A data acquisition method, system and electronic device

Abstract

The application provides a data acquisition method and system and an electronic device, and relates to the technical field of data processing.In the application, first, the original data collected by various data collection devices in a set time range is acquired, and the original data type of each original data is determined based on the data characteristics of each obtained original data;then, the data conversion mode matched with each original data type is selected from a preset data conversion mode set;finally, the corresponding original data is converted into target data based on the obtained various data conversion modes, so that the target data can be displayed under a preset data display condition.This method can not only reduce the number of data processing ports corresponding to various data collection devices, but also improve the efficiency of obtaining various target data.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a data acquisition method, system and electronic device. BACKGROUND

[0002] With the rapid development of computer technology, data is widely used in information bearing and display. Therefore, in order to adjust the production and marketing strategies of products according to the obtained data information, big data technology can be used to collect various product attribute sets and business demand data information on the market.

[0003] In the prior art, in order to accurately acquire data information, the obtained raw data needs to be processed and reported level by level to obtain corresponding target data, so that when it is determined that the target data meets the corresponding data analysis requirements, it is determined whether the attribute set of the target product meets the corresponding business demand according to the obtained target data.

[0004] However, using the above data acquisition method, since the obtained raw data needs to be processed and reported level by level, a large amount of time is required for data processing and reporting, so that the corresponding target data cannot be acquired in time, and thus it is not possible to directly determine whether the attribute set of the target product meets the corresponding business demand according to the target data.

[0005] Therefore, using the above method, the efficiency of data acquisition is low. SUMMARY

[0006] The present application provides a data acquisition method, system and electronic device for improving the efficiency of data acquisition. The specific technical solutions are as follows:

[0007] In a first aspect, the present application provides a data acquisition method, comprising:

[0008] acquiring raw data collected by various data collection devices within a set time range, and determining the raw data type of each of the various raw data based on the data characteristics of each of the obtained various raw data;

[0009] selecting a data conversion mode matched with each of the various raw data types from a preset data conversion mode set, wherein each data conversion mode represents the conversion of the raw data type of the corresponding raw data into a target data type;

[0010] based on the obtained various data conversion modes, converting the corresponding raw data into target data, and displaying the target data under a preset data display condition.

[0011] Through the method, the number of data processing ports corresponding to various data collection devices can be reduced, and the efficiency of obtaining various target data can be improved.

[0012] In a possible design, the original data includes, but is not limited to, any one of the following:

[0013] running time of each type of vehicle, and vehicle demand of each type of vehicle in the running time of the type of vehicle;

[0014] energy consumption and available mileage of each type of vehicle in a specific driving distance, where the available mileage represents a vehicle driving distance corresponding to a remaining amount of energy of the vehicle;

[0015] production amount of each type of vehicle corresponding to each production time node in a set production time range.

[0016] fault data of each type of vehicle in a set historical time range in the target scenario.

[0017] In this way, the original data collected by each collection device in a set time period can be obtained.

[0018] In a possible design, if the original data is the running time of each type of vehicle and the vehicle demand of each type of vehicle in the running time of the type of vehicle, the running time and the vehicle demand of each type of vehicle are obtained in the following manner:

[0019] The following operations are respectively performed for each type of vehicle:

[0020] obtaining the respective rotation speed of a type of vehicle in each time period in a set time range;

[0021] filtering, from the time periods, at least one time period that meets a rotation speed interval corresponding to the type of vehicle according to the rotation speed interval set for the type of vehicle;

[0022] obtaining the running time of the type of vehicle based on the obtained at least one time period, and obtaining the vehicle demand of the type of vehicle in the running time according to a preset vehicle demand statistical rule.

[0023] In this way, the first type of demand data corresponding to the first type of original data can be obtained, and the efficiency of obtaining the first type of demand data is improved.

[0024] In a possible design, if the original data is the energy consumption and the available mileage of each type of vehicle in a specific driving distance, the energy consumption and the available mileage of each type of vehicle are obtained in the following manner:

[0025] For each type of vehicle, the following operations are performed respectively:

[0026] An initial amount of energy and a remaining amount of energy of a type of vehicle driving in the specific driving mileage are obtained;

[0027] According to the initial amount of energy and the remaining amount of energy, an energy consumption amount of the type of vehicle in the specific driving mileage is obtained, and based on the specific driving mileage and the energy consumption amount, an energy consumption index value of the type of vehicle is determined;

[0028] Based on the energy consumption index value and the remaining amount of energy, a range of the type of vehicle is obtained.

[0029] In this way, the second type of demand data corresponding to the second type of original data can be obtained, and the efficiency of obtaining the second type of demand data is improved.

[0030] In a possible design, if the original data is a production amount of each type of vehicle corresponding to each production time node in a set production time range, the following method is used to determine a market demand vehicle model:

[0031] Based on the production amount of each type of vehicle corresponding to each production time node, a total production amount of vehicles of each production time node is determined;

[0032] Based on the obtained total production amount of vehicles of each production time node and the production amount of each type of vehicle corresponding thereto, a vehicle production proportion of each type of vehicle at each production time node is obtained;

[0033] Based on the vehicle production proportion of each type of vehicle at each production time node, a change trend of the vehicle production proportion of each type of vehicle is obtained;

[0034] From the types of vehicles, a target vehicle satisfying a preset vehicle production proportion change trend condition is screened out, and the target vehicle is taken as the market demand vehicle model.

[0035] In this way, the third type of demand data corresponding to the third type of original data can be obtained, and the efficiency of obtaining the third type of demand data is improved.

[0036] In a possible design, if the original data is fault data of each type of vehicle in a set historical time range in the target scene, the following method is used to determine a version configuration of each type of vehicle:

[0037] For each type of vehicle, the following operations are performed respectively:

[0038] obtaining respective fault codes and fault frequencies of each type of fault from fault data of the one type of vehicle in the target scene within the set time range;

[0039] obtaining a fault index value of the one type of vehicle based on the respective fault codes and the respective corresponding fault frequencies;

[0040] determining a version configuration of the one type of vehicle based on a fault index interval to which the fault index value belongs.

[0041] In a specific use environment, the respective fault data of each type of vehicle is classified and counted according to different fault types, to obtain respective fault codes and fault frequencies corresponding to the respective fault data of each type of vehicle;

[0042] obtaining respective fault index values of each type of vehicle according to the respective fault codes and fault frequencies;

[0043] comparing the fault index values with a set index value to determine a vehicle version configuration that meets the specific use environment.

[0044] In this way, the fourth type of demand data corresponding to the fourth type of original data can be obtained, and the efficiency of obtaining the fourth type of demand data is improved.

[0045] In a possible design, the various data conversion manners based on the obtained various data respectively convert the corresponding original data into target data, to display the target data under a preset data display condition, and the method comprises the following steps:

[0046] receiving a data display request sent by a target terminal, and obtaining respective data display types of various target data from the data display request;

[0047] when it is determined that the various target data all meet the preset data display condition, performing data display on the various target data according to the obtained various data display types.

[0048] In this way, the various target data can be provided with respective required display data to users or manufacturers according to a specific display method.

[0049] In a second aspect, the present application provides a data acquisition system, comprising:

[0050] an acquisition module configured to acquire original data collected by various data collection devices within a set time range, and determine respective original data types of the various original data based on respective data features of the various original data;

[0051] The mapping module is configured to filter out, from a preset set of data conversion manners, a data conversion manner that matches each of the various original data types; wherein each data conversion manner represents a conversion of an original data type of corresponding original data into a target data type;

[0052] The conversion module is configured to convert, based on the obtained various data conversion manners, the corresponding original data into target data respectively;

[0053] The display module is configured to display the target data under a preset data display condition.

[0054] In a possible design, the original data includes but is not limited to any one of the following:

[0055] Running time of each type of vehicle, and vehicle demand amount of each type of vehicle in the running time of the type of vehicle;

[0056] Energy consumption amount and available mileage of each type of vehicle in a specific driving mileage, wherein the available mileage represents a vehicle driving distance corresponding to a remaining energy amount of the vehicle;

[0057] Production amount of each type of vehicle corresponding to each production time node in a set production time range;

[0058] Fault data of each type of vehicle in a set historical time range in the target scenario.

[0059] In a possible design, if the original data is the running time of each type of vehicle and the vehicle demand amount of each type of vehicle in the running time of the type of vehicle, the running time of each type of vehicle and the vehicle demand amount of each type of vehicle are obtained in the following manner:

[0060] The following operations are performed on each type of vehicle respectively:

[0061] Obtaining a respective rotation speed of a type of vehicle in each time period in a set time period;

[0062] Filtering, from the time periods, at least one time period that satisfies a rotation speed interval corresponding to the type of vehicle, according to the rotation speed interval;

[0063] Obtaining, based on the obtained at least one time period, a running time of the type of vehicle, and obtaining, according to a preset vehicle demand statistical rule, a vehicle demand amount of the type of vehicle in the running time.

[0064] In a possible design, if the original data is the energy consumption and the range of each type of vehicle in a specific driving mileage, the energy consumption and the range of each type of vehicle are obtained in the following manner:

[0065] The following operations are respectively performed on the types of vehicles:

[0066] An initial energy amount and a remaining energy amount of a type of vehicle driving in the specific driving mileage are obtained;

[0067] The energy consumption of the type of vehicle in the specific driving mileage is obtained according to the initial energy amount and the remaining energy amount, and an energy consumption index value of the type of vehicle is determined based on the specific driving mileage and the energy consumption;

[0068] The range of the type of vehicle is obtained based on the energy consumption index value and the remaining energy amount.

[0069] In a possible design, if the original data is the production amount of each type of vehicle corresponding to each production time node in a set production time range, the following manner is used to determine the market demand vehicle type:

[0070] The total production amount of vehicles of each production time node is determined based on the production amount of each type of vehicle corresponding to each production time node.

[0071] The vehicle production proportion of each type of vehicle at each production time node is obtained based on the obtained total production amount of vehicles of each production time node and the production amount of each type of vehicle corresponding to each production time node.

[0072] The change trend of the vehicle production proportion of each type of vehicle is obtained based on the vehicle production proportion of each type of vehicle at each production time node.

[0073] A target vehicle satisfying a preset vehicle production proportion change trend condition is selected from the types of vehicles, and the target vehicle is used as the market demand vehicle type.

[0074] In a possible design, if the original data is the fault data of each type of vehicle in a set historical time range in the target scene, the following manner is used to determine the version configuration of each type of vehicle:

[0075] The following operations are respectively performed on the types of vehicles:

[0076] The fault code and the fault frequency of each type of fault are obtained from the fault data of a type of vehicle in the set time range in the target scene.

[0077] Based on the obtained various fault codes and their respective corresponding fault frequencies, a fault indicator value of the vehicle of the type is obtained.

[0078] Based on the fault indicator interval to which the fault indicator value belongs, a version configuration of the vehicle of the type is determined.

[0079] In a specific use environment, the fault data of each type of vehicle is classified and counted according to different fault types, and each fault code and fault frequency corresponding to the fault data of each type of vehicle is obtained.

[0080] According to the various fault codes and fault frequencies, a fault indicator value of each type of vehicle is obtained.

[0081] The fault indicator value is compared with a set indicator value to determine a vehicle version configuration that meets the specific use environment.

[0082] In a possible design, the display module is specifically configured to:

[0083] Receive a data display request sent by a target terminal, and obtain a respective data display type of each of various target data from the data display request.

[0084] When it is determined that each of the various target data meets a preset data display condition, data display is performed on the various target data according to the obtained various data display types.

[0085] In a third aspect, the present application provides an electronic device, comprising:

[0086] A memory for storing a computer program.

[0087] A processor for executing the computer program stored on the memory to implement the steps of the above data acquisition method.

[0088] In a fourth aspect, the present application provides a computer readable storage medium, the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the above data acquisition method.

[0089] The above-mentioned aspects of the second to fourth aspects and the technical effects that can be achieved by each aspect are described above with reference to the technical effects that can be achieved by the first aspect or various possible schemes in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF DRAWINGS

[0090] Figure 1 A flowchart of a data acquisition method provided by the present application;

[0091] Figure 2A data acquisition system architecture schematic diagram is provided for the present application.

[0092] Figure 3 A data acquisition system architecture schematic diagram is provided for the present application.

[0093] Figure 4 A data acquisition system architecture schematic diagram is provided for the present application. DETAILED DESCRIPTION

[0094] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings. The specific operation method in the method embodiment can also be applied to the device embodiment or the system embodiment.

[0095] It should be noted that in the description of the present application, "a plurality of" is understood as "at least two". The association relationship of the associated objects is described, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. A and B are connected, which can represent two cases: A and B are directly connected and A and B are connected through C.

[0096] In addition, in the description of the present application, the words "first", "second", etc. are only used for the purpose of distinguishing the description, and cannot be understood as indicating or implying relative importance, nor can it be understood as indicating or implying order.

[0097] The embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0098] With the rapid development of computer technology, data is widely used in information carrying and display, such as precision marketing or public opinion analysis, so in order to adjust the production strategy and marketing strategy of the product according to the obtained data information, big data technology can be used to collect the attribute set and business demand information of various products in the market, and the corresponding strategy is adjusted through data analysis technology.

[0099] In the prior art, in order to accurately obtain data information, the obtained raw data needs to be processed and reported step by step to obtain the corresponding target data, and finally, whether the attribute set of the target product meets the corresponding business demand is judged through the obtained target data.

[0100] However, using the above data acquisition method, since the obtained raw data needs to be processed and reported step by step, a lot of time is needed for data processing and reporting, so the corresponding target data cannot be obtained in time, and then the attribute set of the target product cannot be directly judged according to the target data whether it meets the corresponding business demand.

[0101] Therefore, in the above manner, the data acquisition efficiency is low.

[0102] Therefore, in order to improve the data acquisition efficiency, the present application provides a data acquisition method, which specifically comprises the following steps.

[0103] It can be seen that, in the above manner, the collected various raw data can be directly converted into various target data according to different data types and data conversion modes corresponding to different data types, so that the number of data processing ports corresponding to various data acquisition devices can be reduced and the efficiency of obtaining various target data can be improved.

[0104] Referring to FIG. 1, Figure 1 The method comprises the following steps.

[0105] S1, obtaining raw data collected by various data acquisition devices within a set time range, and determining the raw data type of each raw data based on the data characteristics of each raw data.

[0106] Firstly, the method provided by the present application can be applied to Figure 2 As shown in FIG. 2, the system architecture comprises a collection device (201a, 201b, 201c), a server 202 and a target terminal 203.

[0107] The number of devices is not limited in the present application, for example, Figure 2 As shown in FIG. 2, only the collection device (201a, 201b, 201c), the server 202 and the target terminal 203 are taken as examples for description, and the functions of the above devices will be briefly introduced.

[0108] The collection device (201a, 201b, 201c) is used to collect various types of raw data; the server 202 is used to process the collected various types of raw data and display the processed various target data according to the data display request received from the target terminal 203; and the target terminal 203 is used to send various target data display requests.

[0109] It should be noted that in the embodiments of the present application, since the various original data obtained are in different time ranges, it is assumed here that the original data collected by each data collection device in any set time range is obtained, and then the various original data obtained is classified according to different data characteristics to determine the original data type corresponding to each of the various original data, thereby obtaining various original data.

[0110] Further, it is assumed that the first type of original data in the various types of original data is the running time of each type of vehicle, and the vehicle demand amount of each type of vehicle in the running time of each type of vehicle, and the running time of each type of vehicle and the vehicle demand amount can be obtained in the following manner:

[0111] Since the types of vehicles are different, it is assumed here that for any type of vehicle, first, the server obtains the corresponding speed of the vehicle in each time period within a day, and then according to the preset speed interval of the vehicle, at least one time period that meets the preset speed interval is selected from each time period. It should be noted here that the starting speed of the vehicle is different in the actual running state of vehicles of different power types, for example, the starting speed of a diesel or methanol engine can be 600 revolutions per minute when it is actually running, and the starting speed of a pure electric motor can be 0 revolutions per minute when it is actually running, i.e. the diesel or methanol engine has an additional minimum idle speed state relative to the pure electric motor. Then, the running time of the vehicle is obtained by accumulating the time periods that meet the preset speed interval, and finally, the vehicle demand amount of the vehicle in the running time is obtained according to the preset vehicle demand statistical rule.

[0112] In a possible implementation, it is assumed that the second type of original data in the various types of original data is the energy consumption and the available mileage of each type of vehicle in a specific driving distance, and the energy consumption and the available mileage of each type of vehicle can be obtained in the following manner.

[0113] Since the types of vehicles are different, it is assumed here that for any type of vehicle, first, the server obtains the energy initial amount and the energy remaining amount of the vehicle driving in a specific driving distance, wherein the specific driving distance can be any kilometer, and 100 kilometers are selected here, and then the energy consumption of the vehicle in the specific driving distance is obtained by calculating according to the energy initial amount and the energy remaining amount of the vehicle, and then the energy consumption index of the vehicle is obtained by calculating according to the energy consumption of the vehicle in the specific driving distance, for example:

[0114] It is assumed that the vehicle is a diesel vehicle and the vehicle is not refueled during the specific driving process, and the energy consumption index of the diesel vehicle under the condition of no refueling can be calculated by the following formula, and W1 represents the energy consumption index of the diesel vehicle:

[0115] W1 = AF1 / (S1-S2) x 100

[0116] wherein the unit of the energy consumption index of the diesel vehicle is L / 100km, AF1 is the energy consumption of the vehicle, and AF1 = F1-F2, F1 is the initial amount of energy, and F2 is the remaining amount of energy; S1 is the corresponding driving distance after the vehicle finishes driving; and S2 is the driving distance when the vehicle starts driving.

[0117] Suppose that a type of vehicle is a diesel vehicle and the vehicle is refueled during a specific driving process, then the energy consumption index of the diesel vehicle under the condition of refueling can be calculated by the following formula, and W1 represents the energy consumption index of the diesel vehicle:

[0118] W1 = AF2 / (S1-S2) x 100

[0119] wherein the unit of the energy consumption index of the diesel vehicle is L / 100km, AF2 is the energy consumption of the vehicle, and AF2 = F1-F2+F3-F4, F1 is the initial amount of energy, F2 is the remaining amount of energy at the beginning of refueling, F3 is the initial amount of energy at the completion of refueling, and F4 is the remaining amount of energy when the vehicle finishes the driving distance; S1 is the corresponding driving distance after the vehicle finishes driving; and S2 is the driving distance when the vehicle starts driving.

[0120] Suppose that a type of vehicle is a pure electric vehicle and the vehicle is not charged during a specific driving process, then the energy consumption index of the pure electric vehicle under the condition of not charging can be calculated by the following formula, and W2 represents the energy consumption index of the pure electric vehicle:

[0121] W2 = (SOC1-SOC2) x C1 / (S1-S2) x 100

[0122] wherein the unit of the energy consumption index of the pure electric vehicle is kWh / 100km, SOC1 is the initial amount of electricity of the vehicle, and SOC2 is the remaining amount of electricity of the vehicle; C1 is the battery capacity, S1 is the corresponding driving distance when the vehicle finishes driving; and S2 is the driving distance when the vehicle starts driving.

[0123] Suppose that a type of vehicle is a pure electric vehicle and the vehicle is charged during a specific driving process, then the energy consumption index of the pure electric vehicle under the condition of charging can be calculated by the following formula, and W2 represents the energy consumption index of the pure electric vehicle:

[0124] W2 = ((SOC1-SOC2) x C1+(SOC3-SOC4) x C2) / (S1-S2) x 100

[0125] Wherein, the unit of the energy consumption index of the pure electric vehicle is kWh / 100km, SOC1 is the initial electric quantity of the vehicle, SOC2 is the remaining electric quantity when the vehicle starts charging, SOC3 is the initial electric quantity when the vehicle charging is completed, and SOC4 is the remaining electric quantity when the driving distance is completed; C1 is the capacity of the battery before charging, C2 is the capacity of the battery after charging, and S1 is the driving distance corresponding to the vehicle when the driving is completed; S2 is the driving distance when the vehicle is driving.

[0126] Finally, based on the obtained energy consumption index value and energy remaining value of the vehicle of a type, the driving distance of the vehicle is calculated through a formula, for example:

[0127] Suppose the vehicle of a type is a diesel vehicle, the driving distance of the diesel vehicle can be calculated by the following formula, and L1 represents the driving distance of the diesel vehicle:

[0128] L1=F2 / (ΔF1 / (S1-S2))=F4 / (ΔF2 / (S1-S2)

[0129] Suppose the vehicle of a type is a pure electric vehicle, the driving distance of the pure electric vehicle can be calculated by the following formula, and L2 represents the driving distance of the pure electric vehicle:

[0130]

[0131] In a possible implementation, suppose the third type of original data in the obtained original data is the production quantity of each type of vehicle corresponding to each production time node in a set production time range, the market demand vehicle model can be determined in the following manner:

[0132] Firstly, the set production time range can be any time period, here, it is assumed that the production time range is from 2016 to 2021, and each production time node is the end of each year, the server obtains the production quantity of each type of vehicle corresponding to each production time node, for example, the production quantity of each type of vehicle at the end of 2016; the production quantity of each type of vehicle at the end of 2017, and obtains the total vehicle production quantity of each production time node.

[0133] Then, based on the obtained total vehicle production quantity of each vehicle and the production quantity of each type of vehicle corresponding to each vehicle, the vehicle production proportion of each type of vehicle at each production time node is obtained through calculation.

[0134] Then, based on the vehicle production proportion of each type of vehicle at each production time node, the change trend of the vehicle production proportion of each type of vehicle can be obtained by drawing a column chart or a line chart.

[0135] Finally, from the various types of vehicles, the target vehicle satisfying the preset vehicle production change trend condition is screened out, and the target vehicle is taken as the vehicle model of market demand.

[0136] In a possible implementation, assuming that the fourth type of original data in the various types of original data is the respective failure data of the various types of vehicles in the target scene within a set historical time range, the respective version configuration of the various types of vehicles can be determined in the following manner:

[0137] Since the types of vehicles are different, here, assuming that for any type of vehicle, first, the target scene can be a vehicle use scene in any place, here assuming a cold weather scene in the north, and the set historical time range can be any time period in any year, here the arbitrary time period is assumed to be from October 2016 to April 2017.

[0138] Then, the server obtains the respective fault codes and fault frequencies of the various types of failure data from the failure data of the vehicles of the type in the target scene within the set time range, wherein the fault code can be a fault identifier of at least one component corresponding to the respective functions of the vehicle, such as oil tank heating, independent heating, and parking air conditioning heating, and the fault frequency indicates the number of failures of at least one component corresponding to the respective functions of the vehicle, such as oil tank heating, independent heating, and parking air conditioning heating.

[0139] Then, according to the obtained various types of fault codes and the respective fault frequencies corresponding to the various types of fault codes, the failure index value of the type of vehicle is obtained, wherein the failure index value is used to measure the degree of fit or applicability of the components of the current type of vehicle to the target scene.

[0140] Finally, according to the fault index interval to which the fault index belongs, the version configuration of the current type of vehicle is determined, for example, the current configuration version of the vehicle is V1.0, and the corresponding component failure type of the configuration version V1.0 in the target scene can be n types, here assuming that the failure type x, the failure frequency corresponding to the failure type x is 10, or the current configuration version of the vehicle is V1.1, and the corresponding component failure type of the configuration version V1.1 in the target scene is also x, and the failure frequency corresponding to the failure type x is 8.

[0141] Further, after obtaining the failure index values of various vehicles in the target scene, according to the preset failure types and the index requirements corresponding to the failure types, a vehicle configuration version with few failure types and low failure frequency is screened out, for example, the current version of the vehicle is V1.0, the failure types are X, Y, and Z, and the number of occurrences of X, Y, and Z is 5, 4, and 5 times respectively; or the current version of the vehicle is V1.1, the failure types are X and Y, and the number of occurrences of X and Y is 4 and 4 times respectively, then version V1.1 is selected as the configuration version of the vehicle in the target scene.

[0142] Therefore, if the data processing ports of various data collection devices can be integrated into a server processing port, centralized processing of various types of raw data can be performed to obtain various types of raw data of various types of vehicles in various set time ranges.

[0143] S2, from the set of preset data conversion modes, a data conversion mode matching each type of raw data is selected.

[0144] In the embodiments of the present application, after the server determines the raw data type of each type of raw data, it can select a data conversion mode matching each type of demand data from the set of preset data conversion modes based on the mapping relationship between the preset raw data type and the data conversion mode.

[0145] For example, with four types of raw data, after the server obtains the four types of raw data, the data conversion mode corresponding to each type of raw data is as shown in Table 1:

[0146] Original data type Data.Type1 Data.Type2 Data.Type3 Data.Type4 Data conversion method Method.1 Method.2 Method.3 Method.4

[0147] Table 1

[0148] Based on the above table, if the server determines that the raw data type is Data.Type1, it can select the data conversion mode Method.1 corresponding to the raw data type Data.Type1 from the set of data conversion modes M{Method.1, Method.2, Method.3, Method.4} based on the mapping relationship between the raw data type Data.Type1 and the data conversion mode.

[0149] In the above manner, the data conversion mode matching each type of raw data can be obtained, and each type of raw data can be converted into a target data format.

[0150] S3, based on the obtained various data conversion modes, the corresponding raw data is converted into target data respectively, so as to display the target data under the preset data display condition.

[0151] Specifically, when the step S3 is performed, the server obtains the data conversion mode matched with each of the various raw data, and then converts the various raw data according to the preset conversion rule based on the obtained data conversion mode matched with each of the various raw data, to obtain various demand data corresponding to the various raw data, and then processes the obtained various demand data by a specific target mode to obtain various target data, for example, by performing specific calculation or analysis on the obtained various demand data to obtain various target data corresponding to the various demand data.

[0152] In the embodiment of the present application, since the various demand data corresponding to the various raw data is obtained, it is assumed that the obtained first demand data is the running time of a type of vehicle and the vehicle demand N3 of the type of vehicle in the running time:

[0153] Firstly, in order to adjust the production of the type of vehicle according to the running time of the type of vehicle and the demand N3 of the type of vehicle in the running time, the server obtains the actual production N1 of the type of vehicle in the respective running time and the production N2 of the type of vehicle on the market, wherein the actual production represents the total number of vehicles produced by the vehicle manufacturer, and the production on the market represents the total number of vehicles on the market after passing the quality inspection.

[0154] Then, the production of the type of vehicle is adjusted according to the demand N3 of the type of vehicle, and the production of the type of vehicle can be adjusted by the following method:

[0155] When the market is in a stable period, the difference between N1 and N2 is maintained within a set range, if the difference between N1 and N2 exceeds the set range, overproduction occurs, and a command to reduce production is issued; if the difference between N1 and N2 is lower than the range value, underproduction occurs, and a command to increase production is issued.

[0156] When the market of the type of vehicle is in an upward period, the difference between N1 and N2 is maintained within a set range, and the production of the vehicle is adjusted by presetting the increment of the vehicle production.

[0157] When the market of the type of vehicle is in a downward period, the difference between N1 and N2 is maintained within a set range, and the production of the vehicle is adjusted by presetting the decrement of the vehicle production.

[0158] Finally, the first target data corresponding to the first demand data is obtained.

[0159] In one possible implementation, it is assumed that the obtained second demand data is the energy consumption and the maximum driving distance of each type of vehicle:

[0160] The server can provide the fuel consumption of each type of vehicle according to the energy consumption and the available mileage of each type of vehicle, and can provide the demand of vehicles in each province-level region according to the map, and sort the demand of vehicles in each province-level region, for example, the demand of vehicles in Hebei is 880, ranking first; the demand of vehicles in Tangshan is 640, ranking second; the demand of vehicles in Xingtai is 150, ranking third; the demand of vehicles in Shijiazhuang is 90, ranking fourth; and the server can push the power vehicle that meets the demand of the user and meets the subsidy policy to the user according to the local car purchase subsidy situation.

[0161] The energy consumption and the available mileage of each type of vehicle can also provide data basis for vehicle manufacturers in the design and improvement of complete vehicles.

[0162] Finally, the second type of target data corresponding to the second type of demand data is obtained.

[0163] In one possible implementation, it is assumed that the third type of demand data obtained is a market demand vehicle model:

[0164] The server can push the demand vehicle model that can be developed preferentially to the vehicle manufacturer according to the market demand vehicle model, and increase the production of the demand vehicle model.

[0165] Finally, the third type of target data corresponding to the third type of demand data is obtained.

[0166] In one possible implementation, it is assumed that the fourth type of demand data obtained is the version configuration of each type of vehicle:

[0167] The server can push the solution of the fault code of the parts corresponding to the version configuration of the vehicle to the user according to the version configuration of each type of vehicle, and provide technical support for the user, and when the use time of the vehicle of the user exceeds the set time or the mileage of the user exceeds the set mileage, the server pushes the maintenance reminder to the user, and provides the nearest service station and service telephone number to the user according to the positioning system of the vehicle. The pushed information can be as follows:

[0168] Dear Mr. (Ms.) X, thank you for choosing our remote type x heavy truck. We would like to inform you that you can get free first maintenance within x months and x kilometers of purchasing the car. Wish you a safe journey.

[0169] Dear Mr. (Ms.) X, your car is close to the service station Service Station. X, and we recommend that you do the first maintenance nearby. Wish you a safe journey. Service Station. X, phone: 131xxx.

[0170] Finally, the fourth type of target data corresponding to the fourth type of demand data is obtained.

[0171] In the embodiments of the present application, after obtaining various types of target data:

[0172] First, the server receives a data display request sent by a target terminal, and obtains the data display types of various types of target data from the data display request. Then, when it is determined that various types of target data meet display conditions, the various types of target data are displayed according to the obtained data display types of various types of target data. For example, to display the first type of target data, the first type of demand data matched with the first type of target data is obtained, and the first type of demand data is processed to obtain the first target data, which is converted to a set platform interface, and finally, a function module implemented by the first type of target data is named as a market sales module for display.

[0173] It should be pointed out here that finally, when the interface is displayed, the function module implemented by the first type of target data is named as a market sales module; the function module implemented by the second type of target data is named as a strategic planning and decision-making module; the function module implemented by the third type of target data is named as a strategic planning and decision-making module; and the function module implemented by the fourth type of target data is named as an after-sales module.

[0174] In summary, the data acquisition method provided by the present application can reduce the number of data processing ports corresponding to various data collection devices, improve the efficiency of acquiring various target data, and provide users or manufacturers with the required display data according to a specific display method.

[0175] Based on the method provided in the above embodiments, the embodiments of the present application further provide a data acquisition system, such as Figure 3 The structure diagram of a data acquisition system in the embodiments of the present application is shown in the figure. The system comprises:

[0176] The acquisition module 301 is configured to acquire various types of raw data collected by various data collection devices within a set time range, and determine the raw data types of the various types of raw data based on the data characteristics of the obtained various types of raw data.

[0177] The mapping module 302 is configured to filter out data conversion modes matched with the various types of raw data from a set of preset data conversion modes, wherein each data conversion mode represents the conversion of the raw data type of the corresponding raw data into a target data type.

[0178] The conversion module 303 is configured to convert the corresponding raw data into target data based on the obtained various data conversion modes.

[0179] The display module 304 is configured to display the target data under preset display conditions.

[0180] In a possible design, the original data includes, but is not limited to, any one of the following:

[0181] running time of each type of vehicle, and vehicle demand of each type of vehicle in the running time of the type of vehicle;

[0182] energy consumption and available mileage of each type of vehicle in a specific driving distance, where the available mileage represents a vehicle driving distance corresponding to a remaining energy amount of the vehicle;

[0183] production amount of each type of vehicle corresponding to each production time node in a set production time range;

[0184] fault data of each type of vehicle in a set historical time range in the target scenario.

[0185] In a possible design, if the original data is the running time of each type of vehicle, and the vehicle demand of each type of vehicle in the running time of the type of vehicle, the running time of each type of vehicle and the vehicle demand of each type of vehicle are obtained in the following manner:

[0186] The following operations are respectively performed for each type of vehicle:

[0187] obtaining a respective rotation speed of a type of vehicle in each time period in a set time range;

[0188] filtering, from the time periods, at least one time period that meets a rotation speed interval corresponding to the type of vehicle according to the rotation speed interval set for the type of vehicle;

[0189] obtaining the running time of the type of vehicle based on the obtained at least one time period, and obtaining the vehicle demand of the type of vehicle in the running time according to a preset vehicle demand statistical rule.

[0190] In a possible design, if the original data is the energy consumption and the available mileage of each type of vehicle in a specific driving distance, the energy consumption and the available mileage of each type of vehicle are obtained in the following manner:

[0191] The following operations are respectively performed for each type of vehicle:

[0192] obtaining an initial energy amount and a remaining energy amount of a type of vehicle driving in the specific driving distance;

[0193] According to the initial energy amount and the residual energy amount, an energy consumption amount of the vehicle of the type in the specific driving distance is obtained, and based on the specific driving distance and the energy consumption amount, an energy consumption index value of the vehicle of the type is determined.

[0194] Based on the energy consumption index value and the residual energy amount, a distance that can be traveled by the vehicle of the type is obtained.

[0195] In a possible design, if the original data is production amounts of vehicles of the types corresponding to respective production time nodes within a set production time range, the following manner is used to determine the market demand vehicle type:

[0196] Based on the production amounts of vehicles of the types corresponding to the respective production time nodes, total production amounts of vehicles of the respective production time nodes are determined.

[0197] Based on the obtained total production amounts of vehicles and the production amounts of vehicles of the types corresponding to the respective total production amounts of vehicles, vehicle production proportions of the vehicles of the types at the respective production time nodes are obtained.

[0198] Based on the vehicle production proportions of the vehicles of the types at the respective production time nodes, a change trend of the vehicle production proportions of the vehicles of the types is obtained.

[0199] From the vehicles of the types, a target vehicle that meets a preset vehicle production proportion change trend condition is selected, and the target vehicle is taken as the market demand vehicle type.

[0200] In a possible design, if the original data is fault data of the vehicles of the types in a set historical time range in the target scene, the following manner is used to determine a version configuration of the vehicle of the type:

[0201] The following operations are respectively performed on the vehicles of the types:

[0202] From the fault data of the vehicle of the type in the set time range in the target scene, fault codes and fault frequencies of respective types of faults are obtained.

[0203] Based on the obtained fault codes and the fault frequencies corresponding to the respective fault codes, a fault index value of the vehicle of the type is obtained.

[0204] Based on a fault index interval to which the fault index value belongs, a version configuration of the vehicle of the type is determined.

[0205] In a specific use environment, the fault data of the vehicles of the types is classified and counted according to different fault types, to obtain respective fault codes and fault frequencies corresponding to the fault data of the vehicles of the types.

[0206] According to the various fault codes and fault frequencies, the fault index values of the various types of vehicles are obtained;

[0207] The fault index values are compared with the set index values to determine the vehicle version configuration that meets the specific use environment.

[0208] In a possible design, the display module 304 is specifically configured to:

[0209] receive a data display request sent by a target terminal, and obtain the respective data display types of various target data from the data display request;

[0210] When it is determined that the various target data all meet the preset data display condition, perform data display on the various target data according to the obtained various data display types.

[0211] Based on the same inventive concept, the embodiments of the present application further provide an electronic device, which can implement the functions of the foregoing data acquisition method, and refer to Figure 4 The electronic device includes:

[0212] at least one processor 401 and a memory 402 connected with the at least one processor 401, and the specific connection medium between the processor 401 and the memory 402 is not limited in the embodiments of the present application, Figure 4 In the foregoing embodiments, the connection between the processor 401 and the memory 402 is taken as an example connected through a bus 400. The bus 400 is represented by a thick line in the foregoing Figure 4 The connection modes between other components are only schematically illustrated, and are not limited. The bus 400 can be divided into an address bus, a data bus, a control bus, and the like, and for the convenience of representation, Figure 4 In the foregoing embodiments, the processor 401 can also be referred to as a controller, and the name is not limited.

[0213] In the embodiments of the present application, the memory 402 stores instructions executable by the at least one processor 401, and the at least one processor 401 can execute the data acquisition method discussed in the foregoing by executing the instructions stored in the memory 402. The processor 401 can implement the functions of various modules in the system shown in Figure 3

[0214] The processor 401 is the control center of the device, can connect all parts of the control device through various interfaces and lines, and through running or executing the instructions stored in the memory 402 and calling the data stored in the memory 402, process data and implement various functions of the device, thereby overall monitoring the device. ​

[0215] In a possible design, the processor 401 can include one or more processing units, and the processor 401 can integrate an application processor and a modem processor, where the application processor mainly processes operating systems, user interfaces, and application programs, and the modem processor mainly processes wireless communication. It can be understood that the modem processor can also not be integrated into the processor 401. In some embodiments, the processor 401 and the memory 402 can be implemented on the same chip, and in some embodiments, they can also be implemented on separate chips respectively.

[0216] The processor 401 can be a general-purpose processor, for example, a central processing unit (CPU), a digital signal processor, an application-specific integrated circuit, a field programmable gate array, or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, and can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the data acquisition method disclosed in combination with the embodiments of the present application can be directly embodied as execution completed by a hardware processor, or executed by a combination of hardware and software modules in the processor.

[0217] The memory 402 is a non-volatile computer readable storage medium, which can be used to store non-volatile software programs, non-volatile computer executable programs, and modules. The memory 402 can include at least one type of storage medium, for example, can include flash memory, a hard disk, a multimedia card, a card-type memory, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), a magnetic storage, a magnetic disk, an optical disk, and the like. The memory 402 is any other medium capable of carrying or storing desired program codes in the form of instructions or data structures and capable of being accessed by a computer, but is not limited thereto. The memory 402 in the embodiments of the present application can also be a circuit or any other device capable of realizing a storage function, for storing program instructions and / or data.

[0218] By designing and programming the processor 401, the codes corresponding to the data acquisition method introduced in the foregoing embodiments can be fixed into the chip, so that the chip can execute the codes when running Figure 1The steps of the data acquisition method of the embodiment shown. How to design and program the processor 401 is known to those skilled in the art, and will not be repeated here.

[0219] Based on the same inventive concept, the embodiments of the present application also provide a storage medium, which stores computer instructions, and when the computer instructions run on a computer, the computer instructions make the computer execute the data acquisition method discussed above.

[0220] In some possible implementation manners, various aspects of the data acquisition method provided by the present application can also be implemented in the form of a program product, which includes program codes for causing the control device to execute the steps in the data acquisition method according to various exemplary embodiments of the present application described above in the specification when the program product runs on the device.

[0221] Those skilled in the art should understand that the embodiments of the present application can be provided in the form of a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer-usable program code.

[0222] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus produce a device implemented in accordance with the flowcharts and / or block diagrams. Figure 1 The function specified in one or more flows and / or blocks Figure 1 The device that implements the function specified in one or more flows and / or blocks.

[0223] These computer program instructions can also be stored in a computer-readable memory that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including instruction devices that implement the flowcharts and / or block diagrams. Figure 1 The function specified in one or more flows and / or blocks Figure 1 The device that implements the function specified in one or more flows and / or blocks.

[0224] These computer program instructions can also be loaded into a computer or other programmable data processing devices, so that a series of operational steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide processes for implementing the functions specified in the flowchart Figure 1 one or more flows and / or blocks Figure 1 one or more blocks or steps of the functions specified in the flowchart

[0225] Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A data acquisition method, characterized by, The method comprises: acquiring various data acquisition devices to collect original data of vehicles within a set time range, and determining respective original data types of the various original data based on respective data characteristics of the obtained various original data; based on a mapping relationship between a preset original data type and a data conversion mode, filtering out a data conversion mode matched with each of the various original data types from a preset data conversion mode set, wherein each data conversion mode represents converting an original data type of corresponding original data into a target data type; based on the obtained various data conversion modes, converting the corresponding original data into target data respectively, and based on a data display request sent by a target terminal, displaying the target data under a preset data display condition, wherein the target data is used to determine whether an attribute set of a target product meets a corresponding business requirement.

2. The method of claim 1, wherein, The original data includes but is not limited to any one of the following: the running time of each type of vehicle, and the vehicle demand amount of each type of vehicle within the respective running time; the energy consumption and the range of each type of vehicle in a specific driving distance, wherein the range represents the vehicle driving distance corresponding to the remaining energy of the vehicle; the production amount of each type of vehicle at each production time node within a set production time range; the fault data of each type of vehicle in a target scene within a set historical time range.

3. The method of claim 2, wherein, If the original data is the running time of each type of vehicle and the vehicle demand amount of each type of vehicle within the respective running time, the running time and the vehicle demand amount of each type of vehicle are obtained in the following manner: For each type of vehicle, the following operations are performed: acquiring the respective rotation speed of a type of vehicle within a set time period; from the rotation speed interval set for the type of vehicle, filtering out at least one time period that meets the rotation speed interval from the time periods; based on the obtained at least one time period, obtaining the running time of the type of vehicle, and according to a preset vehicle demand statistical rule, obtaining the vehicle demand amount of the type of vehicle within the running time.

4. The method of claim 2, wherein, If the original data is the energy consumption and the range of each type of vehicle in a specific driving distance, the energy consumption and the range of each type of vehicle are obtained in the following manner: For each type of vehicle, the following operations are performed: acquiring the initial energy and the remaining energy of a type of vehicle driving in the specific driving distance; based on the initial energy and the remaining energy, obtaining the energy consumption of the type of vehicle in the specific driving distance, and based on the specific driving distance and the energy consumption, determining the energy consumption index value of the type of vehicle; based on the energy consumption index value and the remaining energy, obtaining the range of the type of vehicle.

5. The method of claim 2, wherein, If the original data is the production amount of each type of vehicle at each production time node within a set production time range, the following method is used to determine the market demand model: determining a total vehicle production quantity of each of the production time nodes based on the production quantities of the various types of vehicles corresponding to the production time nodes; obtaining a vehicle production proportion of each of the various types of vehicles at each of the production time nodes based on the obtained total vehicle production quantity and the production quantity of each type of vehicle corresponding to the production time node; obtaining a vehicle production proportion change trend of each of the various types of vehicles based on the vehicle production proportion of each of the various types of vehicles at each of the production time nodes; selecting a target vehicle that meets a preset vehicle production proportion change trend condition from the various types of vehicles, and taking the target vehicle as the market demand vehicle model.

6. The method of claim 2, wherein, If the original data is failure data of each of the various types of vehicles in a set historical time range in the target scene, the version configuration of each of the various types of vehicles is determined in the following manner: For each of the various types of vehicles, the following operations are performed: obtaining a fault code and a fault frequency of each type of fault from the failure data of a type of vehicle in the target scene in the set time range; obtaining a fault index value of the type of vehicle based on each fault code and the fault frequency corresponding to the fault code; determining the version configuration of the type of vehicle based on the fault index interval to which the fault index value belongs; In a specific use environment, the failure data of each of the various types of vehicles is classified and counted according to different fault types, to obtain each fault code and fault frequency corresponding to the failure data of each of the various types of vehicles; obtaining a fault index value of each of the various types of vehicles according to the fault code and the fault frequency; comparing the fault index value with a set index value to determine a vehicle version configuration that meets the specific use environment.

7. The method of any one of claims 1-6, wherein, The various data conversion manners are used to convert the corresponding original data into target data for data display under preset data display conditions, including: receiving a data display request sent by a target terminal, and obtaining a data display type of each of the various target data from the data display request; when it is determined that the various target data all meet the preset data display condition, performing data display on the various target data according to the obtained data display types.

8. A data acquisition system characterized by, including: an acquisition module configured to acquire original data of vehicles collected by various data collection devices in a set time range, and determine an original data type of each of the various original data based on a data feature of each of the various original data; a mapping module configured to filter, from a set of preset data conversion manners, a data conversion manner matched with each of the various original data types based on a mapping relationship between the preset original data types and the data conversion manners, wherein each data conversion manner represents a conversion of an original data type of corresponding original data into a target data type; a conversion module configured to convert, based on the obtained various data conversion manners, the corresponding original data into target data, the target data being used to determine whether an attribute set of a target product meets a corresponding business requirement; A display module is configured to display the target data under preset display conditions based on a received data display request sent by a target terminal.

9. An electronic device, comprising: The application relates to a computer program product and a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method steps in any one of claims 1-7. The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method steps in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, ​

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