Cloud data testing method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of data testing, and discloses a cloud data testing method and device, computer equipment and a storage medium, the cloud data uploaded to a cloud end is obtained according to a corresponding mode based on the network address type of a testing scene, and the application scene is wide. And then, according to the data packet type of the cloud data, accurately analyzing the cloud data according to a corresponding mode, and storing an analysis signal and a signal value obtained by analysis. Statistical analysis is automatically carried out on the stored analysis data in combination with the service requirements of the test scene, a tester does not need to carry out manual test and data analysis according to the specific requirements of the test scene, and the cloud data test efficiency is improved. The cloud result statistics of the first analysis signal, the historical signal value statistics of the second analysis signal and the jump condition statistics of the signal value of the third analysis signal are supported, and the service demand can be expanded in combination with the test scene, so that the expansibility is higher.

Description

Technical Field

[0001] The present invention relates to the technical field of data testing, and particularly relates to a method and device for testing data migrated to the cloud, a computer device, and a storage medium. Background Art

[0002] Data migration to the cloud is the process of migrating data to the cloud. To ensure the integrity, consistency, and other performance of the data migrated to the cloud, a series of tests and validations need to be performed on the data migrated to the cloud.

[0003] Since the data migrated to the cloud is a string assembled and compressed through a series of packetization rules and is not intuitive, testers need to manually parse and analyze the data migrated to the cloud to test whether the upload result is correct. However, the business requirements of the test scenarios are complex and diverse, and testers need to perform different data parsing and data analysis in combination with the actual business requirements, resulting in extremely low efficiency and seriously affecting the test efficiency. Summary of the Invention

[0004] In view of this, the present invention provides a method and device for testing data migrated to the cloud, a computer device, and a storage medium to solve the problem of low manual test efficiency in the case of complex and diverse test scenarios.

[0005] In a first aspect, the present invention provides a method for testing data migrated to the cloud, the method comprising:

[0006] Obtaining the data migrated to the cloud according to an obtaining method corresponding to the network address type of the test scenario;

[0007] Parsing the data migrated to the cloud according to a parsing method corresponding to the data packetization type of the data migrated to the cloud to obtain parsed data, and storing the parsed data; wherein, the parsed data includes a parsing signal and a signal value corresponding to the parsing signal;

[0008] Statistically analyzing the stored parsed data according to the business requirements of the test scenario to obtain a test result; wherein, the business requirements include at least some of the following items: statistically analyzing a first parsing signal, statistically analyzing the historical signal values of a second parsing signal, and statistically analyzing the jump condition of the signal value of a third parsing signal.

[0009] Beneficial effects: Based on the network address type of the test scenario, the present application obtains the data uploaded to the cloud in a corresponding manner, with a wide range of applicable scenarios. Then, according to the data packet type of the data uploaded to the cloud, the data uploaded to the cloud is accurately parsed in a corresponding manner, and the parsed signals and signal values obtained by parsing are stored. The stored parsed data is automatically statistically analyzed in combination with the business requirements of the test scenario, eliminating the need for testers to conduct manual tests and data analysis for the specific requirements of the test scenario, and improving the test efficiency of the data uploaded to the cloud. It not only supports the statistical analysis of the cloud upload results of the first parsed signal, the statistical analysis of the historical signal values of the second parsed signal, and the statistical analysis of the signal value jump conditions of the third parsed signal, but also the business requirements can be extended in combination with the test scenario, with stronger scalability.

[0010] In an optional implementation manner, when the business requirements of the test scenario include statistically analyzing the signal value jump conditions of the third parsed signal, the stored parsed data is statistically analyzed according to the business requirements of the test scenario to obtain test results, including:

[0011] The third parsed signal is sequentially collected from the stored parsed data. If the current signal value of the third parsed signal collected this time is inconsistent with the historical signal value of the third parsed signal collected last time, it is determined that the jump condition of the third parsed signal has occurred; among them, if the third parsed signal is collected for the first time, it is determined that the jump condition of the third parsed signal has not occurred;

[0012] According to the jump condition of the third parsed signal and the current signal value, the test result is obtained.

[0013] Beneficial effects: In the embodiment of the present application, by comparing the current signal value and the historical signal value of the third parsed signal, the jump condition of the third parsed signal is determined, the jump condition of the third parsed signal and the current signal value of the jump are statistically analyzed, and the cloud upload results of the changing collected signals are statistically analyzed, eliminating the need for testers to analyze whether the third parsed signal has jumped and the jump result, and improving the test efficiency.

[0014] In an optional implementation manner, when the data packet type of the data uploaded to the cloud is an identification type, the data uploaded to the cloud is parsed according to the parsing method corresponding to the data packet type of the data uploaded to the cloud to obtain parsed data, including:

[0015] The data uploaded to the cloud is parsed into a first timestamp, a first data frame identifier, and a first data frame;

[0016] According to the pre-stored protocol matrix, the first data frame identifier, and the first data frame, the parsed signal and the signal value corresponding to the parsed signal are obtained.

[0017] Beneficial effects: The present application parses the cloud - uploaded data of the identification type into a first timestamp, a first data - frame identifier, and a first data - frame, and parses and calculates the first data - frame identifier and the first data - frame according to the mapping relationship of the protocol matrix to obtain a parsed signal and a signal value for cloud - uploaded data testing.

[0018] In an alternative embodiment, when the data packet type of the cloud - uploaded data is of the signal type, the cloud - uploaded data is parsed according to the parsing method corresponding to the data packet type of the cloud - uploaded data to obtain parsed data, including:

[0019] Parse the cloud - uploaded data into a second timestamp and a second data - frame; wherein, the second data - frame includes a second data - frame identifier, the number of signals, and signal content, and the signal content includes at least some of the following items: timestamp length, signal start bit, relative timestamp, and signal value;

[0020] According to the pre - stored protocol matrix and the second data - frame, obtain the parsed signal and the signal value corresponding to the parsed signal.

[0021] Beneficial effects: The present application parses the cloud - uploaded data of the signal type into a second timestamp and a second data - frame, and through multiple parses of the second data - frame, obtains the second data - frame identifier and the signal value of the cloud - uploaded data, and then obtains the signal name of the parsed signal according to the mapping relationship between the data - frame identifier and the signal name in the protocol matrix. Thus, by parsing the signal name and signal value of the cloud - uploaded data of the signal type, the data readability is improved.

[0022] In an alternative embodiment, the method further includes:

[0023] Obtain the generation requirements of the data to be generated; the generation requirements include at least some of the following items: signal name, data packet type, signal value, data content change type, and data cycle change type;

[0024] Generate target data based on the pre - stored protocol matrix and the generation requirements of the data to be generated.

[0025] Beneficial effects: The present application collects generation requirements such as the signal name, data packet type, data content change type, and data cycle change type of the data to be generated, determines the signal requirement items of the data to be generated according to the data fields defined in the protocol matrix, and generates target data, thereby realizing batch construction of data by combining different generation requirements.

[0026] In an alternative embodiment, obtain the cloud - uploaded data according to the acquisition method corresponding to the network address type of the test scenario, including:

[0027] If the network address type of the test scenario is an internal network, call the internal network login interface and the first query interface to obtain the original data, and call the gateway interface according to the request identifier of the original data to obtain the data uploaded to the cloud;

[0028] If the network address type of the test scenario is a public network, call the second query interface on the cloud to obtain the data uploaded to the cloud.

[0029] Beneficial effects: When the test scenario is an internal network environment, this application authenticates the identity by calling the internal network login interface, and then legally accesses the first query interface to obtain the original data before uploading to the cloud. The decrypted data corresponding to the gateway interface is queried through the request identifier in the original data, so as to obtain the data uploaded to the cloud. If the test scenario is a public network environment, the second query interface on the public network cloud can be directly called to quickly obtain the data uploaded to the cloud. By combining the network address type of the test scenario, a reasonable method is selected to obtain the data uploaded to the public network cloud platform, and the applicable scenarios are extensive.

[0030] In an optional implementation manner, the method further includes:

[0031] Obtain the target original data of the source device and the target data uploaded to the cloud after the source device uploads the target original data to the cloud;

[0032] Perform message conversion on the target original data to obtain a first data list, and perform parsing on the target data uploaded to the cloud to obtain a second data list;

[0033] Compare the first data list and the second data list to obtain a consistency test result.

[0034] Beneficial effects: This application converts the target original data of the source device into a first data list, obtains the target data uploaded to the cloud for the corresponding time period according to the content of the original data, parses and stores the target data uploaded to the cloud as a second data list, and determines whether the data uploaded to the cloud is consistent with the original data by comparing the content of the first data list and the second data list, so as to obtain a consistency test result and realize the accuracy test of data uploading to the cloud.

[0035] In a second aspect, the present invention provides a device for testing data uploaded to the cloud, and the device includes:

[0036] A first processing module, configured to obtain the data uploaded to the cloud according to an acquisition method corresponding to the network address type of the test scenario;

[0037] A second processing module, configured to parse the data uploaded to the cloud according to a parsing method corresponding to the data packet type of the data uploaded to the cloud to obtain parsed data, and store the parsed data; wherein, the parsed data includes a parsed signal and a signal value corresponding to the parsed signal;

[0038] A third processing module, configured to perform statistics on the stored parsed data according to the service requirements of the test scenario to obtain a test result; wherein, the service requirements include at least some of the following items: statistics on the first parsed signal, statistics on the historical signal values of the second parsed signal, and statistics on the jump situation of the signal values of the third parsed signal.

[0039] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to execute the cloud data testing method according to the first aspect or any corresponding embodiment thereof.

[0040] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored. The computer instructions are used to cause a computer to execute the cloud data testing method according to the first aspect or any corresponding embodiment thereof.

[0041] The beneficial effects of the present application are as follows:

[0042] Based on the network address type of the test scenario, the cloud data uploaded to the cloud is obtained in a corresponding manner, and the applicable scenarios are wide. Then, according to the data packet type of the cloud data, the cloud data is accurately parsed in a corresponding manner, and the parsed signals and signal values obtained by parsing are stored. Automatically perform statistical analysis on the stored parsed data in combination with the service requirements of the test scenario, without the need for testers to perform manual testing and data analysis for the specific requirements of the test scenario, improving the testing efficiency of the cloud data. It not only supports the statistics of the cloud results of the first parsed signal, the statistics of the historical signal values of the second parsed signal, and the statistics of the jump situation of the signal values of the third parsed signal, but also the service requirements can be extended in combination with the test scenario, with stronger scalability. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. 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.

[0044] Figure 1 is a schematic flowchart of the cloud data testing method according to an embodiment of the present invention;

[0045] Figure 2 is a schematic flowchart of another cloud data testing method according to an embodiment of the present invention;

[0046] Figure 3It is a schematic flowchart of a data query method according to an embodiment of the present invention;

[0047] Figure 4 It is a schematic flowchart of another cloud data testing method according to an embodiment of the present invention;

[0048] Figure 5 It is a schematic flowchart of a data generation method according to an embodiment of the present invention;

[0049] Figure 6 It is a schematic flowchart of a data comparison method according to an embodiment of the present invention;

[0050] Figure 7 It is a schematic diagram of the operation of a cloud data testing tool according to an embodiment of the present invention;

[0051] Figure 8 It is a schematic diagram of the statistics of signal - type cloud data according to an embodiment of the present invention;

[0052] Figure 9 It is a schematic diagram of the statistics of identification - type cloud data according to an embodiment of the present invention;

[0053] Figure 10 It is a structural block diagram of a cloud data testing device according to an embodiment of the present invention;

[0054] Figure 11 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0055] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0056] According to an embodiment of the present invention, an embodiment of a cloud data testing method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer - executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0057] In this embodiment, a cloud data testing method is provided, which can be used in devices for data testing, such as tablet computers, computers, etc. Figure 1 It is a flowchart of the cloud data testing method according to an embodiment of the present invention, as Figure 1As shown, the process includes the following steps:

[0058] Step S101: Obtain the data uploaded to the cloud according to the acquisition method corresponding to the network address type of the test scenario.

[0059] Specifically, the data uploaded to the cloud refers to the data after uploading the original data of the source device to the cloud. Here, the source device is the device that generates the original data, such as the vehicle terminal.

[0060] In step S101, the original data needs to go through a series of operations such as storage, encryption, and decryption before being uploaded to the cloud. Due to factors such as transmission errors, the original data uploaded by the source device may not be the same as the data uploaded to the cloud. Therefore, when testing the data uploaded to the cloud, the original data uploaded by the source device cannot be directly used as the data uploaded to the cloud, but the data uploaded to the cloud needs to be obtained from relevant nodes in the cloud. The cloud usually has a public network address. Different network address types of the test scenario result in different ways for the test device to access the public cloud data and different ways to obtain the data uploaded to the cloud in the cloud.

[0061] Specifically, the network address types of the test scenario include the internal network and the public network (i.e., the external network). It is possible to determine whether the test device is located in the internal network or the public network according to the range of the subnet mask of the network address (Internet Protocol Address, IP Address) of the test scenario. If the subnet mask conforms to the internal network address range, the test scenario is the internal network; if the subnet mask conforms to the public network address range, the test scenario is the public network. The test device can obtain the data uploaded to the public cloud in both the internal network and the public network, with a wide range of applicable scenarios.

[0062] Step S102: Parse the data uploaded to the cloud according to the parsing method corresponding to the data packet type of the data uploaded to the cloud to obtain the parsed data, and store the parsed data; where the parsed data includes the parsed signal and the signal value corresponding to the parsed signal.

[0063] In the embodiment of the present application, the source device, such as the vehicle terminal, can upload the original data to the cloud through multiple communication protocols. The protocol matrix is used to define and describe the communication methods at the protocol layer, the data field definitions of different signals, the data packet rules, and the data upload logic included in each communication protocol. Among them, the communication protocol can include the "Tag-Length-Value" (TLV) protocol, the Message Queuing Telemetry Transport (MQTT) protocol, etc. The protocol matrix can be in the form of an excel table, a txt text document, etc., and can be specifically set according to the requirements of the actual scenario.

[0064] Specifically, a uf field can be set in the cloud-bound data to represent the data packet type of the cloud-bound data, and the data packet types corresponding to the uf field values are defined, so as to determine the data packet type according to the uf field value in the cloud-bound data. For example, if uf = 1 or uf = 3, the cloud-bound data is an identification-type data packet; if uf = 0 or uf = 2, the cloud-bound data is a signal-type data packet, which can be specifically set according to the actual scenario, and this application is not limited thereto.

[0065] In step S102, multiple pieces of cloud-bound data can be obtained, and each piece of cloud-bound data is stored in a list list. Traverse list, identify the data packet type of each piece of cloud-bound data, parse it in the corresponding manner, and obtain the parsed signal and the corresponding signal value according to the definition of the protocol matrix.

[0066] Step S103, statistically analyze the stored parsed data according to the service requirements of the test scenario to obtain a test result; wherein, the service requirements include at least some of the following items: statistically analyze the first parsed signal, statistically analyze the historical signal values of the second parsed signal, and statistically analyze the jump situation of the signal values of the third parsed signal.

[0067] Taking the communication protocol as the MQTT protocol as an example, when using communication protocols such as the MQTT protocol for vehicle-side data cloud upload and cloud-bound data testing, it is necessary to perform raw data sending, cloud-bound data parsing, and test result analysis based on the content defined by the protocol matrix for the MQTT protocol to verify whether the data uploaded to the cloud is accurate. However, the cloud-bound data in the cloud is a string assembled and compressed through a series of packet rules, with poor intuitiveness and unable to directly confirm whether the cloud-bound data is correct.

[0068] In the embodiment of this application, the stored parsed data is automatically statistically analyzed by identifying the service requirements of the test scenario, supporting complex data analysis such as the cloud upload result statistics of the first parsed signal, the historical signal value statistics of the second parsed signal, and the jump situation statistics of the signal values of the third parsed signal. The parsing efficiency is high, and there is no need for testers to perform manual testing and data analysis for the specific requirements of the test scenario, greatly improving the testing efficiency of the cloud-bound data. Moreover, it can be extended in combination with the actual service requirements, with stronger scalability.

[0069] The cloud data testing method provided in this embodiment obtains the cloud data uploaded to the cloud in a corresponding manner based on the network address type of the test scenario, and has a wide range of applicable scenarios. Then, according to the data packet type of the cloud data, the cloud data is accurately parsed in a corresponding manner, and the parsed signals and signal values obtained by parsing are stored. Automatically perform statistical analysis on the stored parsed data in combination with the business requirements of the test scenario, without the need for testers to conduct manual testing and data analysis for the specific requirements of the test scenario, improving the testing efficiency of the cloud data. It not only supports the statistical analysis of the cloud results of the first parsed signal, the statistical analysis of the historical signal values of the second parsed signal, and the statistical analysis of the signal jump conditions of the third parsed signal, but also the business requirements can be extended in combination with the test scenario, with stronger scalability.

[0070] In this embodiment, a cloud data testing method is provided, which can be used in devices for data testing, such as tablet computers, computers, etc. Figure 2 It is a flowchart of the cloud data testing method according to an embodiment of the present invention, as Figure 2 shown, and this process includes the following steps:

[0071] Step S201, obtain the cloud data according to the acquisition method corresponding to the network address type of the test scenario.

[0072] Specifically, the above step S201 includes:

[0073] Step S2011, if the network address type of the test scenario is an intranet, call the intranet login interface and the first query interface to obtain the original data, and call the gateway interface according to the request identifier of the original data to obtain the cloud data.

[0074] In the embodiment of the present application, after the source device generates the original data in the intranet, the original data is temporarily stored in a paged manner through the bridge service, and the original data is encrypted and transmitted to the gateway. The gateway decrypts the encrypted original data and uploads the decrypted data to the cloud.

[0075] Specifically, when the test device obtains the cloud data, such as Figure 3As shown in the figure, if the test device is in an intranet environment, the intranet login interface is called to obtain the authentication file cookie for securely and legally querying data. Then, the first query interface in the intranet is called to obtain the original data uploaded to the cloud through the bridge service, and it is determined whether the number of data items on the current page exceeds the data item threshold. If the determination result is yes, it indicates that there is still data on the next page that has not been obtained. Then, by modifying the value of the recursive parameter, the acquisition of data on the next page is entered. If the determination result is no, it indicates that there is no more data and the data acquisition is complete, and thus the data query is completed through recursion. Among them, the data item threshold is the upper limit of the list returned by the first query interface, which can be 500 and can be specifically set according to the actual scenario. This application is not limited thereto.

[0076] Further, through the request identifier req_id in the original data, the corresponding decrypted data in the gateway is queried, and the content part of the decrypted data returned by the gateway interface is stored in the list to obtain the data uploaded to the cloud. It should be noted that before each original data is uploaded to the cloud through the bridge service, a unique request identifier req_id will be assigned. The request identifier req_id is used to associate the corresponding encryption key or encrypted data block in the gateway.

[0077] In some alternative embodiments, the open-source tool Kibana can be deployed in the intranet to facilitate testers to obtain the data uploaded to the cloud in a visual manner and improve the data analysis efficiency.

[0078] Step S2012, if the network address type of the test scenario is public network, call the second query interface in the cloud to obtain the data uploaded to the cloud.

[0079] Specifically, when the test device obtains the data uploaded to the cloud, refer to Figure 3 again. If the test device is in a public network environment, directly call the second query interface in the cloud for message tracking, query the gateway node data, and obtain the data uploaded to the cloud.

[0080] In the embodiments of this application, if the test scenario is an intranet environment, identity authentication is performed by calling the intranet login interface, and then the original data before being uploaded to the cloud is legally accessed through the first query interface. The corresponding decrypted data of the gateway interface is queried through the request identifier in the original data, so as to obtain the data uploaded to the cloud. If the test scenario is a public network environment, the second query interface in the cloud in the public network can be directly called to quickly obtain the data uploaded to the cloud. By combining the network address type of the test scenario, a reasonable method is selected to obtain the data uploaded to the cloud platform in the public network, and the applicable scenarios are extensive.

[0081] Step S202: Parse the data uploaded to the cloud according to the parsing method corresponding to the data packet type of the data uploaded to the cloud, obtain the parsed data, and store the parsed data. The parsed data includes parsed signals and signal values corresponding to the parsed signals.

[0082] Specifically, the above step S202 includes:

[0083] Step S2021: When the data packet type of the data uploaded to the cloud is an identification type, parse the data uploaded to the cloud into a first timestamp, a first data frame identifier, and a first data frame. According to the pre-stored protocol matrix, the first data frame identifier, and the first data frame, obtain the parsed signal and the signal value corresponding to the parsed signal.

[0084] Specifically, if the data packet uploaded to the cloud is an identification type data packet, then parse the data uploaded to the cloud in the manner of [First Timestamp + First Data Frame Identification + First Data Frame].

[0085] In some alternative embodiments, the identification type data packet may be a canid type packet, and the data uploaded to the cloud can be parsed in the manner of [First Timestamp + canid + content].

[0086] In some embodiments, the number of the first data frames is at least one (N≥1), and each first data frame corresponds to a first data frame identifier. Taking the data packet as a canid type packet as an example, the data uploaded to the cloud can be parsed in the manner of [First Timestamp + canid1 + content1 + canid2 + content2 +...... + canidN + contentN].

[0087] In some embodiments, the identification type data uploaded to the cloud can be stored in a readable [canid - data frame] structure.

[0088] In the embodiments of the present application, referring again to Figure 3 , parse the pre-stored protocol matrix, such as an excel document, and store the parsing result as a protocol dictionary dict. Among them, different network segments, different network segment types, and different controllers correspond to different logical subnets subnet, and the protocol dictionary dict can store the data fields defined for different data packets according to subnet. For example, the key of the data field can be the data frame identifier canid, and the value of the data field is the signal name name, start bit startbit, length length, scale factor factor, offset offset, byte segment bytes_Segment, and the corresponding value included in the signal corresponding to the data frame identifier canid.

[0089] In step S2021, according to the first data frame identifier and the first data frame, calculate the signal values in each frame of data according to startbit, length, factor, offset, bytes_Segment, etc. corresponding to the first data frame identifier canid in the protocol dictionary corresponding to the protocol matrix, and store them.

[0090] In the embodiment of the present application, the cloud-based data of the identification class is parsed into a first timestamp, a first data frame identifier, and a first data frame, and according to the mapping relationship of the protocol matrix, the first data frame identifier and the first data frame are parsed and calculated to obtain the parsed signal and the signal value, so as to perform cloud-based data testing.

[0091] In step S2022, when the data packet type of the cloud-based data is of the signal class, parse the cloud-based data into a second timestamp and a second data frame; obtain the parsed signal and the signal value corresponding to the parsed signal according to the pre-stored protocol matrix and the second data frame.

[0092] It should be noted that the second data frame includes a second data frame identifier, the number of signals, and signal content, and the signal content includes at least some of the following items: timestamp length, signal start bit, relative timestamp, and signal value.

[0093] Specifically, if the cloud-based data is a signal-class data packet, parse the cloud-based data in the manner of [second timestamp + content], where content needs to be parsed a second time in the manner of [canid + signalCount + signalContent]. The number of signals signalCount represents the number of signals included in signalContent, and signalContent needs to be parsed a third time in the manner of [timestamp length + signal start bit + relative timestamp + signal value]. The number of parsing times of signalContent corresponds to signalCount.

[0094] In some embodiments, the number of second data frames is at least one (M≥1), and the cloud-based data is parsed in the manner of [second timestamp + content1 + content2 +...... + contentM].

[0095] In some embodiments, determine the second data frame identifier canid and the signal value corresponding to the signal-class data packet by parsing the second data frame, and according to the signal name name corresponding to the second data frame identifier canid in the protocol dictionary corresponding to the protocol matrix, convert the cloud-based data into a readable [signal name - signal value] structure.

[0096] In the embodiments of the present application, the signal - type cloud - uploaded data is parsed into a second timestamp and a second data frame. By parsing the second data frame multiple times, the second data - frame identifier and the signal value of the cloud - uploaded data are obtained. Then, according to the mapping relationship between the data - frame identifier and the signal name in the protocol matrix, the signal name of the parsed signal is obtained. Thus, by parsing the signal name and the signal value of the signal - type cloud - uploaded data, the data readability is improved.

[0097] Step S203: Statistically analyze the stored parsed data according to the service requirements of the test scenario to obtain the test result.

[0098] Specifically, the above - mentioned step S203 includes:

[0099] Step S2031: Statistically analyze the first parsed signal.

[0100] Specifically, the identification - type cloud - uploaded data can be stored in a structure of

canid - data frame

signal name - signal value

[0101] In some embodiments, referring again to Figure 3 , the first parsed signal can be a canid - type signal. Determine the target canid of the first parsed signal, collect the canid - type signals matching the target canid in the stored parsed data, and statistically analyze whether the canid signals in the collection requirements are uploaded to the cloud to obtain the cloud - uploading result of the canid - type original data with the canid being the target canid.

[0102] In some embodiments, the target signal name of the first parsed signal can be specified, and by statistically analyzing the parsed data matching the target signal name, the cloud - uploading result of the original data with the signal name being the target signal name is determined.

[0103] Step S2032: Statistically analyze the historical signal values of the second parsed signal.

[0104] Specifically, referring again to Figure 3 , determine the target canid or the target signal name of the second parsed signal, and determine the target time period. Query the signal values of the parsed signals uploaded to the cloud within the target time period and matching the target canid or the target signal name in the stored parsed data, so as to analyze the historical signal values of the target signal uploaded to the cloud within the target time period.

[0105] Step S2033: Sequentially collect the third parsed signal in the stored parsed data. If the current signal value of the third parsed signal collected this time is inconsistent with the historical signal value of the third parsed signal collected last time, determine that the jump situation of the third parsed signal has occurred; obtain the test result according to the jump situation and the current signal value of the third parsed signal.

[0106] It should be noted that if the third parsed signal is collected for the first time, it is determined that there is no jump in the jump situation of the third parsed signal. Among them, the third parsed signal is a variable acquisition signal, and the variable acquisition signal refers to a signal that triggers signal acquisition and cloud upload when the signal value changes. The canid and signal name included in the variable acquisition signal can be defined.

[0107] In some embodiments, referring again to Figure 3 , filter the variable acquisition signals in the acquisition list, initialize the jump result dictionary, initialize the acquisition results of all variable acquisition signals to "not acquired", and then collect the variable acquisition signals in the stored parsed data. If the variable acquisition signal is collected for the first time, modify the acquisition result to false (that is, the jump situation is that no jump occurs), and save the signal value; if the variable acquisition signal is not the first occurrence and the current signal value is inconsistent with the saved signal value, modify the acquisition result to true (that is, the jump situation is that a jump occurs), and save the current signal value. Finally, write the acquisition result of the variable acquisition signal and the current signal value into the test result.

[0108] In the embodiment of the present application, by comparing the current signal value and the historical signal value of the third parsed signal, the jump situation of the third parsed signal is determined, the jump situation of the third parsed signal and the current signal value of the jump are counted, and the cloud upload result of the variable acquisition signal is counted. There is no need for testers to analyze whether the third parsed signal has a jump and the jump result, which improves the test efficiency.

[0109] The cloud upload data testing method provided in this embodiment first determines whether the test scenario is an intranet or a public network, and selects a reasonable method to obtain the cloud upload data uploaded to the cloud platform of the public network, which has a wide range of applicable scenarios. Then, the cloud upload data of the identification type is parsed into the first timestamp, the first data frame identifier, and the first data frame, and according to the mapping relationship of the protocol matrix, the parsed signal and the signal value, as well as the signal name and signal value of the cloud upload data through the parsed signal class, are obtained, which improves the data readability. Finally, according to different service requirements, the stored parsed data is automatically counted and analyzed, which improves the test efficiency of the cloud upload data.

[0110] In this embodiment, a cloud upload data testing method is provided, which can be used for devices for data testing, such as tablet computers, computers, etc. Figure 4 is a flowchart of the cloud upload data testing method according to the embodiment of the present invention, as Figure 4 shown, and this process includes the following steps:

[0111] Step S401, obtain the cloud upload data according to the acquisition method corresponding to the network address type of the test scenario. Specifically, refer to Figure 2The description of step S201 of the illustrated embodiment will not be repeated here.

[0112] Step S402: Parse the data uploaded to the cloud according to the parsing method corresponding to the data packet type of the data uploaded to the cloud to obtain parsed data, and store the parsed data; wherein, the parsed data includes parsed signals and signal values corresponding to the parsed signals. For details, reference can be made to Figure 2 The description of step S202 of the illustrated embodiment will not be repeated here.

[0113] Step S403: Statistically analyze the stored parsed data according to the service requirements of the test scenario to obtain a test result; wherein, the service requirements include at least some of the following items: statistically analyzing the first parsed signal, statistically analyzing the historical signal values of the second parsed signal, and statistically analyzing the jump condition of the signal values of the third parsed signal. For details, reference can be made to Figure 2 The description of step S203 of the illustrated embodiment will not be repeated here.

[0114] Step S404: Obtain the generation requirements of the data to be generated, and generate target data based on the pre-stored protocol matrix and the generation requirements of the data to be generated.

[0115] Specifically, the generation requirements include at least some of the following items: signal name, data packet type, signal value, network segment, data content change type, and data cycle change type. Among them, the signal names of the data to be generated may include controller status signals, fault signals such as Diagnostic Trouble Codes (DTCs), and event condition association signals; the data content change type may include variable data frame content (single signal / multi-signal jump combination, signal value jump, etc.) and immutable data frame content; the data cycle change type may include variable data frame cycle (high frequency, cycle adaptation according to the protocol, etc.) and fixed data frame cycle. The present application is not limited thereto.

[0116] In some alternative embodiments, as Figure 5 shown, convert the generation requirements of the data to be generated into a corresponding acquisition list, determine the signal requirement items of the data to be generated through the data fields defined in the protocol dictionary, then generate a data frame, and finally convert the generated data frame into a list file supported by the software and import it into the software to complete the batch construction of the data.

[0117] In some embodiments, refer again to Figure 5, taking the data frame that generates signal value jumps with data packet grouping as the identification class as an example, when generating data, first generate the initialization data frame of the target signal (all 0s) according to the protocol dictionary, and then generate the data frame after the signal value jump of the target signal. Determine the canid corresponding to the target signal according to the protocol dictionary, and merge the canid corresponding to the target signal and the data frame after the signal value jump, and add them to the target data list.

[0118] In the embodiments of the present application, by collecting generation requirements such as the signal name, data packet grouping type, data content change type, and data cycle change type of the data to be generated, determine the signal requirement items of the data to be generated according to the data fields defined in the protocol matrix, and generate target data, so as to realize the batch construction of data by combining different generation requirements.

[0119] Step S405, obtain the target original data of the source device and the target cloud-uploaded data after the source device uploads the target original data to the cloud, perform message conversion on the target original data to obtain a first data list, and perform parsing on the target cloud-uploaded data to obtain a second data list; compare the first data list and the second data list to obtain a consistency test result.

[0120] In some embodiments, as Figure 6 shown, obtain the original data uploaded by each bus message of the vehicle end, and combine the type of the original data (canid class and signal class), and perform message conversion on the original data in the corresponding manner. If it is canid class original data, convert it to the structure of [canid - data frame], and if it is signal class original data, convert it to the structure of [signal name - signal value] to obtain a first data list.

[0121] Furthermore, referring again to Figure 6 , according to the timestamps of the first data and the last data of the original data, obtain the cloud-uploaded data uploaded to the cloud during the corresponding time period. Then, combine the type of the cloud-uploaded data (canid class and signal class), and perform parsing on the cloud-uploaded data in the corresponding manner. If it is canid class cloud-uploaded data, convert it to the structure of [canid - data frame], and if it is signal class cloud-uploaded data, convert it to the structure of [signal name - signal value] to obtain a second data list.

[0122] Even further, compare the first data list and the second data list, determine the amount of data to be compared according to the set scale factor, and then traverse each canid or signal name in the list to determine whether the cloud-uploaded data can match the original data at the vehicle end to obtain a consistency test result.

[0123] In the embodiment of the present application, the target original data of the source device is converted into a first data list, and the target cloud data for the corresponding time period is obtained according to the content of the original data. The target cloud data is parsed and stored as a second data list. By comparing the content of the first data list and the second data list, it is determined whether the cloud data is consistent with the original data, and a consistency test result is obtained, realizing the accuracy test of data cloudification.

[0124] The cloud data test method provided by the embodiment of the present invention covers aspects such as test data generation, test data query, and test result analysis. It can perform tests on the integrity rate, consistency rate, and success rate of cloud data, and supports the expansion of test scenarios, making the cloud data test efficient, accurate, and the conclusion more comprehensive and reliable.

[0125] Taking the vehicle-end data cloudification as an example, a specific application example is used to illustrate the cloud data test scheme of the present invention in detail.

[0126] This application example provides a cloud data test tool, and the development of this tool includes the following modules: requirement parsing module, protocol parsing module, data generation module, data sending module, data query module, data parsing module, result statistics module, message conversion module, and data analysis module.

[0127] The requirement parsing module is used to traverse and parse the acquisition list, and store the parsed signal requirement items into the signal requirement dictionary, including information such as requirement type, network segment, acquisition mode, controller, canid, and signal name.

[0128] The protocol parsing module is used to parse the content of the protocol matrix and store it into the protocol dictionary, including information such as network segment, network segment type, controller, canid, signal name, Bytes Segment, start bit, length, scale factor, offset, etc.

[0129] The data generation module is used to traverse the signals in the requirement dictionary returned by the requirement parsing module, generate data frames for each data to be generated according to the protocol dictionary returned by the protocol parsing module, and write the relevant information of the data frames (canid, data frame, data type, data frame interval time, etc.) into the data list.

[0130] The data sending module is used to send the data generated by the data generation module to the transmission bus at intervals by loading the common library of the relevant software and calling the associated devices and channels.

[0131] The data query module is used to call the second query interface of the cloud according to the query conditions, obtain the cloud data uploaded by the vehicle during the corresponding time period and save it in the list.

[0132] A data parsing module, which is used to decompress and decode the data obtained by the data query module, and then parse it according to the packet assembly rules, and convert it into readable information such as time, signal name, and signal value.

[0133] A result statistics module, which is used to statistically analyze the data parsed by the data analysis module according to the business type, and can perform statistics according to dimensions such as whether the signal is uploaded to the cloud, whether the signal jumps, and the historical value of the signal uploaded to the cloud, and can be extended in combination with the actual business requirements.

[0134] A message conversion module, which is used to convert the original vehicle data into a message and save it as a first data list, and the first data list can be an excel file.

[0135] A data analysis module, which is used to compare and analyze the second data list saved after parsing by the data parsing module and the first data list of the message conversion module, and judge whether the integrity rate, consistency rate, and success rate of the data meet the cloud uploading business logic according to the results.

[0136] The above cloud uploading data testing tool consists of a runnable program.exe and a configuration file.json. Among them, the content of the configuration file.json fields includes at least the following partial items: the index environment for decrypting cloud uploading data, the vehicle machine identifier tuid, the start time gte for query, the end time lte for query, the directory matrix_file where the protocol matrix file is located, the directory collect_file where the signal acquisition list is located, the directory res_path where the test results are stored, the canid to be queried, i.e., search_id, the English name of the signal to be queried, i.e., search_signal, the network segment signal_net to which the signal to be queried belongs, the vehicle model code series_code, the ratio actor for consistency check (float type data, range 0-1, such as actor is 0.1), the pre-production address url, the data type data_code, and the data packet assembly method uf.

[0137] The usage method of this cloud uploading data testing tool is as follows:

[0138] Step 1, place the configuration file.json and the runnable program.exe in the same directory, update the content of the configuration file.json according to the field content description of the configuration file.json, and then run the tool from the command line.

[0139] Step 2, such as Figure 7As shown, when the tool is initialized, it will verify the protocol matrix information and print the canids in the matrix page of the protocol matrix file that exist but lack the Data Length Code (DLC) information in the overview page. The tester needs to first confirm whether this part of the data involves the requirement of data being uploaded to the cloud.

[0140] Meanwhile, if the following five folders do not exist in the current path of the tool: data_search, data_generate, data_compare, data_transition, and log, they will be created. Among them, data_search is used to store the data queried by the data query service, data_generate is used to store the target data generated by the data generation service, data_compare is used to store the result data of the data analysis service, data_transition is a reserved folder, and log is used to store the log data of the tool.

[0141] Moreover, the tool will create sub-folders with vehicle series codes in each of the folders data_search, data_generate, data_compare, data_transition, and log to distinguish different vehicle model projects. Then, under the sub-folder with the vehicle series code, sub-folders for all data_codes will be created to store different types of data for vehicles with this vehicle series code.

[0142] Step 3, data query.

[0143] Specifically, after running the tool in the command line and referring to Figure 7 again, after selecting the tool type [1. Data Query], there are three business scenarios available:

[0144] Business type 1 is to count the data uploaded to the cloud for signals of a certain data packet type. The query result file will be stored in the data_search / vehicle series code / data_code path, and the file name contains the in-vehicle unit identifier tuid, the data packet type uf, and the time period. As Figure 8 shown, for the document content where uf is 0 or 2 (data parsed as signal types according to the packet rule), the canid, signal name, signal value, and time of the data uploaded to the cloud can be intuitively seen. As Figure 9 shown, for the document content where uf is 1 or 3 (data parsed as canid types according to the packet rule), the canid, data frame, and time of the data uploaded to the cloud can be intuitively seen.

[0145] Service type 2 is to count the cloud - uploading situation of the signal for statistical changes. It is counted according to cani or signal name. The result file will be stored in the data_search / vehicle series code / data_code path. The file name contains the vehicle head unit identifier tuid, the data packet type uf, and the jump result of the changed acquisition signal corresponding to the time period. The file content includes the signal name, the jump acquisition result (not acquired means the signal has not been uploaded to the cloud, no jump means the signal has been uploaded to the cloud but the signal value has not changed, and a jump has occurred means the signal has been uploaded to the cloud and the signal value has changed), and canid, etc., so as to quickly confirm whether the cloud - uploaded data meets the expectations according to the result.

[0146] Service type 3 is to count the historical values of a certain signal uploaded to the cloud. The result file will be stored in the data_search / vehicle series code / data_code path. The file name contains information such as the vehicle head unit identifier tuid, the data packet type uf, the time period, canid, and signal name. The document content includes the historical values of the signal uploaded to the cloud and the corresponding time points, so as to quickly confirm the historical trend of signal changes.

[0147] Step 4, data generation.

[0148] After running the tool in the command line and selecting the tool type [2. Data Generation], relevant data will be generated according to different network segments and different acquisition types in the acquisition list. The console will print the data defined in the acquisition list but not found in the protocol matrix, prompting the tester to confirm whether it is an input error in the document. The generated result file will be stored in the data_generate / vehicle series code path.

[0149] Exemplarily, it can generate the period / change data of the vehicle controller status signal, vehicle fault codes, and event - related data. The generated list file supports batch import into relevant data analysis software and batch sending, etc.

[0150] Step 5, data comparison.

[0151] After running the tool in the command line and selecting the tool type [3. Data Comparison (confirm whether it is at the signal level or canid level according to the set uf)], it will verify the consistency of the cloud - uploaded data and the original vehicle - end data. The messages collected at the vehicle end need to be stored in the data_compare / vehicle series code path. Among them, the vehicle - end messages can be in ASC format, and the file name contains the network segment and CAN signal type, such as INFOCAN_CANFD.ASC (the network segment must be the same as that in the acquisition list / protocol matrix).

[0152] The result file of the consistency comparison will be stored in the path of data_compare / vehicle series code. The file name contains network segment, CAN type, data type, etc. The content of the file is the matching result of the data uploaded to the cloud in the original data at the vehicle end. TRUE indicates consistency, and FALSE indicates inconsistency.

[0153] The present invention can realize functions such as data custom query, cross-platform data generation, data comparison and analysis, etc., support automatic adaptation applications for different projects, and make the data upload to the cloud test experiment have high efficiency, high accuracy, and more comprehensive and reliable conclusions.

[0154] In this embodiment, a data upload to the cloud test device is further provided. This device is used to implement the above-mentioned embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0155] This embodiment provides a data upload to the cloud test device, as Figure 10 shown, including:

[0156] The first processing module 1001 is used to obtain the data uploaded to the cloud according to the acquisition method corresponding to the network address type of the test scenario.

[0157] The second processing module 1002 is used to parse the data uploaded to the cloud according to the parsing method corresponding to the data packet type of the data uploaded to the cloud to obtain parsed data and store the parsed data; wherein, the parsed data includes parsed signals and signal values corresponding to the parsed signals.

[0158] The third processing module 1003 is used to statistically analyze the stored parsed data according to the service requirements of the test scenario to obtain a test result; wherein, the service requirements include at least part of the following items: statistically analyzing the first parsed signal, statistically analyzing the historical signal values of the second parsed signal, and statistically analyzing the jump situation of the signal values of the third parsed signal.

[0159] In some alternative implementation manners, the first processing module 1001 is further used to:

[0160] If the network address type of the test scenario is an intranet, call the intranet login interface and the first query interface to obtain the original data, and call the gateway interface according to the request identifier of the original data to obtain the data uploaded to the cloud;

[0161] If the network address type of the test scenario is a public network, call the second query interface on the cloud to obtain the data uploaded to the cloud.

[0162] In some alternative embodiments, when the data packet type of the data uploaded to the cloud is the identification type, the second processing module 1002 is further configured to:

[0163] Parse the data uploaded to the cloud into a first timestamp, a first data frame identifier, and a first data frame;

[0164] Obtain a parsed signal and a signal value corresponding to the parsed signal according to a pre-stored protocol matrix, the first data frame identifier, and the first data frame.

[0165] In some alternative embodiments, when the data packet type of the data uploaded to the cloud is the signal type, the second processing module 1002 is further configured to:

[0166] Parse the data uploaded to the cloud into a second timestamp and a second data frame; wherein, the second data frame includes a second data frame identifier, the number of signals, and signal content, and the signal content includes at least some of the following items: timestamp length, signal start bit, relative timestamp, and signal value;

[0167] Obtain a parsed signal and a signal value corresponding to the parsed signal according to a pre-stored protocol matrix and the second data frame.

[0168] In some alternative embodiments, when the service requirement of the test scenario includes counting the jump situation of the signal value of the third parsed signal, the third processing module 1003 is further configured to:

[0169] Successively collect the third parsed signal from the stored parsed data. If the current signal value of the third parsed signal collected this time is inconsistent with the historical signal value of the third parsed signal collected last time, determine that the jump situation of the third parsed signal has occurred; wherein, if the third parsed signal is collected for the first time, determine that the jump situation of the third parsed signal has not occurred;

[0170] Obtain a test result according to the jump situation of the third parsed signal and the current signal value.

[0171] In some alternative embodiments, the device is further configured to:

[0172] Obtain the generation requirement of the data to be generated; the generation requirement includes at least some of the following items: signal name, data packet type, signal value, data content change type, and data cycle change type;

[0173] Generate target data based on a pre-stored protocol matrix and the generation requirement of the data to be generated.

[0174] In some alternative embodiments, the device is further configured to:

[0175] Obtain the target original data of the source device and the target data uploaded to the cloud after the source device uploads the target original data to the cloud;

[0176] Perform message conversion on the target original data to obtain a first data list, and perform parsing on the target data to be uploaded to the cloud to obtain a second data list;

[0177] Compare the first data list and the second data list to obtain the consistency test result.

[0178] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding above-mentioned embodiments, and will not be elaborated here.

[0179] The data upload to the cloud testing device in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0180] An embodiment of the present invention further provides a computer device having the above-mentioned Figure 10 data upload to the cloud testing device shown.

[0181] Please refer to Figure 11 , Figure 11 which is a schematic structural diagram of a computer device provided by an optional embodiment of the present invention. As shown in Figure 11 , the computer device includes: one or more processors 10, a memory 20, and an interface for connecting each component, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a set of blade servers, or a multi-processor system). Figure 11 Take one processor 10 as an example in

[0182] The processor 10 can be a central processor, a network processor, or a combination thereof. Among them, the processor 10 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit, a programmable logic device, or a combination thereof. The above-mentioned programmable logic device can be a complex programmable logic device, a field programmable gate array, a general array logic, or any combination thereof.

[0183] Among them, the memory 20 stores instructions that can be executed by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiments.

[0184] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area can store an operating system and application programs required for at least one function; the data storage area can store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided with respect to the processor 10, and these remote memories can be connected to the computer device through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0185] The memory 20 may include a volatile memory, for example, a random access memory; the memory may also include a non-volatile memory, for example, a flash memory, a hard disk, or a solid-state drive; the memory 20 may also include a combination of the above types of memories.

[0186] The computer device further includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30, and the output device 40 may be connected through a bus or other means. Figure 11 Taking the connection through the bus as an example.

[0187] The input device 30 can receive input digital or character information, and generate key signal inputs related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (for example, an LED), and a haptic feedback device (for example, a vibration motor), etc. The above display device includes but is not limited to a liquid crystal display, a light-emitting diode, a display, and a plasma display. In some alternative embodiments, the display device may be a touch screen.

[0188] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored as such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code, and when the software or computer code is accessed and executed by the computer, the processor, or the hardware, the method shown in the above embodiments is implemented.

[0189] A part of the present invention can be applied as a computer program product, for example, computer program instructions, which when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should be able to understand that the forms of existence of computer program instructions in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0190] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for testing data for cloud access, characterized in that, The method includes: Obtaining the data uploaded to the cloud according to an obtaining method corresponding to the network address type of the test scenario; Parsing the data uploaded to the cloud according to a parsing method corresponding to the data packet type of the data uploaded to the cloud to obtain parsed data, and storing the parsed data; wherein, the parsed data includes a parsed signal and a signal value corresponding to the parsed signal; Statistically analyzing the stored parsed data according to the service requirements of the test scenario to obtain a test result; wherein, the service requirements include at least some of the following items: statistically analyzing a first parsed signal, statistically analyzing the historical signal values of a second parsed signal, and statistically analyzing the jump situation of the signal value of a third parsed signal.

2. The method according to claim 1, wherein When the service requirements of the test scenario include statistically analyzing the jump situation of the signal value of the third parsed signal, the statistically analyzing the stored parsed data according to the service requirements of the test scenario to obtain a test result includes: Successively collecting the third parsed signal from the stored parsed data. If the current signal value of the third parsed signal collected this time is inconsistent with the historical signal value of the third parsed signal collected last time, determining that the jump situation of the third parsed signal has occurred; wherein, if the third parsed signal is collected for the first time, determining that the jump situation of the third parsed signal has not occurred; Obtaining a test result according to the jump situation and the current signal value of the third parsed signal.

3. The method according to claim 2, wherein When the data packet type of the data uploaded to the cloud is an identification type, the parsing the data uploaded to the cloud according to a parsing method corresponding to the data packet type of the data uploaded to the cloud to obtain parsed data includes: Parsing the data uploaded to the cloud into a first timestamp, a first data frame identifier, and a first data frame; Obtaining a parsed signal and a signal value corresponding to the parsed signal according to a pre-stored protocol matrix, the first data frame identifier, and the first data frame.

4. The method according to claim 2, wherein When the data packet type of the data uploaded to the cloud is a signal type, the parsing the data uploaded to the cloud according to a parsing method corresponding to the data packet type of the data uploaded to the cloud to obtain parsed data includes: Parsing the data uploaded to the cloud into a second timestamp and a second data frame; wherein, the second data frame includes a second data frame identifier, the number of signals, and signal content, and the signal content includes at least some of the following items: timestamp length, signal start bit, relative timestamp, and signal value; Obtaining a parsed signal and a signal value corresponding to the parsed signal according to a pre-stored protocol matrix and the second data frame.

5. The method according to claim 3 or 4, characterized in that The method further includes: Obtaining the generation requirements of the data to be generated; the generation requirements include at least some of the following items: signal name, data packet type, signal value, data content change type, and data cycle change type; Generating target data based on a pre-stored protocol matrix and the generation requirements of the data to be generated.

6. The method according to any one of claims 1-4,characterized in that, The obtaining the data uploaded to the cloud according to an obtaining method corresponding to the network address type of the test scenario includes: If the network address type of the test scenario is an intranet, calling an intranet login interface and a first query interface to obtain original data, and calling a gateway interface according to the request identifier of the original data to obtain the data uploaded to the cloud; If the network address type of the test scenario is public network, call the second query interface of the cloud to obtain the data uploaded to the cloud.

7. The method according to claim 6, characterized in that, The method further includes: Obtain the target original data of the source device and the target data uploaded to the cloud after the source device uploads the target original data to the cloud; Perform message conversion on the target original data to obtain a first data list, and parse the target data uploaded to the cloud to obtain a second data list; Compare the first data list and the second data list to obtain a consistency test result.

8. A device for testing data uploaded to the cloud, characterized in that, The device includes: A first processing module, configured to obtain the data uploaded to the cloud according to an acquisition method corresponding to the network address type of the test scenario; A second processing module, configured to parse the data uploaded to the cloud according to a parsing method corresponding to the data packet type of the data uploaded to the cloud to obtain parsed data, and store the parsed data; wherein, the parsed data includes a parsed signal and a signal value corresponding to the parsed signal; A third processing module, configured to perform statistics on the stored parsed data according to the service requirements of the test scenario to obtain a test result; wherein, the service requirements include at least some of the following items: statistics of the first parsed signal, historical signal values of the second parsed signal, and jump conditions of the signal values of the third parsed signal.

9. A computer device, characterized in that, Includes: A memory and a processor, the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to execute the method for testing data uploaded to the cloud according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored on the computer-readable storage medium, and the computer instructions are used to cause a computer to execute the method for testing data uploaded to the cloud according to any one of claims 1 to 7.

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