Data monitoring method and device and related equipment

Abstract

The embodiment of the invention discloses a data monitoring method and device and related equipment, and the method comprises the steps: determining one or more target battery early warning items and a sensing weight corresponding to each target battery early warning item according to the user feedback information of electric energy equipment; determining an early warning value of at least one target battery early warning item according to the obtained electric energy equipment data; and determining a battery early-warning coefficient according to the early-warning value of each target battery early-warning item and the sensing weight corresponding to each target battery early-warning item, wherein the battery early-warning coefficient is used for determining an abnormal early-warning priority of the electric energy equipment. In this way, electric energy equipment early warning monitoring can be carried out in batches, and the abnormity early warning priority of the electric energy equipment is determined, so that abnormity of different electric energy equipment can be handled according to the abnormity early warning priority, potential safety hazards existing in the electric energy equipment can be handled more flexibly and effectively, and the safety of the electric energy equipment is improved.

Description

Technical Field

[0001] This application relates to the field of electricity, and in particular, to a data monitoring method, device and related equipment. Background Art

[0002] With the development of the automotive industry, the sales volume of new energy vehicles has reached new highs. The quality and lifespan of batteries are important factors in the performance and cost of new energy vehicles. If there are safety issues with the batteries, it will not only affect the vehicle performance, but may even lead to safety accidents with unforeseeable consequences. Therefore, battery safety is of utmost importance. Detecting potential safety hazards in batteries in advance and issuing warnings allows vehicle owners and manufacturers to take timely countermeasures, such as replacing the battery or performing repairs, etc., to reduce safety risks and protect personal and property safety. This ensures the quality and safety of battery products from the source and promotes the sustainable development of the new energy vehicle industry.

[0003] Currently, for scenarios where there are a large number of electrical energy devices (such as vehicles) to be pre-warned and monitored for electrical energy device pre-warning and monitoring, it is mainly possible to perform electrical energy device pre-warning and monitoring through a server side (such as a cloud server, server cluster, etc.). However, how to quickly and effectively perform batch electrical energy device pre-warning and monitoring is a very important and urgently needed problem to be solved. Summary of the Invention

[0004] Embodiments of this application provide a data monitoring method, device and related equipment, which can perform batch electrical energy device pre-warning and monitoring by combining user feedback data, and determine the abnormal pre-warning priority of the electrical energy device, so as to process the abnormalities of different electrical energy devices according to the abnormal pre-warning priority, which helps to more flexibly and effectively handle the safety hazards existing in the electrical energy devices and improve the safety of the electrical energy devices.

[0005] In a first aspect, embodiments of this application provide a data monitoring method, the method comprising:

[0006] Determine one or more target battery warning items and the perception weight corresponding to each of the target battery warning items according to user feedback information regarding the electrical energy device;

[0007] Determine the warning value of at least one of the target battery warning items according to the obtained electrical energy device data;

[0008] Determine a battery warning coefficient according to the warning value of each of the target battery warning items and the perception weight corresponding to each of the target battery warning items, where the battery warning coefficient is used to determine the abnormal pre-warning priority of the electrical energy device.

[0009] In a second aspect, embodiments of this application provide a data monitoring device, the device comprising:

[0010] A determination unit, configured to determine one or more target battery warning items and the perception weight corresponding to each of the target battery warning items according to user feedback information for the power equipment; and determine the warning value of at least one of the target battery warning items according to the obtained power equipment data.

[0011] A warning unit, configured to determine a battery warning coefficient according to the warning value of each of the target battery warning items and the perception weight corresponding to each of the target battery warning items, where the battery warning coefficient is used to determine the abnormal warning priority of the power equipment.

[0012] In a third aspect, an embodiment of the present application provides a power equipment, where the power equipment includes a communication device configured to communicate with the data monitoring device described in the second aspect above, and the communication device is configured to perform data interaction with the data monitoring device, so that the data monitoring device executes the method described in the first aspect above according to the power equipment data sent by the communication device.

[0013] In a fourth aspect, an embodiment of the present application provides a server, where the server includes a processor, a memory, a bus, and a communication unit. The communication unit is configured to receive power equipment data, and the processor, the communication unit, and the memory are connected through the bus.

[0014] The memory is configured to store a program.

[0015] The processor is configured to call, through the bus, the program stored in the memory and execute the method described in the first aspect above.

[0016] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, where program instructions are stored in the computer-readable storage medium, and when the program instructions are executed, the method described in the first aspect above is implemented.

[0017] In a sixth aspect, an embodiment of the present application provides a computer program product, where the computer program product includes program instructions, and when the program instructions are executed by a processor, the method described in the first aspect above is implemented.

[0018] In the embodiments of the present application, one or more target battery warning items and the perception weight corresponding to each target battery warning item can be determined according to the user feedback information for the power equipment; the warning values of at least one target battery warning item can be determined according to the obtained power equipment data; the battery warning coefficient can be determined according to the warning value of each target battery warning item and the perception weight corresponding to each target battery warning item, and the battery warning coefficient is used to determine the abnormal warning priority of the power equipment. By combining the data of user feedback, batch warning monitoring of power equipment can be carried out, and the abnormal warning priority of the power equipment can be determined, so as to process the abnormalities of different power equipment according to the abnormal warning priority, which helps to process the potential safety hazards of power equipment more flexibly and effectively and improve the safety of power equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 The structural schematic diagram of a data monitoring system provided by the embodiments of the present application;

[0021] Figure 2 It is the flow schematic diagram of a data monitoring method provided by the embodiments of the present application;

[0022] Figure 3 It is the flow schematic diagram of another data monitoring method provided by the embodiments of the present application;

[0023] Figure 4 It is the structural schematic diagram of a data monitoring device provided by the embodiments of the present application;

[0024] Figure 5 It is the structural schematic diagram of a server provided by the embodiments of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0026] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or their groups.

[0027] It should also be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit this application. As used in this application specification and the appended claims, unless the context clearly dictates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0028] It should be further understood that the term "and / or" used in this application specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] In a specific implementation, the server described in the embodiments of this application includes, but is not limited to, other portable devices such as mobile phones, laptop computers, or tablet computers having a touch-sensitive surface (e.g., a touch screen display and / or a touchpad). It should also be understood that in some embodiments, the device is not a portable communication device, but a desktop computer having a touch-sensitive surface (e.g., a touch screen display and / or a touchpad).

[0030] A data monitoring method, apparatus, and related device provided by an embodiment of this application. The data monitoring apparatus can be disposed in an electrical energy device, and the electrical energy device can be disposed in a server. In some embodiments, the server can include, but is not limited to, a cloud server communicatively connected to the electrical energy device. In some embodiments, the electrical energy device can include, but is not limited to, devices used by users such as vehicles, aircraft, ships, temperature regulators, or energy storage cabinets.

[0031] The data monitoring method provided by the embodiments of this application can be applied to the early warning monitoring scenario of a large number of electrical energy devices. Through the server or the cloud server, batch early warning monitoring of electrical energy devices is performed. Combining with user feedback information, the early warning priority of the anomalies feedback by the user is determined from a large number of electrical energy devices, and a batch of abnormal electrical energy devices to be processed feedback by the user is determined from a large number of electrical energy devices according to the early warning priority of the anomalies.

[0032] Specifically, the data monitoring method provided by the embodiments of the present application can determine one or more target battery warning items and the perception weight corresponding to each target battery warning item according to the user feedback information for the power equipment; determine the warning values of at least one target battery warning item according to the obtained power equipment data; determine the battery warning coefficient according to the warning value of each target battery warning item and the perception weight corresponding to each target battery warning item, and the battery warning coefficient is used to determine the abnormal warning priority of the power equipment, so that users or technicians can process the abnormalities of each power equipment according to the order of the abnormal warning priorities from high to low.

[0033] By combining the data feedback by users, batch warning monitoring of power equipment can be carried out, and the abnormal warning priority of the power equipment can be determined, which helps to process the abnormalities of different power equipment according to the abnormal warning priority and improve the safety of the power equipment.

[0034] Such as Figure 1 shown, Figure 1 As shown in the structural schematic diagram of a data monitoring system provided by the embodiments of the present application, the data monitoring system includes a server 101 and a power equipment 102. The server 101 and the power equipment 102 are communicatively connected. The power equipment 102 can collect power equipment data and obtain user feedback information on the use of the power equipment. The power equipment data may include, but is not limited to, battery parameter data, power equipment status data, etc., and send the power equipment data and the user feedback information to the server 101. The server 101 can determine one or more target battery warning items and the perception weight corresponding to each target battery warning item according to the user feedback information for the power equipment; determine the warning values of at least one target battery warning item according to the obtained power equipment data; determine the battery warning coefficient according to the warning value of each target battery warning item and the perception weight corresponding to each target battery warning item. In some embodiments, the user feedback information can be input by the user through the terminal device on the power equipment 102, or sent by the user to the power equipment 102 through the terminal device communicatively connected to the power equipment 102, or can also be the relevant information of the power equipment detected by the power equipment 102 during the user's use process.

[0035] See Figure 2 , Figure 2 is the flowchart of a data monitoring method provided by the embodiments of the present application. As Figure 2 shown, the data monitoring method in this embodiment can be applied to a data monitoring device, and the data monitoring device is set in the server. The method can at least include the following steps.

[0036] S201: Determine one or more target battery warning items and the perception weight corresponding to each target battery warning item according to the user feedback information for the power equipment.

[0037] In the embodiments of the present application, the server may determine one or more target battery warning items and the corresponding perception weight for each target battery warning item according to the user feedback information regarding the electrical energy device. The server may determine one or more target battery warning items and the corresponding perception weight for each target battery warning item according to the user feedback information on the use of the electrical energy device within a certain historical time range, where the user feedback information is some problems reported by the user during the use of the electrical energy device. The historical time range may be any time range before the current moment, such as within one week before the current moment.

[0038] In one embodiment, when the server determines one or more target battery warning items and the corresponding perception weight for each target battery warning item according to the user feedback information regarding the electrical energy device, it may determine one or more fault types reported by the user according to the user feedback information on the use of the electrical energy device; determine one or more target battery warning items corresponding to each of the fault types reported by the user according to the preset mapping relationship between the fault type and the battery warning item; and determine the corresponding perception weight for each target battery warning item corresponding to each of the fault types reported by the user according to the preset mapping relationship between the fault type and the weight corresponding to the target battery warning item.

[0039] In one embodiment, when the server determines one or more fault types reported by the user according to the user feedback information regarding the electrical energy device, it may determine one or more fault type keywords existing in the user feedback information regarding the electrical energy device; and determine one or more fault types corresponding to the one or more fault type keywords according to the preset mapping relationship between the keyword and the fault type.

[0040] In some embodiments, the fault types may include but are not limited to a downward jump in the state of charge (SOC), an upward jump in SOC, a short driving range, etc. The fault type keywords may be any one or more of words, terms, numbers, letters, etc. associated with the fault type.

[0041] In one embodiment, the server may determine whether one or more fault types reported by the user belong to safe fault types; if the judgment result is no, it may perform the step of determining the battery warning coefficient according to the warning value of each target battery warning item and the corresponding perception weight of each target battery warning item; if the judgment result is yes, it may determine that the electrical energy device is normal.

[0042] The embodiments of the present application assign different weights to different target battery warning items, which helps to subsequently assist in calculating the battery warning coefficient so as to determine whether there is an abnormality in the battery according to the battery warning coefficient.

[0043] By introducing user perception factors (i.e., user feedback information) in the embodiments of the present application and paying attention to the user experience, it is helpful to perform batch warning monitoring of power equipment more flexibly and effectively in combination with the user experience, select abnormal power equipment with obvious user perception from a large number of power equipment, and contribute to optimizing the warning monitoring results of batch power equipment.

[0044] S202: Determine the warning values of at least one target battery warning item according to the obtained power equipment data.

[0045] In the embodiments of the present application, the server can determine the warning values of at least one target battery warning item according to the obtained power equipment data. In some embodiments, the battery warning items include, but are not limited to, one or more of the SOC downward jump item, the SOC deviation item, and the overall package capacity item. In other embodiments, the battery warning items include, but are not limited to, one or more of the SOC upward jump item, the SOC deviation item, and the overall package capacity item. Among them, the SOC deviation value is used to evaluate the battery consistency of the power equipment, and the overall package capacity value is used to indicate the current capacity of the battery.

[0046] Among them, the power equipment data includes battery parameter data and / or power equipment status data. The battery parameter data includes, but is not limited to, charging voltage, single-cell voltage, temperature, time, current, SOC, and nominal capacity, etc. In some embodiments, the power equipment status data includes, but is not limited to, charging status, driving status, parking status, etc.

[0047] In one embodiment, the power equipment data includes battery parameter data, and one or more target battery warning items include one or more of the following: the SOC downward jump item, the SOC deviation item, and the overall package capacity item; when the server determines the warning values of at least one target battery warning item according to the obtained power equipment data, in the case where each target battery warning item includes the SOC jump item, the server can determine the SOC jump value of the SOC jump item according to the time and SOC at which the jump occurs in the battery parameter data; and / or, determine the SOC deviation value of the SOC deviation item and / or the overall package capacity value of the overall package capacity item according to the charging voltage, single-cell voltage, temperature, time, current, SOC, and nominal capacity in the battery parameter data.

[0048] In one embodiment, the electrical energy device data includes battery parameter data and / or electrical energy device status data, and one or more target battery warning items include one or more of the following: SOC upward jump item, SOC deviation item, and overall pack capacity item; when determining the warning value of at least one target battery warning item based on the obtained electrical energy device data, the server may, in the case where one or more target battery warning items include the SOC upward jump item, calculate the SOC upward jump value of the SOC upward jump item according to the time of jump and the SOC in the battery parameter data; and / or, in the case where the status of the electrical energy device is determined to be the charging status, calculate the SOC deviation value of the SOC deviation item and / or the overall pack capacity of the overall pack capacity item according to the charging voltage, single cell voltage, temperature, time, current, SOC, and nominal capacity in the battery parameter data.

[0049] In the embodiment of the present application, calculating the battery warning coefficient according to the warning value of each target battery warning item and the weight corresponding to each target battery warning item helps to determine the abnormal warning priority of the electrical energy device according to the battery warning coefficient.

[0050] S203: Determine the battery warning coefficient according to the warning value of each target battery warning item and the perceived weight corresponding to each target battery warning item, and the battery warning coefficient is used to determine the abnormal warning priority of the electrical energy device.

[0051] In the embodiment of the present application, the server may determine the battery warning coefficient according to the warning value of each target battery warning item and the perceived weight corresponding to each target battery warning item, and the battery warning coefficient is used to determine the abnormal warning priority of the electrical energy device.

[0052] In the embodiment of the present application, when the server determines the battery warning coefficient according to the warning value of each target battery warning item and the perceived weight corresponding to each target battery warning item, it may determine the quantified warning value of each target battery warning item according to the warning value of each target battery warning item and the corresponding warning item threshold; determine the battery warning coefficient according to the quantified warning value of each target battery warning item and the perceived weight corresponding to each target battery warning item.

[0053] In one embodiment, in the case where one or more target battery warning items include the SOC upward jump item, the SOC downward jump item, or the SOC deviation item, the calculation method of the quantified warning value of any one of the one or more target battery warning items includes: obtaining the ratio of the warning value of any one target battery warning item to the corresponding warning item threshold; determining the ratio as the quantified warning value of any one target battery warning item.

[0054] When one or more target battery warning items include the SOC upward jump item, for the quantization warning value of the SOC upward jump item, when the server calculates the quantization warning value of the SOC upward jump item, it can obtain the ratio of the warning value of the SOC upward jump item to the warning item threshold corresponding to the SOC upward jump item; and determine this ratio as the quantization warning value of the SOC upward jump item.

[0055] When one or more target battery warning items include the SOC deviation item, for the quantization warning value of the SOC deviation item, when the server calculates the quantization warning value of the SOC deviation item, it can obtain the ratio of the warning value of the SOC deviation item to the warning item threshold corresponding to the SOC deviation item; and determine this ratio as the quantization warning value of the SOC deviation item.

[0056] When one or more target battery warning items include the SOC downward jump item, for the quantization warning value of the SOC downward jump item, when the server calculates the quantization warning value of the SOC downward jump item, it can obtain the ratio of the warning value of the SOC downward jump item to the warning item threshold corresponding to the SOC downward jump item; and determine this ratio as the quantization warning value of the SOC downward jump item.

[0057] In one embodiment, when one or more target battery warning items include the overall pack capacity item, the calculation method of the quantization warning value of the overall pack capacity item includes: calculating the difference between 1 and the overall pack capacity, and determining the ratio of the difference to the warning item threshold corresponding to the overall pack capacity item as the quantization warning value of the overall pack capacity item.

[0058] In one embodiment, after the server determines the battery warning coefficient based on the warning value of each target battery warning item and the sensing weight corresponding to each target battery warning item, it can determine one or more target battery warning coefficients in the battery warning coefficient that are greater than the coefficient threshold; according to the preset correspondence between the battery warning coefficient and the abnormal warning priority, determine the abnormal warning priority corresponding to each target battery warning coefficient, and determine the abnormal warning priority corresponding to each target battery warning coefficient as the abnormal warning priority of the power equipment.

[0059] The embodiments of the present application perform weighted summation calculation on different warning items with weights to obtain the battery warning coefficient, which is convenient for more accurately and effectively determining whether the power equipment is abnormal, and further determining the abnormal warning priority of the power equipment when it is determined that there is an abnormality, so that the user can timely handle the abnormal power equipment with a high abnormal warning priority.

[0060] In an embodiment of the present application, one or more target battery warning items and the corresponding perception weights for each target battery warning item can be determined according to the user feedback information for the power equipment; the warning values of at least one target battery warning item can be determined according to the obtained power equipment data; the battery warning coefficient is determined according to the warning value of each target battery warning item and the corresponding perception weight of each target battery warning item, and the battery warning coefficient is used to determine the abnormal warning priority of the power equipment. By combining the data of user feedback, batch warning monitoring of power equipment can be carried out, and the abnormal warning priority of the power equipment can be determined, so as to process the abnormalities of different power equipment according to the abnormal warning priority, which helps to handle the potential safety hazards of power equipment more flexibly and effectively and improve the safety of power equipment.

[0061] Please refer to Figure 3 , Figure 3 FIG. is a schematic flowchart of another data monitoring method provided by an embodiment of the present application. As shown in the figure, the data monitoring method in this embodiment can be applied to a data monitoring device, and the data monitoring device is set in a server. The method can at least include the following steps.

[0062] S301: Obtain user feedback information for the power equipment, and determine one or more fault types feedback by the user according to the user feedback information.

[0063] S302: Determine one or more target battery warning items corresponding to one or more fault types feedback by the user according to the mapping relationship between the preset fault types and the battery warning items.

[0064] S303: Obtain the battery parameter data and / or the power equipment status data of the power equipment, and calculate the warning values of each target battery warning item according to the battery parameter data and / or the power equipment status data.

[0065] S304: When the battery warning coefficient is greater than the coefficient threshold, determine the weights corresponding to each target battery warning item corresponding to each fault type feedback by the user according to the mapping relationship between the preset fault types and the weights corresponding to the target battery warning items, and calculate the battery warning coefficient according to the warning values of each target battery warning item and the weights corresponding to each target battery warning item.

[0066] S305: Determine whether the battery warning coefficient is greater than the coefficient threshold. If the judgment result is yes, execute step S306. If the judgment result is no, end the current power equipment warning monitoring operation.

[0067] S306: Determine that the power equipment has an abnormality, and send a power equipment warning notification to the power equipment.

[0068] Embodiments of the present application can obtain user feedback information on power equipment within a historical time range, and determine one or more fault types feedback by users according to the user feedback information; determine one or more target battery warning items corresponding to each of the fault types feedback by users according to the mapping relationship between the preset fault types and the battery warning items; determine the weights corresponding to each of the target battery warning items corresponding to each of the fault types feedback by users according to the mapping relationship between the preset fault types and the weights corresponding to the target battery warning items; obtain the battery parameter data of the power equipment and / or the power equipment status data, and calculate the warning values of each of the target battery warning items according to the battery parameter data and the power equipment status data; calculate the battery warning coefficient according to the warning values of each of the target battery warning items and the weights corresponding to each of the target battery warning items, determine whether the power equipment is abnormal according to the battery warning coefficient, and send a power equipment warning notification to the power equipment when it is determined that the power equipment is abnormal. By combining the data feedback by users, batch warning monitoring of power equipment can be carried out, and the abnormal warning priority of the power equipment can be determined, so as to process the abnormalities of different power equipment according to the abnormal warning priority, which helps to handle the potential safety hazards existing in the power equipment more flexibly and effectively, and improve the safety of the power equipment.

[0069] See Figure 4 , Figure 4 is a schematic structural diagram of a data monitoring device provided by an embodiment of the present application. As Figure 4 shown, the data monitoring device in this embodiment may include a determination unit 401 and a warning unit 402. Among them,

[0070] The determination unit 401 is configured to determine one or more target battery warning items and the perception weight corresponding to each of the target battery warning items according to the user feedback information on the power equipment; determine the warning value of at least one of the target battery warning items according to the obtained power equipment data;

[0071] The warning unit 402 is configured to determine the battery warning coefficient according to the warning value of each of the target battery warning items and the perception weight corresponding to each of the target battery warning items, and the battery warning coefficient is used to determine the abnormal warning priority of the power equipment.

[0072] Further, when the determination unit 401 determines one or more target battery warning items and the perception weight corresponding to each of the target battery warning items according to the user feedback information on the power equipment, it is specifically configured to:

[0073] Determine one or more fault types feedback by users according to the user feedback information on the power equipment;

[0074] Determine one or more target battery warning items corresponding to each of the fault types feedback by the user according to the mapping relationship between the preset fault types and the battery warning items;

[0075] Determine the perception weight corresponding to each of the target battery warning items corresponding to each of the fault types feedback by the user according to the mapping relationship between the preset fault types and the weights corresponding to the target battery warning items.

[0076] Further, when the determining unit 401 determines one or more fault types feedback by the user according to the user feedback information for the power equipment, it specifically is used for:

[0077] Determine one or more fault type keywords existing in the user feedback information for the power equipment;

[0078] Determine one or more fault types corresponding to the one or more fault type keywords according to the mapping relationship between the preset keywords and the fault types.

[0079] Further, the power equipment data includes battery parameter data, and the one or more target battery warning items include one or more of the following: state of charge (SOC) downward jump item, SOC deviation item, and overall pack capacity item; when the determining unit 401 determines the warning value of at least one of the target battery warning items according to the obtained power equipment data, it specifically is used for:

[0080] In the case where each of the target battery warning items includes an SOC jump item, determine the SOC jump value of the SOC jump item according to the time and SOC at which the jump occurs in the battery parameter data; and / or,

[0081] Determine the SOC deviation value of the SOC deviation item and / or the overall pack capacity value of the overall pack capacity item according to the charging voltage, single cell voltage, temperature, time, current, SOC, and nominal capacity in the battery parameter data.

[0082] Further, when the warning unit 402 determines the battery warning coefficient according to the warning value of each of the target battery warning items and the perception weight corresponding to each of the target battery warning items, it specifically is used for:

[0083] Determine the quantified warning value of each of the target battery warning items according to the warning value of each of the target battery warning items and their respective corresponding warning item thresholds;

[0084] Determine the battery warning coefficient according to the quantified warning value of each of the target battery warning items and the perception weight corresponding to each of the target battery warning items.

[0085] Further, when one or more of the target battery warning items include an SOC jump item or an SOC deviation item, the calculation method of the quantization warning value of any one of the one or more target battery warning items is as follows:

[0086] Obtain the ratio of the warning value of any one of the target battery warning items to the corresponding warning item threshold;

[0087] Determine the ratio as the quantization warning value of any one of the target battery warning items.

[0088] Further, when one or more of the target battery warning items include a whole-pack capacity item, the calculation method of the quantization warning value of the whole-pack capacity item is as follows:

[0089] Calculate the ratio of the whole-pack capacity value to the warning item threshold corresponding to the whole-pack capacity item;

[0090] Determine the difference between 1 and the ratio as the quantization warning value of the whole-pack capacity item.

[0091] Further, after the warning unit 402 determines the battery warning coefficient according to the warning value of each target battery warning item and the perception weight corresponding to each target battery warning item, it is further used for:

[0092] Determine one or more target battery warning coefficients greater than the coefficient threshold in the battery warning coefficient;

[0093] According to the preset correspondence between the battery warning coefficient and the abnormal warning priority, determine the abnormal warning priority corresponding to each target battery warning coefficient, and determine the abnormal warning priority corresponding to each target battery warning coefficient as the abnormal warning priority of the power equipment.

[0094] Further, the warning unit 402 is further used for:

[0095] Judge whether one or more fault types feedback by the user belong to safe fault types;

[0096] If the judgment result is no, then execute the step of determining the battery warning coefficient according to the warning value of each target battery warning item and the perception weight corresponding to each target battery warning item;

[0097] If the judgment result is yes, then determine that there is no abnormality in the power equipment.

[0098] In the embodiments of the present application, the data monitoring device may determine one or more target battery warning items and the perception weight corresponding to each target battery warning item according to the user feedback information for the power equipment; determine the warning value of at least one target battery warning item according to the obtained power equipment data; determine the battery warning coefficient according to the warning value of each target battery warning item and the perception weight corresponding to each target battery warning item, and the battery warning coefficient is used to determine the abnormal warning priority of the power equipment. By combining the user feedback data, batch warning monitoring of power equipment can be carried out, and the abnormal warning priority of the power equipment can be determined, so as to process the abnormalities of different power equipment according to the abnormal warning priority, which helps to handle the potential safety hazards of power equipment more flexibly and effectively and improve the safety of power equipment.

[0099] See Figure 5 , Figure 5 FIG. is a schematic structural diagram of a server provided by an embodiment of the present application. The server in this embodiment shown in the figure may include: one or more processors 501, a memory 502, a bus 503, and a communication interface 504 (i.e., a communication unit), and the processor 501, the communication interface 504, and the memory 502 are connected through the bus 503; the memory 502 is used to store programs; the processor 501 is used to call the programs stored in the above-mentioned memory through the bus 503 and execute the following steps:

[0100] Determine one or more target battery warning items and the perception weight corresponding to each of the target battery warning items according to the user feedback information for the power equipment;

[0101] Determine the warning value of at least one of the target battery warning items according to the obtained power equipment data;

[0102] Determine the battery warning coefficient according to the warning value of each of the target battery warning items and the perception weight corresponding to each of the target battery warning items, and the battery warning coefficient is used to determine the abnormal warning priority of the power equipment.

[0103] Further, when the processor 501 determines one or more target battery warning items and the perception weight corresponding to each of the target battery warning items according to the user feedback information for the power equipment, it is specifically used for:

[0104] Determine one or more failure types fed back by the user according to the user feedback information for the power equipment;

[0105] Determine one or more target battery warning items corresponding to each of the failure types fed back by the user according to the preset mapping relationship between the failure type and the battery warning item;

[0106] Determine the perceived weight corresponding to each of the target battery warning items corresponding to the fault types feedback by the user according to the mapping relationship between the preset fault types and the weights of the target battery warning items.

[0107] Further, when the processor 501 determines one or more fault types feedback by the user according to the user feedback information for the power equipment, it is specifically configured to:

[0108] Determine one or more fault type keywords existing in the user feedback information for the power equipment;

[0109] Determine one or more fault types corresponding to the one or more fault type keywords according to the mapping relationship between the preset keywords and the fault types.

[0110] Further, the power equipment data includes battery parameter data, and the one or more target battery warning items include one or more of the following: the state of charge (SOC) downward jump item, the SOC deviation item, and the overall pack capacity item; when the processor 501 determines the warning value of at least one of the target battery warning items according to the obtained power equipment data, it is specifically configured to:

[0111] In the case where each of the target battery warning items includes the SOC jump item, determine the SOC jump value of the SOC jump item according to the time and SOC at which the jump occurs in the battery parameter data; and / or,

[0112] Determine the SOC deviation value of the SOC deviation item and / or the overall pack capacity value of the overall pack capacity item according to the charging voltage, cell voltage, temperature, time, current, SOC, and nominal capacity in the battery parameter data.

[0113] Further, when the processor 501 determines the battery warning coefficient according to the warning value of each of the target battery warning items and the perceived weight corresponding to each of the target battery warning items, it is specifically configured to:

[0114] Determine the quantified warning value of each of the target battery warning items according to the warning value of each of the target battery warning items and their respective warning item thresholds;

[0115] Determine the battery warning coefficient according to the quantified warning value of each of the target battery warning items and the perceived weight corresponding to each of the target battery warning items.

[0116] Further, in the case where the one or more target battery warning items include the SOC jump item or the SOC deviation item, the calculation method of the quantified warning value of any one of the one or more target battery warning items includes:

[0117] Obtain the ratio of the warning value of any one of the target battery warning items to the corresponding warning item threshold;

[0118] Determine the quantified warning value of any one of the target battery warning items as the ratio.

[0119] Further, when the one or more target battery warning items include the overall pack capacity item, the calculation method of the quantified warning value of the overall pack capacity item includes:

[0120] Calculate the ratio of the overall pack capacity value to the warning item threshold corresponding to the overall pack capacity item;

[0121] Determine the difference between 1 and the ratio as the quantified warning value of the overall pack capacity item.

[0122] Further, after the processor 501 determines the battery warning coefficient according to the warning value of each target battery warning item and the corresponding perception weight of each target battery warning item, it is further used for:

[0123] Determine one or more target battery warning coefficients greater than the coefficient threshold in the battery warning coefficient;

[0124] According to the preset correspondence between the battery warning coefficient and the abnormal warning priority, determine the abnormal warning priority corresponding to each target battery warning coefficient, and determine the abnormal warning priority corresponding to each target battery warning coefficient as the abnormal warning priority of the power equipment.

[0125] Further, the processor 501 is further used for:

[0126] Judge whether one or more fault types feedback by the user belong to safe fault types;

[0127] If the judgment result is no, then execute the step of determining the battery warning coefficient according to the warning value of each target battery warning item and the corresponding perception weight of each target battery warning item;

[0128] If the judgment result is yes, then determine that there is no abnormality in the power equipment.

[0129] In the embodiments of the present application, the server may determine one or more target battery warning items and the corresponding perception weights for each target battery warning item according to the user feedback information for the power equipment; determine the warning values of at least one target battery warning item according to the obtained power equipment data; and determine a battery warning coefficient according to the warning value of each target battery warning item and the corresponding perception weight for each target battery warning item. The battery warning coefficient is used to determine the abnormal warning priority of the power equipment. By combining the data feedback by users, batch warning monitoring of power equipment can be carried out, and the abnormal warning priority of the power equipment can be determined, so as to handle the abnormalities of different power equipment according to the abnormal warning priority, which helps to handle the potential safety hazards of power equipment more flexibly and effectively and improve the safety of power equipment.

[0130] It should be understood that in the embodiments of the present application, the processor 501 may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0131] The memory 502 may include a read-only memory and a random access memory, and provide instructions and data to the processor 501. A part of the memory 502 may also include a non-volatile random access memory. For example, the memory 502 may also store information about the device type.

[0132] In specific implementation, the processor 501 described in the embodiments of the present application may execute the implementation manners described in the data monitoring method provided in the embodiments of the present application, and may also execute the implementation manners of the battery monitoring device described in the embodiments of the present application, which will not be elaborated herein.

[0133] The embodiments of the present application further provide a power equipment, which includes a communication device for communicatively connecting with a data monitoring device. The communication device is used for data interaction with the data monitoring device, so that the data monitoring device executes the implementation manners described in the data monitoring method provided in the embodiments of the present application according to the power equipment data sent by the communication device.

[0134] Embodiments of the present application also provide a computer-readable storage medium storing a computer program, which when executed by a processor, implements the methods described in the corresponding embodiments of the present application and can also implement the devices corresponding to the embodiments of the present application, which will not be elaborated herein.

[0135] The computer-readable storage medium may be an internal storage unit of the device described in any of the foregoing embodiments, such as the hard disk or memory of the device. The computer-readable storage medium may also be an external storage device of the device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. equipped on the device. Further, the computer-readable storage medium may also include both the internal storage unit and the external storage device of the device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computing device. The computer-readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0136] Embodiments of the present application also provide a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computing device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computing device executes the methods provided in the above various embodiments.

[0137] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0138] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described terminal and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be elaborated herein.

[0139] In several embodiments provided in the present application, it should be understood that the disclosed server and method can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed coupling or direct coupling or communication connection to each other can be an indirect coupling or communication connection through some interfaces, devices or units, or can also be an electrical, mechanical or other form of connection.

[0140] The steps in the method embodiments of the present application can be adjusted, combined and deleted according to actual needs.

[0141] The units in the terminal embodiments of the present application can be combined, divided and deleted according to actual needs.

[0142] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can also be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present application.

[0143] In addition, each functional unit in the various embodiments of the present application can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0144] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: USB flash drive, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disk and other various media that can store program codes.

[0145] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A data monitoring method, characterized in that, The method includes: Determining one or more target battery warning items and a perception weight corresponding to each of the target battery warning items according to user feedback information regarding the power equipment; Determining a warning value of at least one of the target battery warning items according to the obtained power equipment data; Determining a battery warning coefficient according to the warning value of each of the target battery warning items and the perception weight corresponding to each of the target battery warning items, where the battery warning coefficient is used to determine the abnormal warning priority of the power equipment.

2. The method according to claim 1, wherein The determining one or more target battery warning items and a perception weight corresponding to each of the target battery warning items according to user feedback information regarding the power equipment includes: Determining one or more fault types feedback by the user according to the user feedback information regarding the power equipment; Determining one or more target battery warning items corresponding to each of the fault types feedback by the user according to the mapping relationship between the preset fault types and the battery warning items; Determining a perception weight corresponding to each of the target battery warning items corresponding to each of the fault types feedback by the user according to the mapping relationship between the preset fault types and the weights corresponding to the target battery warning items.

3. The method according to claim 2, wherein The determining one or more fault types feedback by the user according to the user feedback information regarding the power equipment includes: Determining one or more fault type keywords existing in the user feedback information regarding the power equipment; Determining one or more fault types corresponding to the one or more fault type keywords according to the mapping relationship between the preset keywords and the fault types.

4. The method according to claim 1, wherein The power equipment data includes battery parameter data, and one or more of the target battery warning items include one or more of the following: state of charge (SOC) jump item, SOC deviation item, and overall pack capacity item; The determining a warning value of at least one of the target battery warning items according to the obtained power equipment data includes: When each of the target battery warning items includes an SOC jump item, determining the SOC jump value of the SOC jump item according to the time and SOC at which the jump occurs in the battery parameter data; and / or, Determining the SOC deviation value of the SOC deviation item and / or the overall pack capacity value of the overall pack capacity item according to the charging voltage, single cell voltage, temperature, time, current, SOC, and nominal capacity in the battery parameter data.

5. The method according to any one of claims 1-4, characterized in that The determining a battery warning coefficient according to the warning value of each of the target battery warning items and the perception weight corresponding to each of the target battery warning items includes: Determining a quantified warning value of each of the target battery warning items according to the warning value of each of the target battery warning items and the respective warning item thresholds; Determining a battery warning coefficient according to the quantified warning value of each of the target battery warning items and the perception weight corresponding to each of the target battery warning items.

6. The method according to claim 5, characterized in that When one or more of the target battery warning items include an SOC jump item or an SOC deviation item, the calculation method of the quantified warning value of any one of the one or more target battery warning items includes: Obtain the ratio of the warning value of any one of the target battery warning items to the corresponding warning item threshold; Determine the ratio as the quantified warning value of any one of the target battery warning items.

7. The method according to claim 5, characterized in that, When one or more of the target battery warning items include the overall pack capacity item, the calculation method of the quantified warning value of the overall pack capacity item includes: Calculate the ratio of the overall pack capacity value to the warning item threshold corresponding to the overall pack capacity item; Determine the difference between 1 and the ratio as the quantified warning value of the overall pack capacity item.

8. The method according to claim 1 or 5, characterized in that After determining the battery warning coefficient according to the warning value of each target battery warning item and the corresponding perception weight of each target battery warning item, it further includes: Determine one or more target battery warning coefficients greater than the coefficient threshold in the battery warning coefficient; According to the preset correspondence between the battery warning coefficient and the abnormal warning priority, determine the abnormal warning priority corresponding to each target battery warning coefficient, and determine the abnormal warning priority corresponding to each target battery warning coefficient as the abnormal warning priority of the power equipment.

9. The method according to claim 2 or 3, characterized in that, The method further includes: Judge whether one or more fault types feedback by the user belong to safe fault types; If the judgment result is no, then execute the step of determining the battery warning coefficient according to the warning value of each target battery warning item and the corresponding perception weight of each target battery warning item; If the judgment result is yes, then determine that there is no abnormality in the power equipment.

10. A data monitoring device, characterized in that, The device includes: A determination unit, configured to determine one or more target battery warning items and the corresponding perception weight of each target battery warning item according to the user feedback information for the power equipment; determine the warning value of at least one of the target battery warning items according to the obtained power equipment data; A warning unit, configured to determine a battery warning coefficient according to the warning value of each target battery warning item and the corresponding perception weight of each target battery warning item, where the battery warning coefficient is used to determine the abnormal warning priority of the power equipment.

11. An electrical energy device, characterized in that, The power equipment includes a communication device for communicating with a data monitoring device, and the communication device is used for data interaction with the data monitoring device, so that the data monitoring device executes the method according to any one of claims 1-9 based on the power equipment data sent by the communication device.

12. The electrical energy device according to claim 11, characterized in that, The power equipment is a vehicle, an aircraft, a ship, a temperature regulator or an energy storage cabinet.

13. A server, characterized in that, The server includes a processor and a communication unit; the communication unit is configured to receive power equipment data, and the processor is configured to execute the method according to any one of claims 1-9.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program instructions, and when the program instructions are executed, the method according to any one of claims 1-9 is implemented.

15. A computer program product, characterized in that, The computer program product includes program instructions, and when the program instructions are executed by a processor, the method according to any one of claims 1-9 is implemented.

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