CN117522037A - Multi-client multi-program product intelligent perception model - Google Patents
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Abstract
The invention discloses an intelligent perception model of a multi-client multi-procedure data product, which relates to the technical field of product evaluation and comprises three neural network layers of a resource library, a strategy allocation and perception application module, wherein each neural network layer consists of a plurality of execution units and neurons, the resource library is divided into static resources and dynamic resources, the static resources comprise but are not limited to data client attribute resources, rule resource libraries and environment resource libraries, and the dynamic resources comprise but are not limited to product resource libraries, detection resource libraries and external functional resource libraries. The invention provides an intelligent perception model for multi-client multi-program products, which realizes resource policy screening, and particularly analyzes and distinguishes different product data through an intelligent perception means, determines different screening policies according to resource occupation conditions, and finally adopts the same set of processing and detecting technology, thereby realizing processing and detecting of the multi-client multi-program products on one product data platform.
Description
Technical Field
The invention relates to the technical field of product evaluation, in particular to an intelligent perception model of a multi-client and multi-working-procedure product.
Background
The data products are products which can reduce the data threshold used by users, improve the data use efficiency, exert the data value and assist the users to make decisions and act. Different data products have different attributes, usually have fixed and corresponding processing technologies, have fixed and corresponding detection technologies, and for various similar product data, the data products are called as 'multi-process data product evaluation', and in the present intelligent age, the data products can be automatically realized by a robot intelligent means.
The invention discloses a multi-dimensional artificial intelligence product evaluation method and a device thereof, for example, the patent document with the application number of 202310687517.1, wherein the invention collects the evaluation data of each evaluation item of each evaluation object in each evaluation dimension, evaluates the evaluation object according to the evaluation method of each evaluation item, and obtains the evaluation result of the evaluation item of the evaluation object in the evaluation dimension; the method can realize the evaluation of multiple dimensions such as function evaluation, performance evaluation, safety evaluation and the like on the artificial intelligent model, the artificial intelligent algorithm and the artificial intelligent hardware, and the evaluation method of the evaluation item of the evaluation object in the function evaluation is an evaluation method based on an environmental condition set, so that the evaluation item of the evaluation object in the function evaluation is the completeness and correctness evaluation, and the advantages and disadvantages of the artificial intelligent product can be found more clearly.
However, existing product evaluation methods similar to the above application still have the following disadvantages:
the existing detection technology needs to process or detect on different platforms when facing multiple clients who touch frequently, needs to adopt different technologies, wastes much space and much resources, and cannot realize the comprehensive evaluation of the multi-client multi-procedure product on one evaluation platform by using an intelligent means, so that the detection efficiency is low, and the output of the product is affected.
Therefore, the invention aims at researching and improving the existing structure and deficiency, and provides an intelligent perception model of multi-client multi-process data products.
Disclosure of Invention
The invention aims to provide an intelligent perception model of a multi-client multi-process data product so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the intelligent perception model of the multi-client multi-program product comprises three neural network layers of a resource library, a strategy allocation and perception application module, each neural network layer comprises a plurality of execution units and neurons,
the resource library is divided into a static resource and a dynamic resource, wherein the static resource comprises but is not limited to a data client attribute resource, a rule resource library and an environment resource library, and the dynamic resource comprises but is not limited to a product resource library, a detection resource library and an external functional resource library;
the strategy allocation is used for reasonably and effectively distributing and utilizing resources required by the project in the project implementation process so as to ensure that the project can be completed in time, quality and quantity, and comprises a login strategy, a detection strategy, a time strategy, a collection strategy, a screening strategy and a downloading strategy;
the perception application module comprises a plurality of execution units, and the execution units comprise a plurality of neurons, wherein the execution units comprise a sensing unit, a screening unit and a source downloading unit.
Further, the data client attribute resource mainly comprises a data client name, a code, a plate, a main product, a technical attribute and intellectual property rights, the rule resource library mainly comprises a login rule, a detection rule, a time rule, an acquisition rule, a screening rule and a downloading rule, and the environment resource library mainly comprises an external resource path and authority, a cloud server, a database, cloud storage, a public visual detection tool, an authority setting and detection tool.
Furthermore, the rule resource library is used for storing detection data structures and algorithm rules, so that the data structures and algorithms which are agreed before are applied in the product detection process, the rules are required to be followed, the proper data structures are selected, and the conventional algorithms are adopted, so that complex algorithms are avoided.
Further, the product resource library mainly includes product attribute resources, product parameter resources, source paths and authority resources, the detection resource library mainly includes detection standard resources, detection laboratory resources, detection method resources, detection result patterns, detection numbers, authorization identifiers, authority settings, result report feedback formats, result feedback paths and authorities, and the external functional resource library often needs to be applied to external resources to support a certain functional index for realizing detection in the product detection process, including but not limited to standard, enterprise information, OCR, search engines and AI tools, and also often needs a certain authority, cost and periodic maintenance.
Furthermore, the tool supports a mainstream browser, simultaneously supports running in a headless mode and a headless mode, provides synchronous and asynchronous APIs, can be used in combination with a Pytest test framework, and supports automatic script recording at the browser side.
Further, the login strategy selects different login strategies according to different data client system login modes, the detection strategy detection element uses an external dynamic resource and a compliant data crawling tool to detect the position of a data object according to different data object systems by means of detection tools, and the acquisition strategy is determined by detecting the position of the data object at a normal and reasonable imitation manual speed, wherein the time strategy is a data acquisition time strategy and comprises production line demand triggering, timing triggering and checking idle triggering, the acquisition strategy is a data client classification strategy, the screening strategy is determined according to different dynamic data objects, and the downloading strategy is a source data downloading strategy and comprises a source data export position detection strategy, a data source breakpoint continuous transmission strategy and an export data source verification strategy.
Further, the sensing unit comprises a login element for completing login of the data client system, a detection element for completing position touch and detection of the data object and a collection element for completing collection of the data object.
Further, the screening unit comprises a dynamic element for completing dynamic data object policy determination and a screening element for completing data object screening.
Further, the source download unit comprises a connection element for detecting the connection state of the data client system, a continuation element for exporting and breakpoint continuous transmission of the source data of the data object and a check element for exporting and checking the source data of the data object.
Further, the usage flow of the intelligent perception model of the multi-client multi-program product is as follows:
step one, logging in a data client system: the login element automatically completes the automatic login of the data client system by means of the detection tool through the data client resources authorized by the compliance method;
detecting the position of the data object and determining an acquisition mode: the detection element touches and detects the position of the data object by means of a detection tool, determines an acquisition strategy, completes acquisition, and screens the acquired data object according to a screening strategy;
step three, determining a data acquisition time strategy: determining to adopt different acquisition strategies according to multiple factors such as the beat of a data production line, the auditing time of an auditing system, the concurrency number of external resources and the like;
step four, determining a data object acquisition strategy: selecting a corresponding data object acquisition method according to the number of application client resources, and if a data client system has an import/export function, adopting screening objects/data for direct import, aiming at the whole target data object, and having definite attribute; screening the big data of the object if the data object is far larger than the target data object; if the target data object occupies most or all of the data source, the screening object detection object is subjected to specified comparison;
step five, determining a dynamic data object screening strategy: in the evaluating process, new data to be evaluated enter the system continuously, a dynamic screening strategy is required to be adapted to the system, two correlated evaluating data object screening libraries are required to be established, namely a data object to be evaluated list library and a data object reference list library, a data object list captured by a data client system is stored and displayed through the data object to be evaluated list library, and a reference list is correspondingly provided through different data screening strategies of the data object reference list library;
step six, data object screening: according to the collection mode determined in the fourth step, adopting a corresponding screening strategy, wherein the method comprises the following steps: if the whole data object has definite attribute, all the data objects in the obtained data object pending list library are 'pending', the data objects which are not yet evaluated are set as 'pending-unprocessed', and the data object reference list library is added; if the data object is far larger than the target data object, all the intra-domain data objects in the obtained data object pending list library are pending, the pending-unprocessed data objects which are not yet evaluated are set to be pending, and the data and state are added and the data object reference list library is updated; if the target data object occupies most or all of the data sources, comparing all the data objects in the obtained data object pending list library with the data object reference list library, wherein the data objects are in accordance with 'pending', the data objects which have not been evaluated are set as 'pending-unprocessed', and the data object reference list library is updated in state;
step seven, source data export and downloading: the method comprises the steps of firstly detecting the connection state of a data client system to ensure the normal connection of the data client system and ensure the normal export of data and the downloading of accessory data, then determining the position of the process source data according to a source data export position detection strategy, including the process data and the document, setting a storage position according to static resources, ensuring the smooth proceeding of export and downloading processes according to a process data source intermittent connection prevention strategy, finally executing an export process data source verification strategy, and verifying the data and the document after the process data is exported.
The invention provides an intelligent perception model of a multi-client multi-process data product, which has the following beneficial effects:
the invention provides an intelligent perception model for realizing resource policy screening, which is characterized in that different product data are analyzed and distinguished through an intelligent perception means, different screening policies are determined according to the occupation condition of resources, and finally the same set of processing and detecting technology is adopted, so that the processing and the detection of multi-client multi-procedure data products on one product data platform are realized.
Drawings
FIG. 1 is a schematic diagram of a multi-client multi-program product intelligent perception model according to the present invention;
FIG. 2 is a schematic diagram of a comprehensive evaluation resource allocation model of the intelligent perception model of the multi-client multi-program product of the invention.
Detailed Description
Embodiments of the present invention are described in further detail below with reference to the accompanying drawings and examples. The following examples are illustrative of the invention but are not intended to limit the scope of the invention.
As shown in fig. 1-2, the multi-client multi-program product intelligent perception model consists of three neural network layers of a resource library, a strategy allocation and perception application module, and each neural network layer consists of a plurality of execution units and neurons,
the resource library is divided into a static resource and a dynamic resource, wherein the static resource comprises but is not limited to a data client attribute resource, a rule resource library and an environment resource library, and the dynamic resource comprises but is not limited to a product resource library, a detection resource library and an external functional resource library;
the data client attribute resources mainly comprise data client names, codes, affiliated plates, main products, technical attributes and intellectual property rights, the rule resource library mainly comprises login rules, detection rules, time rules, acquisition rules, screening rules and downloading rules, and the environment resource library mainly comprises external resource paths and authorities, cloud servers, databases, cloud storage, public visual detection tools, authority setting and detection tools;
the rule resource library is used for storing detection data structures and algorithm rules so that the data structures and algorithms which are appointed before are applied in the product detection process need to follow some rules, a proper data structure is selected, and a common algorithm is adopted, so that the complex algorithm is avoided;
the product resource library mainly comprises product attribute resources, product parameter resources, source paths and authority resources, the detection resource library mainly comprises detection standard resources, detection laboratory resources, detection method resources, detection result patterns, detection numbers, authorization identifiers, authority settings, result report feedback formats, result feedback paths and authorities, and the external functional resource library often needs to be applied to external resources to support a certain functional index for realizing detection in the product detection process, including but not limited to standards, enterprise information, OCR, search engines and AI tools, and also often needs certain authorities, cost and periodic maintenance;
the tool supports a mainstream browser, simultaneously supports running in a headless mode and a headless mode, provides synchronous and asynchronous APIs, can be used in combination with a Pytest test framework, and supports automatic script recording at a browser end;
the strategy allocation is used for reasonably and effectively distributing and utilizing resources required by the project in the project implementation process so as to ensure that the project can be completed in time, quality and quantity, and comprises a login strategy, a detection strategy, a time strategy, a collection strategy, a screening strategy and a downloading strategy;
the login strategy selects different login strategies according to different data client system login modes, the detection strategy detection element detects the positions of data objects by means of detection tools according to different data object systems and by using external dynamic resources and a compliant data crawling tool at normal and reasonable imitation manual speeds, and determines an acquisition strategy, wherein the time strategy is a data acquisition time strategy and comprises production line demand triggering, timing triggering and checking idle triggering, the acquisition strategy is a data client classification strategy, the screening strategy is determined according to different dynamic data objects, and the downloading strategy is a source data downloading strategy and comprises a source data export position detection strategy, a data source breakpoint continuous transmission strategy and an export data source verification strategy;
the sensing application module comprises a plurality of execution units, the execution units comprise a plurality of neurons, and the execution units comprise a sensing unit, a screening unit and a source downloading unit;
the sensing unit comprises a login element for completing login of the data client system, a detection element for completing position touch and detection of the data object and a collection element for completing collection of the data object;
the screening unit comprises a dynamic element for finishing dynamic data object strategy determination and a screening element for finishing data object screening;
the source downloading unit comprises a connecting element for detecting the connection state of the data client system, a continuous element for exporting and breakpoint continuous transmission of the source data of the data object and a check element for exporting and checking the source data of the data object.
In summary, with reference to fig. 1-2, the usage flow of the intelligent perception model of the multi-client multi-program product is as follows:
step one, logging in a data client system: the login element automatically completes the automatic login of the data client system by means of the detection tool through the data client resources authorized by the compliance method;
detecting the position of the data object and determining an acquisition mode: the detection element touches and detects the position of the data object by means of a detection tool, determines an acquisition strategy, completes acquisition, and screens the acquired data object according to a screening strategy;
step three, determining a data acquisition time strategy: determining to adopt different acquisition strategies according to multiple factors such as the beat of a data production line, the auditing time of an auditing system, the concurrency number of external resources and the like;
step four, determining a data object acquisition strategy: selecting a corresponding data object acquisition method according to the number of application client resources, and if a data client system has an import/export function, adopting screening objects/data for direct import, aiming at the whole target data object, and having definite attribute; screening the big data of the object if the data object is far larger than the target data object; if the target data object occupies most or all of the data source, the screening object detection object is subjected to specified comparison;
step five, determining a dynamic data object screening strategy: in the evaluating process, new data to be evaluated enter the system continuously, a dynamic screening strategy is required to be adapted to the system, two correlated evaluating data object screening libraries are required to be established, namely a data object to be evaluated list library and a data object reference list library, a data object list captured by a data client system is stored and displayed through the data object to be evaluated list library, and a reference list is correspondingly provided through different data screening strategies of the data object reference list library;
step six, data object screening: according to the collection mode determined in the fourth step, adopting a corresponding screening strategy, wherein the method comprises the following steps: if the whole data object has definite attribute, all the data objects in the obtained data object pending list library are 'pending', the data objects which are not yet evaluated are set as 'pending-unprocessed', and the data object reference list library is added; if the data object is far larger than the target data object, all the intra-domain data objects in the obtained data object pending list library are pending, the pending-unprocessed data objects which are not yet evaluated are set to be pending, and the data and state are added and the data object reference list library is updated; if the target data object occupies most or all of the data sources, comparing all the data objects in the obtained data object pending list library with the data object reference list library, wherein the data objects are in accordance with 'pending', the data objects which have not been evaluated are set as 'pending-unprocessed', and the data object reference list library is updated in state;
step seven, source data export and downloading: the method comprises the steps of firstly detecting the connection state of a data client system to ensure the normal connection of the data client system and ensure the normal export of data and the downloading of accessory data, then determining the position of the process source data according to a source data export position detection strategy, including the process data and the document, setting a storage position according to static resources, ensuring the smooth proceeding of export and downloading processes according to a process data source intermittent connection prevention strategy, finally executing an export process data source verification strategy, and verifying the data and the document after the process data is exported.
The embodiments of the invention have been presented for purposes of illustration and description, and are not intended to be exhaustive or limited to the invention in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, and to enable others of ordinary skill in the art to understand the invention for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (10)
1. The intelligent perception model of the multi-client multi-program product is characterized by comprising a resource library, a strategy allocation and perception application module and three neural network layers, wherein each neural network layer comprises a plurality of execution units and neurons,
the resource library is divided into a static resource and a dynamic resource, wherein the static resource comprises but is not limited to a data client attribute resource, a rule resource library and an environment resource library, and the dynamic resource comprises but is not limited to a product resource library, a detection resource library and an external functional resource library;
the strategy allocation is used for reasonably and effectively distributing and utilizing resources required by the project in the project implementation process so as to ensure that the project can be completed in time, quality and quantity, and comprises a login strategy, a detection strategy, a time strategy, a collection strategy, a screening strategy and a downloading strategy;
the perception application module comprises a plurality of execution units, and the execution units comprise a plurality of neurons, wherein the execution units comprise a sensing unit, a screening unit and a source downloading unit.
2. The multi-client multi-program data product intelligent perception model according to claim 1, wherein the data client attribute resources mainly comprise data client names, codes, affiliated plates, main products, technical attributes and intellectual property rights, the rule resource library mainly comprises login rules, detection rules, time rules, collection rules, screening rules and downloading rules, and the environment resource library mainly comprises external resource paths and authorities, cloud servers, databases, cloud storage, public visual detection tools, authority setting and detection tools.
3. The intelligent perception model of multi-client multi-program data product according to claim 1, wherein the rule repository is used for storing detection data structures and algorithm rules, so that the previously agreed data structures and algorithms used in the product detection process need to follow some rules, a proper data structure is selected, and a common algorithm is adopted, so that complex algorithms are avoided.
4. The multi-client multi-program product intelligent perception model according to claim 1, wherein the product resource library mainly comprises product attribute resources, product parameter resources, source paths and authority resources, the detection resource library mainly comprises detection standard resources, detection laboratory resources, detection method resources, detection result patterns, detection numbers, authorized identifications, authority settings, result report feedback formats and result feedback paths and authorities, and the external functional resource library often needs to be applied to external resources to support a certain functional index for realizing detection in the product detection process, including but not limited to standards, enterprise information, OCR, search engines, AI tools, and often needs a certain authority, cost and periodic maintenance.
5. The multi-client, multi-program product intelligent awareness model of claim 4, wherein the tool supports a mainstream browser while supporting running in headless mode, and provides a synchronous, asynchronous API that can be used in conjunction with a Pytest framework and supports automated script recording at the browser end.
6. The intelligent perception model of multi-client multi-program data products according to claim 1, wherein the login strategy is selected to adopt different login strategies according to different data client system login modes, the detection strategy detection element utilizes an external dynamic resource and a compliant data crawling tool to detect the position of a data object at a normal and reasonable imitation manual speed aiming at different data object systems by means of a detection tool, an acquisition strategy is determined, the time strategy is a data acquisition time strategy and comprises a production line demand trigger, a timing trigger and an audit idle trigger, the acquisition strategy is a data client classification strategy, the screening strategy is determined according to different dynamic data objects, and the downloading strategy is a source data downloading strategy and comprises a source data export position detection strategy, a data source breakpoint continuous transmission strategy and an export data source verification strategy.
7. The multi-client multi-program data product intelligent perception model according to claim 1, wherein the sensory unit comprises a login element for completing a data client system login, a probe element for completing a data object position touch, a probe, and a collection element for completing a data object collection.
8. The multi-client, multi-program data product intelligent awareness model of claim 1 wherein the screening unit comprises a dynamic element for performing dynamic data object policy determinations and a screening element for performing data object screening.
9. The multi-client multi-program data product intelligent perception model according to claim 1, wherein the source download unit comprises a connection element for data client system connection status detection, a continuation element for data object source data export and breakpoint resume, and a check element for data object source data export and check.
10. The multi-client multi-program data product intelligent awareness model according to any one of claims 1-9, wherein the multi-client multi-program data product intelligent awareness model is used as follows:
step one, logging in a data client system: the login element automatically completes the automatic login of the data client system by means of the detection tool through the data client resources authorized by the compliance method;
detecting the position of the data object and determining an acquisition mode: the detection element touches and detects the position of the data object by means of a detection tool, determines an acquisition strategy, completes acquisition, and screens the acquired data object according to a screening strategy;
step three, determining a data acquisition time strategy: determining to adopt different acquisition strategies according to multiple factors such as the beat of a data production line, the auditing time of an auditing system, the concurrency number of external resources and the like;
step four, determining a data object acquisition strategy: selecting a corresponding data object acquisition method according to the number of application client resources, and if a data client system has an import/export function, adopting screening objects/data for direct import, aiming at the whole target data object, and having definite attribute; screening the big data of the object if the data object is far larger than the target data object; if the target data object occupies most or all of the data source, the screening object detection object is subjected to specified comparison;
step five, determining a dynamic data object screening strategy: in the evaluating process, new data to be evaluated enter the system continuously, a dynamic screening strategy is required to be adapted to the system, two correlated evaluating data object screening libraries are required to be established, namely a data object to be evaluated list library and a data object reference list library, a data object list captured by a data client system is stored and displayed through the data object to be evaluated list library, and a reference list is correspondingly provided through different data screening strategies of the data object reference list library;
step six, data object screening: according to the collection mode determined in the fourth step, adopting a corresponding screening strategy, wherein the method comprises the following steps: if the whole data object has definite attribute, all the data objects in the obtained data object pending list library are 'pending', the data objects which are not yet evaluated are set as 'pending-unprocessed', and the data object reference list library is added; if the data object is far larger than the target data object, all the intra-domain data objects in the obtained data object pending list library are pending, the pending-unprocessed data objects which are not yet evaluated are set to be pending, and the data and state are added and the data object reference list library is updated; if the target data object occupies most or all of the data sources, comparing all the data objects in the obtained data object pending list library with the data object reference list library, wherein the data objects are in accordance with 'pending', the data objects which have not been evaluated are set as 'pending-unprocessed', and the data object reference list library is updated in state;
step seven, source data export and downloading: the method comprises the steps of firstly detecting the connection state of a data client system to ensure the normal connection of the data client system and ensure the normal export of data and the downloading of accessory data, then determining the position of the process source data according to a source data export position detection strategy, including the process data and the document, setting a storage position according to static resources, ensuring the smooth proceeding of export and downloading processes according to a process data source intermittent connection prevention strategy, finally executing an export process data source verification strategy, and verifying the data and the document after the process data is exported.
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