CN113656324A

CN113656324A - Full link testing method, device, equipment and medium for disease entry and decision

Info

Publication number: CN113656324A
Application number: CN202111012757.9A
Authority: CN
Inventors: 周珍珠; 彭晶
Original assignee: Ping An Life Insurance Company of China Ltd
Current assignee: Ping An Life Insurance Company of China Ltd
Priority date: 2021-08-31
Filing date: 2021-08-31
Publication date: 2021-11-16
Anticipated expiration: 2041-08-31
Also published as: CN113656324B

Abstract

The invention relates to the field of artificial intelligence and testing, and provides a full-link testing method, a device, equipment and a medium for disease entry and decision, which can realize the configurability of a testing environment by creating a configuration file, the unified management of case pictures is realized through a dictionary, so that the disease test is more generalized, the information of the uniform resource locator is directly positioned to the corresponding interface to execute AI auxiliary entry, the normal execution of the test is avoided being influenced by overlarge byte stream objects, the problem that the return time consumption is greatly different from the actual link use time consumption is also avoided, thereby improving the test effect, realizing the full link test of AI auxiliary input and intelligent decision by building a common interface test frame, data sharing is carried out between the AI auxiliary entry test and the intelligent decision test, so that data multiplexing is realized, and the test efficiency is improved. In addition, the invention also relates to a block chain technology, and the mapping table can be stored in the block chain node.

Description

Full link testing method, device, equipment and medium for disease entry and decision

Technical Field

The invention relates to the technical field of artificial intelligence and testing, in particular to a full link testing method, device, equipment and medium for disease input and decision.

Background

In the nuclear protection claim settlement project, deep fusion is carried out on the basis of AI (Artificial Intelligence) technologies such as OCR (Optical Character Recognition), NLP (Natural Language Processing) and knowledge graph and the like and big data, an intelligent solution for nuclear protection claim settlement is created, functions such as intelligent collection, intelligent entry, intelligent audit and intelligent wind control are supported, and a new generation nuclear protection claim system with better customer experience, more accurate risk Recognition and higher operation efficiency is created.

The most core functions are AI auxiliary entry and intelligent decision, so in order to ensure the effectiveness of the AI auxiliary entry and the intelligent decision, a full link test needs to be performed on the AI auxiliary entry and the intelligent decision.

However, in the existing test scheme, a Postman or meter interface test tool is mainly used for entry operation, when the number of pictures is large, the produced byte stream is also large, and if the number of the pictures exceeds the maximum value of the limitation of the Postman request content, the test cannot be continuously executed. And the AI auxiliary entry and the intelligent decision are respectively executed for testing, and data sharing is not realized in the testing process, so that the mapping relation between the byte stream and the original image cannot be automatically recorded, manual maintenance is needed, and the byte stream object needs to be regenerated when diseases of different medical records are tested each time. In addition, in the testing process, with the increase of the number of diseases, the manual testing working tool for carrying out the full link on each disease is large, the testing efficiency is low, and meanwhile, the difference between the time consumed by the Postman test return and the time consumed by the actual link is large, so that the testing efficiency is further influenced.

Disclosure of Invention

The embodiment of the invention provides a full link test method, device, equipment and medium for disease input and decision, and aims to solve the problems of poor test effect and low efficiency of full link test for disease AI auxiliary input and intelligent decision.

In a first aspect, an embodiment of the present invention provides a full link testing method for disease entry and decision, including:

responding to a test request, and determining a test environment and a disease to be tested according to the test request;

acquiring a pre-created configuration file, and generating target uniform resource locator information according to the test environment and the configuration file;

acquiring a pre-configured dictionary, and generating a target address according to the disease to be tested and the dictionary;

connecting to the target address, and acquiring a to-be-processed picture set from the target address according to the test request;

converting each picture to be processed in the picture set to be processed into a byte stream object;

determining an AI auxiliary input interface and an intelligent decision interface according to the target uniform resource locator information;

transmitting the byte stream object of each picture to be processed to the AI auxiliary entry interface, receiving data returned by the AI auxiliary entry interface as a target AI auxiliary entry result of each picture to be processed, and recording a target identifier of the picture to be processed corresponding to the target AI auxiliary entry result of each picture to be processed;

transmitting the target AI auxiliary input result and the target identifier of each picture to be processed to the intelligent decision interface, and receiving data returned by the intelligent decision interface as the target intelligent decision result of each picture to be processed;

and calling a pre-configured mapping table, verifying the target AI auxiliary entry result and the target intelligent decision result according to the mapping table, and generating a test result.

According to a preferred embodiment of the present invention, the generating target uniform resource locator information according to the test environment and the configuration file includes:

reading a target field from the configuration file by using a read-write configuration class as a target IP and a target port corresponding to the test environment;

extracting general interface information from the configuration file;

and splicing the general interface information, the target IP and the target port to obtain the target uniform resource locator information.

According to a preferred embodiment of the present invention, before the obtaining the pre-configured dictionary, the method further includes:

acquiring case pictures from a configuration database at preset time intervals, and acquiring disease names corresponding to the case pictures;

dividing the case pictures with the same disease name into a group to obtain at least one case picture group;

saving the at least one case picture group to a designated folder, and naming each case picture group according to a disease name corresponding to each case picture group to obtain at least one subfolder;

configuring the coding of case pictures contained in each subfolder;

and acquiring a directory corresponding to each case picture, and generating the dictionary according to the directory.

According to a preferred embodiment of the invention, the method further comprises:

acquiring the response time of the AI auxiliary input interface to the byte stream object of each picture to be processed from the execution log as the first response time of each picture to be processed, and acquiring the response time of the intelligent decision interface to the target AI auxiliary input result and the target identifier of each picture to be processed as the second response time of each picture to be processed;

when the first response time of the picture to be processed is detected to be larger than or equal to a first configuration time threshold value and/or the second response time of the picture to be processed is detected to be larger than or equal to a second configuration time threshold value, determining the detected picture to be processed as a target picture;

and skipping the verification of the target AI auxiliary input result and the target intelligent decision result of the target picture, and generating early warning information according to the target picture.

According to a preferred embodiment of the present invention, before the retrieving the pre-configured mapping table, the method further includes:

acquiring historical test data according to a configured time period;

acquiring a mapping relation between an AI auxiliary entry result and an intelligent decision result from the historical test data;

establishing the mapping table according to the mapping relation between the obtained AI auxiliary input result and the intelligent decision result;

when a test result of a newly added disease is detected, acquiring a mapping relation between an AI auxiliary entry result corresponding to the newly added disease and an intelligent decision result from the test result of the newly added disease;

and maintaining the mapping relation between the AI auxiliary entry result corresponding to the newly added disease and the intelligent decision result to the mapping table.

According to a preferred embodiment of the present invention, after the generating the test result, the method further comprises:

acquiring an execution log in a test process;

acquiring the total response time of the AI auxiliary entry interface in the data transmission process and acquiring the total response time of the intelligent decision interface in the data transmission process from the execution log;

calculating the sum of the total response time of the AI auxiliary entry interface in the data transmission process and the total response time of the intelligent decision interface in the data transmission process as the total interface time consumption;

recording a byte stream object of each picture to be processed and a target identifier of each picture to be processed;

generating a visual target file according to each picture to be processed, the byte stream object of each picture to be processed, the target identifier of each picture to be processed, the total interface time consumption and the test result;

and calling a configuration library to send the target file to a specified mailbox.

when the test result shows that the target AI auxiliary input result and the corresponding target intelligent decision result do not pass the verification, acquiring a to-be-processed picture which does not pass the verification;

and deleting the to-be-processed picture which is not verified from the dictionary.

In a second aspect, an embodiment of the present invention provides a full link testing apparatus for disease entry and decision, including:

the device comprises a determining unit, a judging unit and a judging unit, wherein the determining unit is used for responding to a test request and determining a test environment and a disease to be tested according to the test request;

the generating unit is used for acquiring a pre-created configuration file and generating target uniform resource locator information according to the test environment and the configuration file;

the generating unit is further used for acquiring a pre-configured dictionary and generating a target address according to the disease to be tested and the dictionary;

the acquisition unit is used for connecting to the target address and acquiring a picture set to be processed from the target address according to the test request;

the conversion unit is used for converting each picture to be processed in the picture set to be processed into a byte stream object;

the determining unit is further configured to determine an AI auxiliary entry interface and an intelligent decision interface according to the target uniform resource locator information;

the transmission unit is used for transmitting the byte stream object of each picture to be processed to the AI auxiliary input interface, receiving data returned by the AI auxiliary input interface as a target AI auxiliary input result of each picture to be processed, and recording a target identifier of the picture to be processed corresponding to the target AI auxiliary input result of each picture to be processed;

the transmission unit is further configured to transmit the target AI auxiliary entry result and the target identifier of each to-be-processed picture to the intelligent decision interface, and receive data returned by the intelligent decision interface as a target intelligent decision result of each to-be-processed picture;

and the checking unit is used for calling a pre-configured mapping table, checking the target AI auxiliary entry result and the target intelligent decision result according to the mapping table and generating a test result.

In a third aspect, an embodiment of the present invention further provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the processor implements the full link testing method for disease entry and decision making according to the first aspect.

In a fourth aspect, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and the computer program, when executed by a processor, causes the processor to execute the full link testing method for disease entry and decision making according to the first aspect.

The embodiment of the invention provides a full link testing method, a device, equipment and a medium for disease entry and decision, which can respond to a testing request, determine a testing environment and a disease to be tested according to the testing request, acquire a pre-created configuration file, generate target uniform resource locator information according to the testing environment and the configuration file, realize the configurability of the testing environment by creating the configuration file, open different testing environments, enable different testing environments to share a uniform testing frame, acquire a pre-configured dictionary, generate a target address according to the disease to be tested and the dictionary, realize the uniform management of case pictures through the pre-constructed dictionary, enable the disease testing to be more universal, support the testing of various diseases by maintaining the dictionary, reduce the testing time, the efficiency of testing and production verification is further accelerated, the testing and production verification is connected to the target address, a to-be-processed picture set is obtained from the target address according to the testing request, each to-be-processed picture in the to-be-processed picture set is converted into a byte stream object, an AI auxiliary entry interface and an intelligent decision interface are determined according to the target uniform resource locator information, the byte stream object of each to-be-processed picture is transmitted to the AI auxiliary entry interface, data returned by the AI auxiliary entry interface is received as a target AI auxiliary entry result of each to-be-processed picture, a target identifier of the to-be-processed picture corresponding to the target AI auxiliary entry result of each to-be-processed picture is recorded, the generated uniform resource locator information is directly positioned to the corresponding interface, and AI auxiliary entry is executed by using the corresponding interface, so that the problem that the normal execution of the testing is influenced by the overlarge byte stream object can be effectively avoided, meanwhile, the problem that the time consumption is greatly different from the time consumption of actual link use when Postman test is used is avoided, the test effect is further improved, the target AI auxiliary input result and the target identifier of each picture to be processed are transmitted to the intelligent decision interface, the data returned by the intelligent decision interface are received and serve as the target intelligent decision result of each picture to be processed, a pre-configured mapping table is called, the target AI auxiliary input result and the target intelligent decision result are verified according to the mapping table, the test result is generated, a common interface test frame is built, the full link test of AI auxiliary input and intelligent decision is realized, data sharing is performed between the AI auxiliary input test and the intelligent decision test, data multiplexing is realized, and the test efficiency is further improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.

Fig. 1 is a schematic flow chart of a full link testing method for disease entry and decision according to an embodiment of the present invention;

FIG. 2 is a schematic block diagram of a full link testing apparatus for disease entry and decision making provided by an embodiment of the present invention;

FIG. 3 is a schematic block diagram of a computer device provided by an embodiment of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

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

Fig. 1 is a schematic flow chart of a full link testing method for disease entry and decision according to an embodiment of the present invention.

And S10, responding to the test request, and determining the test environment and the disease to be tested according to the test request.

In this embodiment, the test request may be triggered by a relevant worker, such as a tester, a developer, and the like.

In this embodiment, the test environment may include, but is not limited to: the environment corresponding to the test task, the environment corresponding to the production task, and the like.

In this embodiment, the disease to be tested can include, but is not limited to, thyroiditis, appendicitis, and the like.

S11, obtaining the pre-established configuration file, and generating the target uniform resource locator information according to the test environment and the configuration file.

In this embodiment, the configuration file is configured according to different test environments and interfaces.

In this embodiment, by creating the configuration file, the test environment can be configured, different test environments are opened, and a uniform test framework can be shared among different test environments.

In at least one embodiment of the present invention, the generating target uniform resource locator information according to the test environment and the configuration file includes:

extracting general interface information from the configuration file;

The read-write configuration class may be a ConfigParser class.

For example: assuming that the configuration file is cfg _ trace.ini, the environment information of the test environment and the interface information required by the test are configured in the file, and the configuration file is divided into three parts: a common part for storing common interface information, such as URI (Uniform Resource Identifier) of various core protection claim project interfaces (e.g.,/vas/ai _ platform/media _ report _ else _ uws); a test part for configuring an IP and a port of a test environment; prd section for configuring the IP and ports of the production environment. The fields required for the read-write configuration class parsing are used from the configuration file, and then the fields are spliced into the target uniform resource locator information, for example, the media _ RECG _ ELES _ UWS _ URL is http://192.168.16.123/vas/ai _ platform/media _ report _ RECG _ elis _ UWS is a corresponding target uniform resource locator information generated according to the test request.

S12, acquiring a pre-configured dictionary, and generating a target address according to the disease to be tested and the dictionary.

In at least one embodiment of the present invention, before the obtaining the pre-configured dictionary, the method further includes:

configuring the coding of case pictures contained in each subfolder;

The preset time interval may be configured by user, for example: every 30 days.

The configuration database may be a database of a designated hospital, and the like, which is not limited in the present invention.

Through the embodiment, unified management of the case pictures can be realized through the pre-constructed dictionary, so that the disease test is more generalized, and through maintenance of the dictionary, tests on various diseases can be supported, the test time is reduced, and the test and production verification efficiency is accelerated.

Through verification, the disease test is generalized, the test time can be effectively reduced, the test time is reduced to 25 seconds from the original 13 minutes for testing the full link of one disease, and the test and production verification efficiency is improved.

In at least one embodiment of the present invention, the generating a target address according to the disease to be tested and the dictionary comprises:

acquiring a disease name corresponding to the disease to be tested;

and inquiring in the dictionary according to the disease name corresponding to the disease to be tested to obtain the target address.

For example: the picture of 1, 2, 3, 4, 10 cases under the upper respiratory tract infection subfolder can be obtained through the target address { 'basePath': D: \ \ Users \ \ Documents \ \ nuclear protection claims \ \ image-test \ \ upper respiratory tract infection ',' dirInfo ': 1', '2', '3', '4', '10' ]; the 1 st, 2 rd, 3 rd, 4 th, 5 th, 6 th, 7 th, 8 th, 9 th, 10 th, 11 th, 12 th case picture under the thyrotropin subfolder can be acquired through the target address { 'basePath': D: \ \ Users \ \ Documents \ \ nuclear protection claims \ \ image-test \ \ thyrotropin ',' dirInfo ': 1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12'.

And S13, connecting to the target address, and acquiring the picture set to be processed from the target address according to the test request.

In this embodiment, the to-be-processed picture may be a medical image, and the type of the object included in the to-be-processed picture is a focus, that is, a portion of the body where a lesion occurs. Medical images refer to images of internal tissues, e.g., stomach, abdomen, heart, knee, brain, which are obtained in a non-invasive manner for medical treatment or medical research, such as images generated by medical instruments, e.g., CT (Computed Tomography), MRI (Magnetic Resonance Imaging), US (ultrasound), X-ray images, electroencephalograms, and photo lamps.

In at least one embodiment of the present invention, the obtaining the to-be-processed picture set from the target address according to the test request includes:

determining the number of case pictures to be acquired as a target number according to the test request;

and acquiring the case pictures of the target number from the target address as the pictures to be processed.

For example: when three case pictures need to be tested, the first three pictures or any three pictures can be acquired from the target address as the pictures to be processed.

And S14, converting each picture to be processed in the picture set to be processed into a byte stream object.

In at least one embodiment of the present invention, the converting each picture to be processed in the set of pictures to be processed into a byte stream object includes:

starting a standard base64 tool of python, and opening each picture to be processed in a binary mode;

and reading the file content of each picture to be processed, and converting the file content of each picture to be processed into base64 codes to obtain a byte stream object converted from each picture to be processed.

And S15, determining an AI auxiliary input interface and an intelligent decision interface according to the target uniform resource locator information.

In this embodiment, interface information may be obtained through the target uniform resource locator information, that is, the AI auxiliary entry interface and the intelligent decision interface may be further determined according to the interface information.

In this embodiment, the AI-assisted logging interface is capable of performing an AI-assisted logging task, i.e. logging risk information of a case.

In this embodiment, the intelligent decision interface is configured to perform an intelligent decision task, that is, predict a disease degree through risk information of an entered case, and further determine a risk degree of insurance to determine whether an insurance application requirement of an insurance to be purchased is met.

And S16, transmitting the byte stream object of each picture to be processed to the AI auxiliary entry interface, receiving data returned by the AI auxiliary entry interface as a target AI auxiliary entry result of each picture to be processed, and recording a target identifier of the picture to be processed corresponding to the target AI auxiliary entry result of each picture to be processed.

It should be noted that, in a traditional AI assisted entry testing scenario, a Postman or Jmeter interface testing tool is usually used to fill a request body for AI assisted entry operation, and when the data volume of a case picture is large, and the generated byte stream is also large, if the maximum value of the limitation of the Postman or Jmeter request content is exceeded, the test cannot be executed.

In this embodiment, the generated uniform resource locator information is directly located to the corresponding interface, and the corresponding interface is used to perform AI auxiliary entry, so that the problem that the normal execution of the test is affected due to the too large byte stream object can be effectively avoided, and meanwhile, the problem that the consumed time and the actual link are used by returning time greatly when the Postman test is used is also avoided, and the test effect is further improved.

And S17, transmitting the target AI auxiliary entry result and the target identifier of each picture to be processed to the intelligent decision interface, and receiving data returned by the intelligent decision interface as the target intelligent decision result of each picture to be processed.

In the conventional test scheme, an AI auxiliary entry test and an intelligent decision test are executed separately, and because the data of the AI auxiliary entry test and the intelligent decision test are not universal, the test consumes longer time and has lower efficiency.

According to the embodiment, a common interface test frame is set up, full link tests of AI auxiliary input and intelligent decision are realized, data sharing is performed between the AI auxiliary input test and the intelligent decision test, data multiplexing is realized, and the test efficiency is further improved.

In at least one embodiment of the invention, the method further comprises:

The first configuration time threshold is an upper limit value of the response time of the AI auxiliary entry interface, and if the first configuration time threshold is exceeded, it is indicated that the response of the AI auxiliary entry interface is abnormal.

The second configuration time threshold is an upper limit value of the response time of the intelligent decision interface, and if the second configuration time threshold is exceeded, it indicates that the response of the intelligent decision interface is abnormal.

Through the embodiment, the interface can be detected in real time in the test process, time is consumed, the abnormal influence is timely processed when the abnormal influence is found, the test time is prevented from being influenced, and the test efficiency is improved.

And S18, calling a pre-configured mapping table, verifying the target AI auxiliary entry result and the target intelligent decision result according to the mapping table, and generating a test result.

For example: the target AI auxiliary entry result is hyperglycemia, and the target intelligent decision is that the diabetes risk exists, so that the method is suitable for the disclaimer of the serious illness risk. And if the data in the mapping table is the same as the data in the mapping table, determining that the test result is an AI auxiliary entry result and an intelligent decision result.

In at least one embodiment of the present invention, before the invoking of the preconfigured mapping table, the method further comprises:

acquiring historical test data according to a configured time period;

Through the implementation mode, the established mapping table is maintained periodically, and the mapping table is updated in time when new diseases exist, so as to assist in carrying out complete full link test.

Of course, in other embodiments, when the newly added disease does not respond to the test data, a prompt message may be sent to the terminal device of the designated contact person, so as to prompt the designated contact person to upload the mapping relationship between the AI auxiliary entry result and the intelligent decision result corresponding to the newly added disease.

In at least one embodiment of the present invention, the verifying the target AI auxiliary entry result and the target intelligent decision result according to the mapping table includes:

inquiring the mapping relation between the target AI auxiliary entry result and the target intelligent decision result from the mapping table;

when the mapping relation between the target AI auxiliary entry result and the target intelligent decision result is inquired in the mapping table, determining that the target AI auxiliary entry result and the target intelligent decision result pass verification; or

And when the mapping relation between the target AI auxiliary entry result and the target intelligent decision result is not inquired in the mapping table, determining that the target AI auxiliary entry result and the target intelligent decision result are not verified.

By the implementation mode, automatic full-link testing for AI auxiliary input and intelligent decision can be realized.

Through verification, each link in the scheme shortens the test time, so that the test of 3 days originally can be shortened to be completed within 2 hours as a whole.

In at least one embodiment of the invention, after the generating the test result, the method further comprises:

acquiring an execution log in a test process;

The target file may be in an excel format.

For example: and saving each picture to be processed, the byte stream object of each picture to be processed, the target identifier of each picture to be processed, the total interface time consumption and the test result in excel by using an xlwt expansion tool of python to obtain the target file.

The configuration library may be a third party library, such as an email.

The designated mailbox can comprise a mailbox of a tester, a mailbox of a developer and the like, and can be configured in a user-defined mode according to different test scenes.

In the embodiment, by acquiring a series of log information in the test process, the problem is conveniently checked and positioned; the recorded total interface time consumption can effectively reflect the performance of the interface in the test process; the recorded byte stream object of each picture to be processed and the target identifier of each picture to be processed can realize the multiplexing of the generated byte stream object, and avoid the regeneration in each test to cause time loss; meanwhile, data such as test results are stored in a visual file format and sent to an execution mailbox, so that the user can check the data conveniently.

Through the embodiment, the picture with problems can be prevented from being reused, and the problem of occurrence on a reproduction line in the production process is effectively avoided.

It should be noted that, in order to further improve the security of the data and avoid malicious tampering of the data, the mapping table may be stored in the blockchain node.

It can be seen from the above technical solutions that, in response to a test request, the present invention determines a test environment and a disease to be tested according to the test request, obtains a pre-created configuration file, generates target url information according to the test environment and the configuration file, implements configurability of the test environment by creating the configuration file, opens up different test environments, enables different test environments to share a unified test frame, obtains a pre-configured dictionary, generates a target address according to the disease to be tested and the dictionary, implements unified management of case pictures by the pre-constructed dictionary, makes disease testing more universal, supports testing of various diseases by maintaining the dictionary, reduces testing time, further accelerates testing and production verification efficiency, connects to the target address, acquiring a picture set to be processed from the target address according to the test request, converting each picture to be processed in the picture set to be processed into a byte stream object, determining an AI auxiliary entry interface and an intelligent decision interface according to the target uniform resource locator information, transmitting the byte stream object of each picture to be processed to the AI auxiliary entry interface, receiving data returned by the AI auxiliary entry interface as a target AI auxiliary entry result of each picture to be processed, recording a target identifier of the picture to be processed corresponding to the target AI auxiliary entry result of each picture to be processed, directly locating the generated uniform resource locator information to the corresponding interface, executing AI auxiliary entry by using the corresponding interface, and effectively avoiding the problem that the normal execution of the test is influenced by the overlarge byte stream object, the problem that the time consumption is greatly different from the time consumption of actual link use when Postman testing is used is solved, the testing effect is further improved, the target AI auxiliary input result and the target identifier of each picture to be processed are transmitted to the intelligent decision interface, the data returned by the intelligent decision interface are received and serve as the target intelligent decision result of each picture to be processed, a pre-configured mapping table is called, the target AI auxiliary input result and the target intelligent decision result are verified according to the mapping table, the testing result is generated, the full-link testing of AI auxiliary input and intelligent decision is realized by building a common interface testing frame, data sharing is performed between the AI auxiliary input test and the intelligent decision test, data multiplexing is realized, and the testing efficiency is further improved.

The embodiment of the invention also provides a disease entry and decision full link testing device, which is used for executing any embodiment of the disease entry and decision full link testing method. Specifically, referring to fig. 2, fig. 2 is a schematic block diagram of a full link testing apparatus for disease entry and decision provided by an embodiment of the present invention.

As shown in fig. 2, the full link testing apparatus 100 for disease entry and decision includes: the device comprises a determining unit 101, a generating unit 102, an acquiring unit 103, a converting unit 104, a transmitting unit 106 and a checking unit 107.

In response to the test request, the determining unit 101 determines the test environment and the disease to be tested according to the test request.

The generating unit 102 obtains a pre-created configuration file, and generates target uniform resource locator information according to the test environment and the configuration file.

In at least one embodiment of the present invention, the generating unit 102 generates the target uniform resource locator information according to the test environment and the configuration file includes:

extracting general interface information from the configuration file;

The read-write configuration class may be a ConfigParser class.

The generating unit 102 obtains a pre-configured dictionary, and generates a target address according to the disease to be tested and the dictionary.

In at least one embodiment of the present invention, before the pre-configured dictionary is obtained, a case picture is obtained from a configuration database at preset time intervals, and a disease name corresponding to the case picture is obtained;

configuring the coding of case pictures contained in each subfolder;

The preset time interval may be configured by user, for example: every 30 days.

In at least one embodiment of the present invention, the generating unit 102 generates the target address according to the disease to be tested and the dictionary includes:

acquiring a disease name corresponding to the disease to be tested;

The obtaining unit 103 is connected to the target address, and obtains a to-be-processed picture set from the target address according to the test request.

In at least one embodiment of the present invention, the obtaining unit 103 obtains the to-be-processed picture set from the target address according to the test request includes:

The conversion unit 104 converts each picture to be processed in the set of pictures to be processed into a byte stream object.

In at least one embodiment of the present invention, the converting unit 104 converts each picture to be processed in the set of pictures to be processed into a byte stream object, including:

The determining unit 101 determines an AI auxiliary entry interface and an intelligent decision interface according to the target uniform resource locator information.

The transmission unit 106 transmits the byte stream object of each picture to be processed to the AI auxiliary entry interface, receives data returned by the AI auxiliary entry interface as a target AI auxiliary entry result of each picture to be processed, and records a target identifier of the picture to be processed corresponding to the target AI auxiliary entry result of each picture to be processed.

The transmission unit 106 transmits the target AI auxiliary entry result and the target identifier of each to-be-processed picture to the intelligent decision interface, and receives data returned by the intelligent decision interface as the target intelligent decision result of each to-be-processed picture.

In at least one embodiment of the present invention, a response time of the AI auxiliary entry interface to a byte stream object of each to-be-processed picture is obtained from an execution log as a first response time of each to-be-processed picture, and a response time of the intelligent decision interface to a target AI auxiliary entry result and a target identifier of each to-be-processed picture is obtained as a second response time of each to-be-processed picture;

The checking unit 107 retrieves a pre-configured mapping table, and checks the target AI auxiliary entry result and the target intelligent decision result according to the mapping table to generate a test result.

In at least one embodiment of the present invention, before the pre-configured mapping table is called, historical test data is obtained according to a configured time period;

In at least one embodiment of the present invention, the verifying unit 107, according to the mapping table, verifies the target AI auxiliary entry result and the target intelligent decision result, including:

In at least one embodiment of the present invention, after the test result is generated, an execution log in the test process is obtained;

The target file may be in an excel format.

The configuration library may be a third party library, such as an email.

In at least one embodiment of the present invention, after the test result is generated, when the test result shows that the target AI auxiliary entry result and the corresponding target intelligent decision result do not pass the verification, a to-be-processed picture that does not pass the verification is obtained;

It can be seen from the above technical solutions that, in response to a test request, the present invention determines a test environment and a disease to be tested according to the test request, obtains a pre-created configuration file, generates target url information according to the test environment and the configuration file, implements configurability of the test environment by creating the configuration file, opens up different test environments, enables different test environments to share a unified test frame, obtains a pre-configured dictionary, generates a target address according to the disease to be tested and the dictionary, implements unified management of case pictures by the pre-constructed dictionary, makes disease testing more universal, supports testing of various diseases by maintaining the dictionary, reduces testing time, further accelerates testing and production verification efficiency, connects to the target address, acquiring a picture set to be processed from the target address according to the test request, converting each picture to be processed in the picture set to be processed into a byte stream object, determining an auxiliary entry interface and an intelligent decision interface according to the target uniform resource locator information, transmitting the byte stream object of each picture to be processed to the AI auxiliary entry interface, receiving data returned by the AI auxiliary entry interface as a target AI auxiliary entry result of each picture to be processed, recording a target identifier of the picture to be processed corresponding to the target AI auxiliary entry result of each picture to be processed, directly locating the generated uniform resource locator information to the corresponding interface, and executing AI auxiliary entry by using the corresponding interface, thereby effectively avoiding the problem that the normal execution of the test is influenced by the overlarge byte stream object, and simultaneously avoiding the problem that the time consumption is greatly different from the time consumption of the actual link use when using Postman test, and then the test effect is improved, the target AI auxiliary input result and the target identifier of each picture to be processed are transmitted to the intelligent decision interface, the data returned by the intelligent decision interface are received as the target intelligent decision result of each picture to be processed, a pre-configured mapping table is called, the target AI auxiliary input result and the target intelligent decision result are verified according to the mapping table, the test result is generated, the full-link test of AI auxiliary input and intelligent decision is realized by building a common interface test frame, the data sharing is performed between the AI auxiliary input test and the intelligent decision test, the data multiplexing is realized, and the test efficiency is further improved.

The above-described full link testing apparatus for disease entry and decision making may be implemented in the form of a computer program which may be run on a computer device as shown in fig. 3.

Referring to fig. 3, fig. 3 is a schematic block diagram of a computer device according to an embodiment of the present invention. The computer device 500 is a server, and the server may be an independent server or a server cluster composed of a plurality of servers. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.

Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

Referring to fig. 3, the computer device 500 includes a processor 502, memory, and a network interface 505 connected by a system bus 501, where the memory may include a storage medium 503 and an internal memory 504.

The storage medium 503 may store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, causes the processor 502 to perform a full link testing method of disease entry and decision making.

The processor 502 is used to provide computing and control capabilities that support the operation of the overall computer device 500.

The internal memory 504 provides an environment for the execution of the computer program 5032 on the storage medium 503, and when the computer program 5032 is executed by the processor 502, the processor 502 can execute a full link testing method for disease entry and decision making.

The network interface 505 is used for network communication, such as providing transmission of data information. Those skilled in the art will appreciate that the configuration shown in fig. 3 is a block diagram of only a portion of the configuration associated with aspects of the present invention and is not intended to limit the computing device 500 to which aspects of the present invention may be applied, and that a particular computing device 500 may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

The processor 502 is configured to run the computer program 5032 stored in the memory to implement the full link testing method for disease entry and decision disclosed in the embodiment of the present invention.

Those skilled in the art will appreciate that the embodiment of a computer device illustrated in fig. 3 does not constitute a limitation on the specific construction of the computer device, and in other embodiments a computer device may include more or fewer components than those illustrated, or some components may be combined, or a different arrangement of components. For example, in some embodiments, the computer device may only include a memory and a processor, and in such embodiments, the structures and functions of the memory and the processor are consistent with those of the embodiment shown in fig. 3, and are not described herein again.

It should be understood that, in the embodiment of the present invention, the Processor 502 may be a Central Processing Unit (CPU), and the Processor 502 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.

In another embodiment of the invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a nonvolatile computer-readable storage medium or a volatile computer-readable storage medium. The computer readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the full link testing method for disease entry and decision making disclosed by embodiments of the present invention.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, devices and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again. Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

In the embodiments provided by the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only a logical division, and there may be other divisions when the actual implementation is performed, or units having the same function may be grouped into one unit, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.

The invention is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a magnetic disk, or an optical disk.

While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A full link testing method for disease entry and decision making is characterized by comprising the following steps:

2. The disease entry and decision making full link testing method of claim 1, wherein the generating target uniform resource locator information from the testing environment and the configuration file comprises:

extracting general interface information from the configuration file;

3. The full link test method for disease entry and decision making according to claim 1, wherein prior to said obtaining a preconfigured dictionary, said method further comprises:

configuring the coding of case pictures contained in each subfolder;

4. The full link test method for disease entry and decision making according to claim 1, further comprising:

5. The full link test method for disease entry and decision making according to claim 1, wherein prior to said invoking a preconfigured mapping table, said method further comprises:

acquiring historical test data according to a configured time period;

6. The full link test method for disease entry and decision making according to claim 1, wherein after said generating a test result, said method further comprises:

acquiring an execution log in a test process;

7. The full link test method for disease entry and decision making according to claim 1, wherein after said generating a test result, said method further comprises:

8. A full link testing apparatus for disease entry and decision making, comprising:

9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the computer program implements a full link testing method for disease entry and decision making according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, causes the processor to execute the method for full link testing for disease entry and decision making according to any one of claims 1 to 7.