-term temperature surge and waste heat dissipation resources in the compartments that do not require a high charging time. Therefore, a more efficient and intelligent power charging management technology solution is currently needed to address the above issues. 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 solution: a method for monitoring abnormality of power charging data based on big data, the method comprising the following steps:

[0006] S100. Collect historical logs and protocol comparison tables, as well as location distribution maps, temperature change maps, and operating data inside the charging cabinet. The data center receives charging requests 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 idle charging grids and sets the directly affected objects, calculates the interference coefficient of each directly affected object and the impact index of the marked charging grid, and transmits the information of the marked charging grid with the smallest impact index back to the charging cabinet, controlling the grid door to open in response to the charging request.

[0008] S300. Calculate the temperature threshold of each operating charging grid using the interference coefficient to determine whether the current temperature of each operating charging grid is abnormal. Under abnormal conditions, obtain a debugging protocol based on the protocol comparison table, 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 restore the temperature to normal.

[0009] S400. After the charging grid is used, a 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 visual large 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. Each usage record includes the battery identifier and performance parameters, charging grid, charging information and time period. Charging information includes charging temperature and charging protocol. Charging temperature refers to the average temperature during the charging time, and charging protocol refers to the charging protocol used during the charging time. Time period refers to the time from inserting the battery to removing the battery when using the charging grid. The protocol comparison table includes all 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 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 period. 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 running and idle.

[0012] Battery performance parameters include capacity, voltage, energy density, cycle life, charging efficiency, internal resistance, and maximum charging current. The battery automatically stops charging when it reaches full capacity within the charging grid, so the time period in the usage record does not necessarily correspond to the charging period.

[0013] The charging protocol refers to the communication protocol between the charger and the battery during the charging process, specifically including the voltage and current values ​​output by the charger. Different charging protocols produce different charging powers, and the charging protocol ensures safe battery charging. During the charging process, the charger in the charging compartment can automatically switch between charging protocols. 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 applies to the time period during which it is used.

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

[0015] S201, when the data center receives a charging request, obtains the status of all charging grids under the corresponding charging cabinet, marks all charging grids in the 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 adjacent running state in any direction of up, down, left, and right are BD n Create a usage time period set for each charging grid, retrieve the usage records corresponding to each charging grid in the historical log, and add the time periods in these usage records to the corresponding usage time period set.

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

[0017] S202: Create an impact time period set for each directly affected object. Obtain all time periods in the usage time period set for the directly affected object, filter out time periods that do not overlap with the usage time period set for the corresponding marked charging grid, and add all of these time periods to the corresponding impact time period set. After all directly affected objects have been filtered, remove any time periods that overlap with the directly affected objects in the marked charging grid from their respective impact time period sets.

[0018] Filtering time periods ensures that the marked charging grid and its directly affected objects are not simultaneously operating within any time period in the affected time period set. Deleting time periods ensures that no two directly affected objects are simultaneously operating within any time period in the affected time period set. Ensure that only one directly affected object affects the temperature of the marked charging grid within a time period to prevent the temperatures of multiple directly affected objects from interfering with 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 within the impact period set, set an interval length L, divide the time period length by L, and round up to obtain the number of sampling points S for the corresponding time period. Mark all time periods within the impact period set on the temperature change graphs of the directly affected object and the corresponding marked charging grid, and evenly set S sampling points within 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] Where 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 directly influencing object on the temperature of the marked charging grid during the marked time period.

[0026] S203-3. Analyze the charging grid marked 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] Where 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 influencing object.

[0029] Indirectly affected objects are the closest running charging grids (below, above, below, left, and right) to the marked charging grid, excluding directly affected objects. The maximum number of indirectly affected objects is four, and the minimum is zero. 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 , search BSF in history log e For all usage records, the charging temperatures in these usage records are summed up and the average value is calculated to obtain the base temperature T base Identify the direct impact 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 influencing 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 currently used charging protocol CX now , search BSF in the protocol comparison table u All supported charging protocols, filtering out those with power less than CX now The charging protocol is placed in the corresponding protocol set.

[0036] S303. Adjust the protocol set based on the performance parameters of the abnormal battery, analyze the adjusted protocol set to obtain a debugging protocol, 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 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.

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

[0038] S303-1. Build an electrochemical model in MATLAB based on the performance parameters of the abnormal battery to obtain the current charge 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 , SC ed Add SC cm Get the estimated duration SC of each charging agreement yj .

[0039] S303-2. Obtain all usage records of the abnormal battery in the history 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 is selected from the SC yj The minimum charging protocol serves 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 operating state charging grid 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 placed in 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 of the objects to be adjusted is 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 dynamically and recursively. 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 A1's protocol set is empty, the adjustment will continue through B1, the directly affected object with the largest interference coefficient. B1 will also establish a protocol set to switch the charging protocol to adjust A1's temperature. If B1's protocol set is also empty, and A1 is still abnormal, the adjustment will continue through B2, the directly affected object with the second largest interference coefficient, to establish a protocol set to switch the charging protocol. If the protocol sets of all directly affected objects are empty and A1 is still abnormal, C1, the directly affected object with the largest interference coefficient of B1, will be called to establish a protocol set to switch the charging protocol to adjust A1's temperature. And so on, until A1's temperature returns to normal.

[0042] If the temperature of the charging grid of the abnormal battery still cannot be restored to normal, an early warning will be issued through the visual large screen and manual processing will be required. This technical solution can be applied based on the existing cooling system, saving the resources consumed by the cooling system and improving the cooling efficiency.

[0043] A power charging data anomaly monitoring system based on big data, the system includes 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, as well as location distribution maps, temperature change maps, and operating data of charging cabinets. When the intelligent analysis module receives a charging request at 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 in response to the charging request. The abnormality management module is used to calculate the temperature threshold of each operating 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 operating 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 acquisition module includes a history log acquisition unit, a chart information acquisition unit and an operation data acquisition unit.

[0046] The historical 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; charging information includes charging temperature and charging protocol.

[0047] The chart information collection unit is used to collect location distribution maps, temperature change maps, and protocol comparison tables. The location distribution map shows the location distribution of all charging compartments in the charging cabinet. The temperature change map shows the temperature change of each charging compartment over time. The protocol comparison table includes all charging protocols supported by each battery, each with its own power rating.

[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 running 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 in the corresponding charging cabinet, marks all idle grids, and identifies the operating grids adjacent to the marked grids in the location distribution map as directly affected. A usage time set is created for each grid, and all time periods recorded in the historical log for the same grid are placed into the corresponding usage time set.

[0052] Secondly, an impact time period set is established for each directly affected object, all time periods in the usage time period set of the directly affected object are obtained, and time periods that do not overlap with the usage time period set of the corresponding marked charging grid are filtered out and put into the corresponding impact time period set. Time periods that overlap between the impact time period sets of each directly affected object under each marked charging grid are deleted.

[0053] Finally, the interference coefficient of the directly affected object is calculated through all 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. It transmits the information of the charging grid with the smallest impact index back to the charging cabinet. After receiving the information, the charging cabinet opens the corresponding charging cabinet door to respond to the charging request.

[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 impact 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 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 influencing 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 abnormal battery identifier BSF u And the currently used charging protocol CX now , search BSF in the protocol comparison table u All supported charging protocols, filtering out those with power less than CX now The charging protocol is placed in 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 present invention has the following beneficial effects:

[0063] 1. Rationalized Setup: All data processing in this application is performed in the data center. The charging cabinet does not require any hardware or software configuration; it simply uploads operational data and receives instructions from the data center. This significantly limits data processing capabilities and complicates heat dissipation in traditional charging cabinets, which require extensive hardware installation.

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

[0065] 3. Efficient adjustment: In this application, the temperature threshold is dynamically calculated through the interference index of the direct impact object to determine whether it is abnormal; the appropriate charging protocol is screened out based on the average usage time of the abnormal battery in the historical log, and the temperature is reduced by adjusting the charging protocol itself or the charging protocol of the direct impact 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 This is a flow chart of a method for monitoring abnormality of power charging data based on big data according to 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

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

[0072] S100. Collect historical logs and protocol comparison tables, as well as location distribution maps, temperature change maps, and operating data inside the charging cabinet. The data center receives charging requests 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 idle charging grids and sets the directly affected objects, calculates the interference coefficient of each directly affected object and the impact index of the marked charging grid, and transmits the information of the marked charging grid with the smallest impact index back to the charging cabinet, controlling the grid door to open in response to the charging request.

[0074] S300. Calculate the temperature threshold of each operating charging grid using the interference coefficient to determine whether the current temperature of each operating charging grid is abnormal. Under abnormal conditions, obtain a debugging protocol based on the protocol comparison table, 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 restore the temperature to normal.

[0075] S400. After the charging grid is used, a 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 visual large 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. Each usage record includes the battery identifier and performance parameters, charging grid, charging information and time period. Charging information includes charging temperature and charging protocol. Charging temperature refers to the average temperature during the charging time, and charging protocol refers to the charging protocol used during the charging time. Time period refers to the time from inserting the battery to removing the battery when using the charging grid. The protocol comparison table includes all 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 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 period. 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 running and idle.

[0078] Battery performance parameters include capacity, voltage, energy density, cycle life, charging efficiency, internal resistance, and maximum charging current. The battery automatically stops charging when it reaches full capacity within the charging grid, so the time period in the usage record does not necessarily correspond to the charging period.

[0079] The charging protocol refers to the communication protocol between the charger and the battery during the charging process, specifically including the voltage and current values ​​output by the charger. Different charging protocols produce different charging powers, and the charging protocol ensures safe battery charging. During the charging process, the charger in the charging compartment can automatically switch between charging protocols. 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 applies to the time period during which it is used.

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

[0081] S201, when the data center receives a charging request, obtains the status of all charging grids under the corresponding charging cabinet, marks all charging grids in the 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 adjacent running state in any direction of up, down, left, and right are BD n Create a usage time period set for each charging grid, retrieve the usage records corresponding to each charging grid in the historical log, and add the time periods in these usage records to the corresponding usage time period set.

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

[0083] S202: Create an impact time period set for each directly affected object. Obtain all time periods in the usage time period set for the directly affected object, filter out time periods that do not overlap with the usage time period set for the corresponding marked charging grid, and add all of these time periods to the corresponding impact time period set. After all directly affected objects have been filtered, remove any time periods that overlap with the directly affected objects in the marked charging grid from their respective impact time period sets.

[0084] Filtering time periods ensures that the marked charging grid and its directly affected objects are not simultaneously operating within any time period in the affected time period set. Deleting time periods ensures that no two directly affected objects are simultaneously operating within any time period in the affected time period set. Ensure that only one directly affected object affects the temperature of the marked charging grid within a time period to prevent the temperatures of multiple directly affected objects from interfering with 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 within the impact period set, set an interval length L, divide the time period length by L, and round up to obtain the number of sampling points S for the corresponding time period. Mark all time periods within the impact period set on the temperature change graphs of the directly affected object and the corresponding marked charging grid, and evenly set S sampling points within 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] Where 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 directly influencing object on the temperature of the marked charging grid during the marked time period.

[0092] S203-3. Analyze the charging grid marked 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] Where 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 influencing object.

[0095] Indirectly affected objects are the closest running charging grids (below, above, below, left, and right) to the marked charging grid, excluding directly affected objects. The maximum number of indirectly affected objects is four, and the minimum is zero. 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 , search BSF in history log e For all usage records, the charging temperatures in these usage records are summed up and the average value is calculated to obtain the base temperature T baseIdentify the direct impact 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 influencing 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 currently used charging protocol CX now , search BSF in the protocol comparison table u All supported charging protocols, filtering out those with power less than CX now The charging protocol is placed in the corresponding protocol set.

[0102] S303. Adjust the protocol set based on the performance parameters of the abnormal battery, analyze the adjusted protocol set to obtain a debugging protocol, 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 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.

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

[0104] S303-1. Build an electrochemical model in MATLAB based on the performance parameters of the abnormal battery to obtain the current charge 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 , SC ed Add SC cm Get the estimated duration SC of each charging agreement yj.

[0105] S303-2. Obtain all usage records of the abnormal battery in the history 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 is selected from the SC yj The minimum charging protocol serves 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 operating state charging grid 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 placed in 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 of the objects to be adjusted is 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 dynamically and recursively. 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 A1's protocol set is empty, the adjustment will continue through B1, the directly affected object with the largest interference coefficient. B1 will also establish a protocol set to switch the charging protocol to adjust A1's temperature. If B1's protocol set is also empty, and A1 is still abnormal, the adjustment will continue through B2, the directly affected object with the second largest interference coefficient, to establish a protocol set to switch the charging protocol. If the protocol sets of all directly affected objects are empty and A1 is still abnormal, C1, the directly affected object with the largest interference coefficient of B1, will be called to establish a protocol set to switch the charging protocol to adjust A1's temperature. And so on, until A1's temperature returns to normal.

[0108] If the temperature of the charging grid of the abnormal battery still cannot be restored to normal, an early warning will be issued through the visual large screen and manual processing will be required. This technical solution can be applied based on the existing cooling system, saving the resources consumed by the cooling system and improving the cooling 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, as well as location distribution maps, temperature change maps, and operating data of charging cabinets. When the intelligent analysis module receives a charging request at 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 in response to the charging request. The abnormality management module is used to calculate the temperature threshold of each operating 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 operating 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 acquisition module includes a history log acquisition unit, a chart information acquisition unit and an operation data acquisition unit.

[0112] The historical 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; charging information includes charging temperature and charging protocol.

[0113] The chart information collection unit is used to collect location distribution maps, temperature change maps, and protocol comparison tables. The location distribution map shows the location distribution of all charging compartments in the charging cabinet. The temperature change map shows the temperature change of each charging compartment over time. The protocol comparison table includes all charging protocols supported by each battery, each with its own power rating.

[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 running 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 in the corresponding charging cabinet, marks all idle grids, and identifies the operating grids adjacent to the marked grids in the location distribution map as directly affected. A usage time set is created for each grid, and all time periods recorded in the historical log for the same grid are placed into the corresponding usage time set.

[0118] Secondly, an impact time period set is established for each directly affected object, all time periods in the usage time period set of the directly affected object are obtained, and time periods that do not overlap with the usage time period set of the corresponding marked charging grid are filtered out and put into the corresponding impact time period set. Time periods that overlap between the impact time period sets of each directly affected object under each marked charging grid are deleted.

[0119] Finally, the interference coefficient of the directly affected object is calculated through all 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. It transmits the information of the charging grid with the smallest impact index back to the charging cabinet. After receiving the information, the charging cabinet opens the corresponding charging cabinet door to respond to the charging request.

[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 impact 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 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 influencing 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 abnormal battery identifier BSF u And the currently used charging protocol CX now , search BSF in the protocol comparison table u All supported charging protocols, filtering out those with power less than CX now The charging protocol is placed in 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. The temperature of the object and the corresponding marked charging grid at each sampling point at the corresponding time is as follows:

[0129] Sampling point 1: directly affected object 25℃, marked charging grid 18℃, temperature difference: 7℃;

[0130] Sampling point 2: directly affected object 30℃, marked 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, the interference coefficient of the marked time period is calculated by substituting it into the formula:

[0135] Interference coefficient:

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

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

[0138] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are 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 will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A method for monitoring abnormality of power charging data based on big data, characterized by: The method comprises the following steps: S100: Collect historical logs and protocol comparison tables, as well as location distribution maps, temperature change maps, and operating data inside the charging cabinet. The data center receives charging requests transmitted by the charging cabinet in real time. S200: The data center analyzes the status of all charging grids in the charging cabinet, marks idle charging grids and sets 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 operating charging grid using the interference coefficient to determine whether the current temperature of each operating charging grid is abnormal. If abnormal, analyze the protocol comparison table to obtain a debugging protocol, 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 restore the temperature to normal. S400: After the charging grid is used, a 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 on the large visual screen of the data center; In S200, the specific steps are as follows: S201, when the data center receives a charging request, obtains the status of all charging grids under the corresponding charging cabinet, marks all charging grids in the 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 adjacent running state in any direction of up, down, left, and right 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: 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 add all of these time periods to the corresponding impact time period set; after all directly affected objects have been filtered, remove time periods that overlap with the directly affected objects in the marked charging grid from their respective impact 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 based on 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; In S203, the specific steps are as follows: S203-1. Obtain all time periods in the affected time period set, set an interval length L, divide the time period length by L, and round up to obtain the number of sampling points S for the corresponding time period; mark all time periods in the affected time period set in the temperature change graph 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: ； Where 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 marked 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: ； Where 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 influencing object.

2. The method for monitoring abnormal power charging data based on big data according to claim 1, characterized in that: In S100, the history log includes all usage records of the charging cabinet, and 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 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 into the charging grid to taking the battery out when using the charging grid; the protocol comparison table includes all 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 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; 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 running and idle.

3. The method for monitoring abnormal power charging data based on big data according to claim 2, 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 , search BSF in history log e For all usage records, the charging temperatures in these usage records are summed up and the average value is calculated to obtain the base temperature T base ; Identify the objects directly affecting the running charging grid and obtain the interference coefficient. Substitute this into the formula to calculate the temperature threshold of each running charging grid. Obtain the current temperature of each running charging grid. Mark the batteries in the running 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 influencing object; S302: Establish a protocol set for each abnormal battery and obtain the identifier BSF of the abnormal battery u And the currently used charging protocol CX now , search BSF in the protocol comparison table u All supported charging protocols, filtering out those with power less than CX now The charging protocol is placed in the corresponding protocol set; S303. Adjust the protocol set based on the performance parameters of the abnormal battery, analyze the adjusted protocol set to obtain a debugging protocol, 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 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.

4. The method for monitoring abnormal power charging data based on big data according to claim 3, characterized in that: In S303, the specific steps are as follows: S303-1. Build an electrochemical model in MATLAB based on the performance parameters of the abnormal battery to obtain the current charge 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 , SC ed Add SC cm Get the estimated duration SC of each charging agreement yj ; S303-2. Obtain all usage records of the abnormal battery in the history 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 is selected from the SC 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 operating state charging grid 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 placed in 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 of the objects to be adjusted is adjusted until the temperature of the charging grids in all operating states is no greater than the corresponding temperature threshold.

5. A power charging data anomaly monitoring system based on big data, used to execute the power charging data anomaly monitoring method based on big data according to any one of claims 1 to 4, characterized in that: 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, as well as charging cabinet location distribution maps, temperature change maps, and operating data; 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 abnormality 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.

6. The power charging data anomaly monitoring system based on big data according to claim 5, characterized in that: The data acquisition module includes a history log acquisition unit, a chart information acquisition unit, and an operation data acquisition unit; The historical 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; charging information includes charging temperature and charging protocol; The chart information collection unit is used to collect location distribution diagrams, temperature change diagrams, and protocol comparison tables. The location distribution diagram refers to the location distribution of all charging compartments in the charging cabinet. The temperature change diagram refers to the relationship between the internal temperature of each charging compartment and time. The protocol comparison table includes all 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 running and idle.

7. The power charging data anomaly monitoring system based on big data according to claim 6, characterized in that: 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 running 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 all time periods of the same charging grid usage records in the historical log are placed in the corresponding usage period set; secondly, each directly affected object establishes an impact period set, obtains all time periods in the usage period set of the directly affected object, filters out time periods that do not overlap with the usage period set of the corresponding marked charging grid, and places all of them in the corresponding impact period set, and deletes 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 time periods in the impact period set, and the influence index of the corresponding marked charging grid is calculated based on the interference coefficient of each directly affected object; The intelligent recommendation unit is used to respond to charging requests; it transmits the marked charging grid information with the smallest impact index 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.

8. The power charging data anomaly monitoring system based on big data according to claim 7, characterized in that: The exception management module includes an exception 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 charging grid whose current temperature is greater than the corresponding temperature threshold is marked as abnormal; where x and v are constants, and b is the number of directly affected objects. is the interference coefficient of the hth directly influencing object; The protocol adjustment unit is used to identify the debugging protocol; First, establish a protocol set for each abnormal battery and obtain the abnormal battery identifier BSF u And the currently used charging protocol CX now , search BSF in the protocol comparison table u All supported charging protocols, filtering out those with power less 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 based on the analysis of the adjusted protocol set; 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 to ensure that the temperature of each charging grid in the operating state under the charging cabinet is not greater than the corresponding temperature threshold.

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