CN116485508A - Mobile intelligent evaluation system and intelligent evaluation supervision method based on cloud technology - Google Patents
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Abstract
The invention relates to the technical field of intelligent bid evaluation, in particular to a mobile intelligent bid evaluation system and an intelligent bid evaluation supervision method based on a cloud technology, which are characterized in that a plurality of mobile bid evaluation subsystems integrated with desktop monitoring, site monitoring and video consultation monitoring are used for collecting target item bid evaluation related data, and cloud service systems connected with each mobile bid evaluation subsystem and used for carrying out data processing and storage on the bid evaluation related data are used for sensing and identifying abnormal bid evaluation behaviors, so that target item bid evaluation violation events are generated; and recording and archiving the target project rating violation event, and forming a violation event disposal closed loop through early warning reminding and tracking feedback. The invention can realize localized AI analysis and alarm, reduce network flow, improve alarm efficiency, improve evaluation quality, actively build good evaluation environment, break time, space and human factor limit, and make evaluation service more convenient and efficient.
Description
Technical Field
The invention relates to the technical field of intelligent bid evaluation, in particular to a mobile intelligent bid evaluation system based on a cloud technology and an intelligent bid evaluation supervision method realized based on the system.
Background
The bidding is an important mode of market competition, the market competition principle of 'public, fair and fair' can be fully embodied, a plurality of bidders can conduct fair competition through bidding purchasing, and optimal goods, engineering or service can be obtained at the lowest or lower price, so that the purposes of improving economic benefit and social benefit, improving the quality of bidding projects, improving the fund use efficiency and the like are achieved. For some large-scale bidding projects, the regional involvement is wide, the number of people is numerous, all participants in each region need to be coordinated and supervised for review at the same time, but the problems of inconvenient traffic, review cost and the like can influence the attendance of bidding experts, and further the quality and efficiency of the purchasing process are seriously influenced.
The existing evaluation mark adopts an online conference mode, for example, all parties access to a Tencent conference to share a computer screen and real-time video information, but expert extraction can only be completed on line, on-site video recording can only be completed manually and uploaded, and on-site abnormal alarm conditions (such as smoking, sleeping sentry and the like) can only be found manually. The method is characterized in that a method of evaluating the target cloud platform and a remote target evaluating platform is adopted, all parties access the remote target evaluating platform, a computer screen and real-time video information are shared, expert extraction, on-site video recording and on-site abnormal condition alarming are completed through the target evaluating cloud platform and the remote target evaluating platform, but all on-site videos must be returned to the target evaluating cloud platform, AI analysis is carried out on the videos by the cloud platform, and finally, an analysis result alarming mode is returned to the remote target evaluating platform, so that a large amount of network flow is consumed, meanwhile, alarming delay is large, and user perception is poor.
Disclosure of Invention
Therefore, the invention provides a mobile intelligent bid evaluation system based on a cloud technology and an intelligent bid evaluation supervision method realized based on the system, which solve the problem of alarm delay in the existing remote bid evaluation return.
According to the design scheme provided by the invention, a mobile intelligent evaluation system based on a cloud technology is provided, comprising: the mobile bid evaluation system comprises a plurality of mobile bid evaluation subsystems integrated with desktop monitoring, site monitoring and video consultation monitoring and used for collecting target project bid evaluation related data, and cloud service systems connected with each mobile bid evaluation subsystem and used for carrying out data processing and storage on the bid evaluation related data, wherein the mobile bid evaluation subsystem is provided with a bid evaluation client used for remotely evaluating the targets of an expert and a local server connected with the bid evaluation client, a cloud desktop used for carrying out real-time monitoring on an expert bid evaluation interface and the audios and videos of the bid consultation and a site behavior collecting end used for carrying out real-time monitoring on the bid evaluation site are arranged on the bid evaluation client, the cloud desktop and the site behavior collecting end are all connected with the local server, and the local server carries out data processing on received real-time monitoring data to perceive and identify abnormal bid evaluation behavior data and encrypt and upload the abnormal bid evaluation behavior data to the cloud service system, and simultaneously carries out encryption storage on the received real-time monitoring data by utilizing the local server.
As the mobile intelligent evaluation system based on the cloud technology, the evaluation subsystem further comprises: a fixed evaluation subsystem; the fixed bid evaluation subsystem comprises a bid evaluation client side which is arranged in a bid evaluation room and used for expert bid evaluation, a cloud desktop which is arranged on the bid evaluation client side and used for carrying out real-time monitoring on an expert bid evaluation interface and bid evaluation consultation audios and videos, a network hard disk video recording terminal which is used for carrying out merging and storing on the expert bid evaluation interface and the bid evaluation consultation audios and videos, a fixed bid evaluation room environment video acquisition end which is arranged in the fixed bid evaluation room and used for carrying out real-time monitoring on a fixed bid evaluation place, and a project management end which is fixed in the bid evaluation room and used for receiving real-time monitoring data of the network hard disk video recording terminal and the fixed bid evaluation room environment video acquisition end and perceiving and identifying abnormal bid evaluation behaviors, wherein the project management end is connected with a cloud service system and uploads the received real-time monitoring data and the perceived and identified abnormal bid evaluation behaviors to the cloud service system.
As the mobile intelligent bid evaluation system based on the cloud technology, the local server or the project management end further utilizes an AI intelligent algorithm to perceive and identify abnormal bid evaluation behaviors in the process of perceiving and identifying the abnormal bid evaluation behaviors, wherein the AI intelligent algorithm comprises the following steps: firstly, dividing real-time monitoring data, and intercepting audio and video clips in the real-time monitoring data; the pre-trained machine learning model is then utilized to identify output abnormal scoring behavior.
As the mobile intelligent evaluation system based on the cloud technology, the invention further comprises the following steps: and intercepting an abnormal evaluation behavior picture or video fragment in the monitoring data, recording and parallel archiving the picture or video fragment and uploading the picture or video fragment to a cloud service system, wherein the evaluation project file number, the evaluation project file type and the abnormal behavior related information are recorded simultaneously in the linkage archiving, and the abnormal behavior related information at least comprises evaluation rule violation classification, evaluation rule violation event, evaluation rule violation time and evaluation rule violation description.
As the mobile intelligent evaluation system based on the cloud technology, the field behavior acquisition terminal further comprises: the visible light camera is used for collecting global video data of the evaluation place, the thermal infrared imager is used for detecting personnel data of the evaluation place through thermal imaging video, and the RFID reader-writer is used for identifying the identity of the evaluation personnel.
Further, the invention also provides an intelligent evaluation and supervision method based on the cloud technology, which is realized based on the mobile intelligent evaluation and supervision system, and the realization process comprises the following steps:
and aiming at the collected expert bid evaluation interface, bid evaluation consultation audio and video and bid evaluation field data, sensing and identifying abnormal bid evaluation behaviors, and generating target project bid evaluation violation events, wherein the abnormal bid evaluation behaviors comprise but are not limited to: evaluation field personnel violation status and violation behavior;
recording and archiving the target project rating violation event, and forming a violation event disposal closed loop through early warning reminding and tracking feedback.
As the intelligent evaluation and supervision method based on the cloud technology, the invention further senses and identifies abnormal evaluation behaviors, and comprises the following steps: the number of the personnel in the bid evaluation place, the validity of the personnel identity in the bid evaluation place and the validity of the behavior state of the bid evaluation personnel are identified through the global video data of the bid evaluation place, the personnel data of the bid evaluation place and the identity of the bid evaluation personnel, which are acquired by the site behavior acquisition end.
As the intelligent label evaluation supervision method based on the cloud technology, in the process of identifying the identity legitimacy of the label evaluation place personnel, the target item label evaluation participator is taken as the current target item label evaluation staff in advance, and the related information of the current target item label evaluation staff is input into the white list staff database, wherein the target item label evaluation participator information comprises but is not limited to: person name, person fingerprint data and/or person face data; and comparing the personnel information of the evaluation site personnel with personnel information in the white list personnel database to identify whether the evaluation site personnel are legal personnel in the preset white list personnel database.
As the intelligent evaluation and supervision method based on the cloud technology, in the validity of the behavior state of the evaluation staff, the AI intelligent algorithm is utilized to sense and identify the rule breaking state and/or rule breaking behavior of the evaluation staff on site in the monitoring data through the pre-trained machine learning model, wherein the machine learning model adopts a network structure integrated with a staff state classification and identification network model and a staff behavior classification and identification network model.
As the intelligent evaluation supervision method based on the cloud technology, the invention further records and files the target project evaluation offence, and simultaneously records the target project name, the evaluation offence behavior type, the evaluation offence behavior time and the evaluation offence description for tracing and allocating offence.
The invention has the beneficial effects that:
the invention has the advantages that the information interaction between the label evaluation subsystem and the cloud server arranged in each place is stable, the multi-tenant mode is allowed, the mobile label evaluation subsystem integrating functions of monitoring, identification, allocation, early warning, tracing and the like is used for realizing localized AI analysis and warning, reducing network flow and improving warning efficiency, and the system label evaluation platform can realize cross-regional authorization of the grade of entering and exiting from a label evaluation place, intelligent identification of personnel behaviors, automatic sign-in registration, trace whole-course trace and the like, so that the interconnection interaction of a label evaluation platform, service equipment, a label evaluation expert and a label evaluation place in an internet-of-things manner is convenient for realizing, the AI intelligent supervision in the label evaluation process can be realized, the label evaluation quality is improved, a good label evaluation environment is positively built, the limitation of time, space and human factors is broken, the label evaluation platform can participate in the label evaluation as long as the system label evaluation platform is opened at any time and any place, the label evaluation service is more green and intelligent, the label evaluation service is more convenient and efficient, and the discovered abnormal behaviors can be timely sent to a prompt and faithful record through the management and control center, and the real-time examination of a work file and a real-time examination and a real-time audit service can be provided; the movable and fixed evaluation subsystem can be combined to realize the monitoring, encryption and anti-interference of activities such as cross-region fixed and movable consultation places, consultation computer desktops, expert video consultation, and the like, and the cloud technology can be combined to enable the monitoring and auditing data of all consultation projects to be filed and checked on line, so that the purchasing quality and efficiency are ensured.
Description of the drawings:
FIG. 1 is a schematic block diagram illustration of a mobile intelligent bid evaluation system based on cloud technology in an embodiment;
FIG. 2 is a schematic block diagram of a mobile label evaluation subsystem integrated machine in an embodiment;
fig. 3 is an application schematic of the mobile intelligent label evaluation system in the embodiment.
The specific embodiment is as follows:
the present invention will be described in further detail with reference to the drawings and the technical scheme, in order to make the objects, technical schemes and advantages of the present invention more apparent.
Aiming at factors such as regional traffic inconvenience in centralized evaluation, the embodiment of the invention, as shown in fig. 1, provides a mobile intelligent evaluation system based on a cloud technology, which comprises: the mobile bid evaluation system comprises a plurality of mobile bid evaluation subsystems integrated with desktop monitoring, site monitoring and video consultation monitoring and used for collecting target project bid evaluation related data, and cloud service systems connected with each mobile bid evaluation subsystem and used for carrying out data processing and storage on the bid evaluation related data, wherein the mobile bid evaluation subsystem is provided with a bid evaluation client used for remotely evaluating the targets of an expert and a local server connected with the bid evaluation client, a cloud desktop used for carrying out real-time monitoring on an expert bid evaluation interface and the audios and videos of the bid consultation and a site behavior collecting end used for carrying out real-time monitoring on the bid evaluation site are arranged on the bid evaluation client, the cloud desktop and the site behavior collecting end are all connected with the local server, and the local server carries out data processing on received real-time monitoring data to perceive and identify abnormal bid evaluation behavior data and encrypt and upload the abnormal bid evaluation behavior data to the cloud service system, and simultaneously carries out encryption storage on the received real-time monitoring data by utilizing the local server. The mobile label evaluation subsystem integrated machine integrated with desktop monitoring, site monitoring and video consultation monitoring is used for data acquisition, a local server is used for analysis, second-level perception early warning of abnormal label evaluation can be achieved, secondary encryption can be carried out on label evaluation data by the local server, safe remote label evaluation is achieved, data transmission and storage of an encryption algorithm are supported, data cannot be tampered, and even if a network is interrupted, the site monitoring can still upload a storage center for archiving afterwards.
As a preferred embodiment, further, the rating subsystem further comprises: a fixed evaluation subsystem; the fixed bid evaluation subsystem comprises a bid evaluation client side which is arranged in a bid evaluation room and used for expert bid evaluation, a cloud desktop which is arranged on the bid evaluation client side and used for carrying out real-time monitoring on an expert bid evaluation interface and bid evaluation consultation audios and videos, a network hard disk video recording terminal which is used for carrying out merging and storing on the expert bid evaluation interface and the bid evaluation consultation audios and videos, a fixed bid evaluation room environment video acquisition end which is arranged in the fixed bid evaluation room and used for carrying out real-time monitoring on a fixed bid evaluation place, and a project management end which is fixed in the bid evaluation room and used for receiving real-time monitoring data of the network hard disk video recording terminal and the fixed bid evaluation room environment video acquisition end and perceiving and identifying abnormal bid evaluation behaviors, wherein the project management end is connected with a cloud service system and uploads the received real-time monitoring data and the perceived and identified abnormal bid evaluation behaviors to the cloud service system. The movable and fixed evaluation sub-system is combined to realize the monitoring, encryption and interference resistance of activities such as cross-region fixed and movable consultation places, consultation computer desktops, expert video consultation and the like, and the monitoring and auditing data of all consultation projects can be filed and checked on line, so that the purchasing quality and efficiency are ensured.
In the process of perceiving and identifying the abnormal bid evaluation behavior, the local server or the project management end can specifically utilize an AI intelligent algorithm to perceive and identify the abnormal bid evaluation behavior, wherein the AI intelligent algorithm comprises: firstly, dividing real-time monitoring data, and intercepting audio and video clips in the real-time monitoring data; the pre-trained machine learning model is then utilized to identify output abnormal scoring behavior.
Further, in the embodiment of the present disclosure, the local server or the project management end perceives and identifies an abnormal evaluation behavior, further includes: and intercepting an abnormal evaluation behavior picture or video fragment in the monitoring data, recording and parallel archiving the picture or video fragment and uploading the picture or video fragment to a cloud service system, wherein the evaluation project file number, the evaluation project file type and the abnormal behavior related information are recorded simultaneously in the linkage archiving, and the abnormal behavior related information at least comprises evaluation rule violation classification, evaluation rule violation event, evaluation rule violation time and evaluation rule violation description.
Referring to fig. 2, the site behavior acquisition end may include: the visible light camera is used for collecting global video data of the evaluation place, the thermal infrared imager is used for detecting personnel data of the evaluation place through thermal imaging video, and the RFID reader-writer is used for identifying the identity of the evaluation personnel. By configuring a visible light camera, an infrared camera, an RFID reader-writer and the like at the site behavior acquisition end, real-time monitoring and localized second-level AI warning of the whole evaluation site are realized, network traffic is reduced, and warning efficiency is improved.
Further, the embodiment of the invention also provides an intelligent evaluation and supervision method based on the cloud technology, which is realized based on the mobile intelligent evaluation and supervision system, and the realization process comprises the following steps:
and aiming at the collected expert bid evaluation interface, bid evaluation consultation audio and video and bid evaluation field data, sensing and identifying abnormal bid evaluation behaviors, and generating target project bid evaluation violation events, wherein the abnormal bid evaluation behaviors comprise but are not limited to: evaluation field personnel violation status and violation behavior;
recording and archiving the target project rating violation event, and forming a violation event disposal closed loop through early warning reminding and tracking feedback.
Referring to fig. 3, the main functions of the cloud software may be designed to include basic capabilities such as distributed management, security authentication, data security, etc., and business functions such as project management, command scheduling, business supervision, statistical analysis, etc. In the client software, the client is mainly used by a manager and a bid evaluation expert, and the manager can record or report abnormal conditions in the bid evaluation process by controlling the whole bid evaluation process in use. When in use, the experts can realize the real-time voice or video communication between the bid evaluation experts to carry out consultation, know the bid evaluation content of the project and carry out the bid evaluation of the project.
As a preferred embodiment, further, sensing and identifying abnormal bid evaluation behavior includes: the number of the personnel in the bid evaluation place, the validity of the personnel identity in the bid evaluation place and the validity of the behavior state of the bid evaluation personnel are identified through the global video data of the bid evaluation place, the personnel data of the bid evaluation place and the identity of the bid evaluation personnel, which are acquired by the site behavior acquisition end.
The intelligent abnormal evaluation behavior algorithm is carried to support the collection and analysis of three major core information of personnel, equipment and environment, and when behaviors such as unmanned places, mobile phone playing, smoking, off duty, sleeping duty, frequent entrance and exit, illegal personnel entrance and the like occur, alarm prompt is automatically carried out, and records can be automatically saved after abnormal recognition.
Specifically, in the identification of the validity of the personnel identity in the evaluation place, the target item evaluation participator is taken as the current target item evaluation staff in advance, and the related information of the current target item evaluation staff is recorded into a white list staff database, wherein the target item evaluation participator information includes but is not limited to: person name, person fingerprint data and/or person face data; and comparing the personnel information of the evaluation site personnel with personnel information in the white list personnel database to identify whether the evaluation site personnel are legal personnel in the preset white list personnel database.
Specifically, in the validity of the behavior state of the label evaluation personnel, an AI intelligent algorithm is utilized to sense and identify the rule breaking state and/or rule breaking behavior of the label evaluation field personnel in the monitoring data through a pre-trained machine learning model, wherein the machine learning model adopts a network structure integrated with a personnel state classification identification network model and a personnel behavior classification identification network model.
In the process of recording and archiving the target project rating violation event, the target project name, the rating violation event behavior type, the rating violation event behavior time and the rating violation event description for tracing and allocating the violation behavior can be recorded simultaneously.
For the abnormal evaluation behaviors common at present, the perception and identification process of each behavior can be described as follows:
in unmanned site identification, a 1080P zoom camera can be used as a visible light camera for real-time global video acquisition of a comment site; the 320x240 temperature measurement type thermal imager can be used as an infrared thermal imager, and can be used for shooting thermal imaging videos in real time in an evaluation place and detecting the surface temperature of an object in the videos; when the surface temperature of an object in a video is detected to be between 35 and 37 ℃ by using a built-in algorithm based on the thermal infrared imager, the object is primarily identified as a person, and meanwhile, the relevant video is transmitted to a living body detection AI machine identification module; the video-based living body detection AI module is used for identifying whether personnel and the number of the personnel in the evaluation place are present or not through the actions and behaviors of the object; when no person is detected at the evaluation site, an unmanned alarm at the evaluation site can be automatically sent out, and a thermal imaging photo and a visible light photo are taken and site position information is recorded; and transmitting the thermal imaging photo, the visible light photo and the site location information 4G/5G communication module to a local server in real time.
In the mobile phone playing identification realization process, a visible light camera is utilized to collect global videos of the evaluation places in real time; when the surface temperature of an object in a video is detected to be between 35 ℃ and 37 ℃ based on the thermal infrared imager, primarily identifying the object as a person, simultaneously transmitting the relevant video to a mobile phone detection AI machine identification module, and identifying whether the person plays a mobile phone in the video by using the machine identification module through the action, the behavior and the appearance of the object; when detecting that a person plays a mobile phone on the evaluation site, automatically sending out an alarm of the person playing the mobile phone on the evaluation site, shooting a thermal imaging photo and a visible light photo at the same time, recording site location information, and transmitting the thermal imaging photo, the visible light photo and the site location information to a local server in real time through a 4G/5G communication module configured on the integrated machine.
In the smoking identification realization process, a visible light camera is utilized to acquire global video acquisition of a comment place in real time; shooting a thermal imaging video in real time by using a thermal infrared imager in an evaluation place, detecting the surface temperature of an object in the video, primarily recognizing the object as a cigarette end when the surface temperature of the object in the video is above 50 ℃ based on the detection of the thermal infrared imager, simultaneously transmitting the related video to a cigarette end detection AI machine recognition module, and recognizing whether the cigarette ends and the number of the cigarette ends exist in the video or not according to the shape and the color of the object; when detecting that people smoke at the evaluation site, automatically sending out an evaluation site smoke alarm, shooting a thermal imaging photo and a visible light photo at the same time, recording site position information, and transmitting the thermal imaging photo, the visible light photo and the site position information to a local server in real time through a 4G/5G communication module configured on the integrated machine.
In the off-duty identification realization process, configuring an ultrahigh frequency RFID chest card for each label evaluation person, binding the name of the person with the ID of the ultrahigh frequency RFID chest card so as to identify the person in the label evaluation place, and requiring all label evaluation persons to wear the ultrahigh frequency RFID chest card; the ultra-high frequency RFID reader-writer and the antenna are utilized to detect an ultra-high frequency RFID chest card worn by a label reader in a label evaluation place, the ultra-high frequency RFID chest card can be read once every 10 seconds, the label reader is indicated to be on duty when the ultra-high frequency RFID chest card ID is read, and the label reader is indicated to be off duty when the ultra-high frequency RFID chest card ID is not read; when detecting that someone leaves the post on the mark evaluation site, the electronic mark evaluation and supervision system all-in-one machine automatically sends out mark evaluation site off-post alarms, simultaneously shoots visible light photos, records site location information, ultra-high frequency RFID chest card ID and off-post personnel names, and transmits the visible light photos, the site location information, the ultra-high frequency RFID chest card ID and the off-post personnel names to a local server in real time through a 4G/5G communication module.
In the sleep post identification realization process, a visible light camera is utilized to collect global video of an evaluation place in real time, when the surface temperature of an object in the video is detected to be between 35 ℃ and 37 ℃ based on a thermal infrared imager, the object is primarily identified as a person, and meanwhile, the related video is transmitted to an electronic evaluation and supervision system all-in-one machine to be configured in a sleep post detection AI machine identification module based on the video, and whether personnel sleep post exists in the video is identified through the action and behavior of the object; when the person sleeping sentry on the evaluation site is detected, the alarm of the person sleeping sentry on the evaluation site is automatically sent out, a thermal imaging photo and a visible light photo are taken at the same time, site position information is recorded, and the thermal imaging photo, the visible light photo and the site position information are transmitted to a local server in real time through a 4G/5G communication module configured on the integrated machine.
In the realization process of frequent entrance and exit identification, configuring an ultrahigh frequency RFID chest card for each label evaluation person, and binding the name of the person with the ID of the ultrahigh frequency RFID chest card for identifying the identity of the person in a label evaluation place, so that all label evaluation persons are required to wear the ultrahigh frequency RFID chest card; the ultrahigh frequency RFID reader-writer and the antenna are utilized to detect an ultrahigh frequency RFID chest card worn by a label reader in a label evaluation place; reading the ultra-high frequency RFID chest card once every 10 seconds, indicating that the label evaluation personnel is on duty when the ultra-high frequency RFID chest card ID is read, and indicating that the label evaluation personnel is off duty when the ultra-high frequency RFID chest card ID is not read; and can set up and detect that a certain comment personnel has off duty and on duty condition in 60 seconds many times, confirm that this comment personnel has frequent business turn over condition; when frequent entrance and exit of personnel in the evaluation site are detected, frequent entrance and exit alarms of personnel in the evaluation site are automatically sent out, meanwhile, a visible light photo is shot, site position information, an ultra-high frequency RFID chest card ID and names of the personnel which enter and exit frequently are recorded, and the visible light photo, the site position information, the ultra-high frequency RFID chest card ID and the names of the personnel which enter and exit frequently are transmitted to a local server in real time through a 4G/5G communication module.
In the process of realizing illegal personnel entering identification, a face recognition camera can be used as a face comparator, and face recognition is rapidly completed through a built-in GPU graphic calculation unit and a white list database, so that photos are not required to be uploaded to a local server through a network, and the face recognition efficiency is greatly improved; shooting a front photo and a side photo of legal personnel in the evaluation field, and inputting the front photo and the side photo into a white list database of a face recognition camera, wherein the legal personnel comprise evaluation field evaluation personnel; in the process of evaluating the mark, a face recognition camera captures face information in an image in real time and compares the face information with a white list; when the face recognition camera finds faces outside the white list, automatically sending out an illegal person entering alarm, storing illegal person photos and recording site position information; and transmitting the illegal personnel photos and the site location information to a local server in real time through a 4G/5G communication module.
The method aims at the problems that the existing evaluation platform is poor in efficiency in retrospective timeliness, only can passively trace and turn over sudden event postmortem, real-time early warning, real-time alarm and quick processing cannot be achieved, fragmented video data is not integrally managed and controlled, and a large amount of video data of a database is not formed into useful global information for decision reference and the like. In the embodiment of the present disclosure, by automatically storing the offending video and offending audio, two storage modes may be set, including picture interception of offending behavior (supporting a picture format such as JPG, BMP, PNG) and video clip interception (supporting a video format such as MP3 and MP 4); for the illegal behaviors, the configuration is stored in a linkage filing system in a picture/video/report mode, and information such as corresponding file numbers, types, illegal classifications, illegal events, illegal time, illegal descriptions and the like is recorded at the same time so as to trace the events when needed.
The evaluation mark supervision scheme can carry out hierarchical and refined management on the events prejudged by the evaluation mark system, forms an event handling closed loop through pushing, feedback, statistics and the like of tracking and early warning, can generate a data result and upload the data result to a cloud service system so as to carry out analysis and arrangement of related data, realize the digital transformation of evaluation mark AI identification, solve the evaluation mark process, trace and allocate illegal behaviors and ensure the controllability, the traceability and the traceability of the whole evaluation mark process; meanwhile, the second-level warning of abnormal bid evaluation behaviors can be realized by combining real-time behavior analysis, the legal and compliance of bid evaluation work is ensured, the bid-incurring cost is saved, the differential rate cost is reduced, and the bid evaluation quality is improved.
The relative steps, numerical expressions and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The elements and method steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or a combination thereof, and the elements and steps of the examples have been generally described in terms of functionality in the foregoing description to clearly illustrate 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 solution. Those of ordinary skill in the art may implement the described functionality using different methods for each particular application, but such implementation is not considered to be beyond the scope of the present invention.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the above methods may be performed by a program that instructs associated hardware, and that the program may be stored on a computer readable storage medium, such as: read-only memory, magnetic or optical disk, etc. Alternatively, all or part of the steps of the above embodiments may be implemented using one or more integrated circuits, and accordingly, each module/unit in the above embodiments may be implemented in hardware or may be implemented in a software functional module. The present invention is not limited to any specific form of combination of hardware and software.
Finally, it should be noted that: the above examples are only specific embodiments of the present invention, and are not intended to limit the scope of the present invention, but it should be understood by those skilled in the art that the present invention is not limited thereto, and that the present invention is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. The mobile intelligent label evaluation system based on the cloud technology is characterized by comprising: the mobile bid evaluation system comprises a plurality of mobile bid evaluation subsystems integrated with desktop monitoring, site monitoring and video consultation monitoring and used for collecting target project bid evaluation related data, and cloud service systems connected with each mobile bid evaluation subsystem and used for carrying out data processing and storage on the bid evaluation related data, wherein the mobile bid evaluation subsystem is provided with a bid evaluation client used for remotely evaluating the targets of an expert and a local server connected with the bid evaluation client, a cloud desktop used for carrying out real-time monitoring on an expert bid evaluation interface and the audios and videos of the bid consultation and a site behavior collecting end used for carrying out real-time monitoring on the bid evaluation site are arranged on the bid evaluation client, the cloud desktop and the site behavior collecting end are all connected with the local server, and the local server carries out data processing on received real-time monitoring data to perceive and identify abnormal bid evaluation behavior data and encrypt and upload the abnormal bid evaluation behavior data to the cloud service system, and simultaneously carries out encryption storage on the received real-time monitoring data by utilizing the local server.
2. The cloud technology based mobile intelligent label evaluation system of claim 1, wherein the label evaluation subsystem further comprises: a fixed evaluation subsystem; the fixed bid evaluation subsystem comprises a bid evaluation client side which is arranged in a bid evaluation room and used for expert bid evaluation, a cloud desktop which is arranged on the bid evaluation client side and used for carrying out real-time monitoring on an expert bid evaluation interface and bid evaluation consultation audios and videos, a network hard disk video recording terminal which is used for carrying out merging and storing on the expert bid evaluation interface and the bid evaluation consultation audios and videos, a fixed bid evaluation room environment video acquisition end which is arranged in the fixed bid evaluation room and used for carrying out real-time monitoring on a fixed bid evaluation place, and a project management end which is fixed in the bid evaluation room and used for receiving real-time monitoring data of the network hard disk video recording terminal and the fixed bid evaluation room environment video acquisition end and perceiving and identifying abnormal bid evaluation behaviors, wherein the project management end is connected with a cloud service system and uploads the received real-time monitoring data and the perceived and identified abnormal bid evaluation behaviors to the cloud service system.
3. The cloud technology-based mobile intelligent bid evaluation system according to claim 1 or 2, wherein the local server or the project management end uses an AI intelligent algorithm to perceive and recognize abnormal bid evaluation behaviors in perceiving and recognizing the abnormal bid evaluation behaviors, wherein the AI intelligent algorithm comprises: firstly, dividing real-time monitoring data, and intercepting audio and video clips in the real-time monitoring data; the pre-trained machine learning model is then utilized to identify output abnormal scoring behavior.
4. The cloud technology-based mobile intelligent bid evaluation system of claim 3, wherein the local server or the project management end perceives and recognizes abnormal bid evaluation behaviors, and further comprises: and intercepting an abnormal evaluation behavior picture or video fragment in the monitoring data, recording and parallel archiving the picture or video fragment and uploading the picture or video fragment to a cloud service system, wherein the evaluation project file number, the evaluation project file type and the abnormal behavior related information are recorded simultaneously in the linkage archiving, and the abnormal behavior related information at least comprises evaluation rule violation classification, evaluation rule violation event, evaluation rule violation time and evaluation rule violation description.
5. The cloud technology-based mobile intelligent bid evaluation system of claim 1, wherein the venue behavior acquisition terminal comprises: the visible light camera is used for collecting global video data of the evaluation place, the thermal infrared imager is used for detecting personnel data of the evaluation place through thermal imaging video, and the RFID reader-writer is used for identifying the identity of the evaluation personnel.
6. The intelligent evaluation and supervision method based on the cloud technology is characterized by being realized based on the mobile intelligent evaluation and supervision system of claim 1, and the realization process comprises the following steps:
and aiming at the collected expert bid evaluation interface, bid evaluation consultation audio and video and bid evaluation field data, sensing and identifying abnormal bid evaluation behaviors, and generating target project bid evaluation violation events, wherein the abnormal bid evaluation behaviors comprise but are not limited to: evaluation field personnel violation status and violation behavior;
recording and archiving the target project rating violation event, and forming a violation event disposal closed loop through early warning reminding and tracking feedback.
7. The cloud technology-based intelligent bid evaluation supervision method according to claim 6, wherein sensing and identifying abnormal bid evaluation behaviors comprises: the number of the personnel in the bid evaluation place, the validity of the personnel identity in the bid evaluation place and the validity of the behavior state of the bid evaluation personnel are identified through the global video data of the bid evaluation place, the personnel data of the bid evaluation place and the identity of the bid evaluation personnel, which are acquired by the site behavior acquisition end.
8. The cloud technology-based intelligent bid evaluation supervision method according to claim 7, wherein in the identification of the validity of the identity of the bid evaluation place personnel, the target project bid evaluation participant is taken as the current target project bid evaluation participant in advance, and the related information of the current target project bid evaluation participant is recorded into a white list personnel database, wherein the target project bid evaluation participant information includes but is not limited to: person name, person fingerprint data and/or person face data; and comparing the personnel information of the evaluation site personnel with personnel information in the white list personnel database to identify whether the evaluation site personnel are legal personnel in the preset white list personnel database.
9. The cloud technology-based intelligent evaluation and supervision method according to claim 7, wherein in the validity of the behavior state of the evaluation staff, the rule breaking state and/or rule breaking behavior of the evaluation staff on the scene in the monitoring data are perceived and identified by using an AI intelligent algorithm through a pre-trained machine learning model, wherein the machine learning model adopts a network structure integrated with a staff state classification identification network model and a staff behavior classification identification network model.
10. The cloud technology-based intelligent evaluation and supervision method according to claim 6, wherein in the process of recording and archiving the target project evaluation and supervision violations, the target project name, the evaluation and supervision violation event behavior type, the evaluation and supervision violation event behavior time and the evaluation and supervision violation event description for tracing and dispatching the violations are recorded at the same time.
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