CN117909819A

CN117909819A - Trusted adaptation method, device, equipment and medium of situation awareness system

Info

Publication number: CN117909819A
Application number: CN202410035190.4A
Authority: CN
Inventors: 方宏佳; 杨逸岳; 于廷文; 查晶; 梁誉; 陈海光; 周司维; 黄磊; 罗欢; 张丽娟; 李慧娟; 连晨
Original assignee: China Southern Power Grid Digital Platform Technology Guangdong Co ltd
Current assignee: China Southern Power Grid Digital Platform Technology Guangdong Co ltd
Priority date: 2024-01-09
Filing date: 2024-01-09
Publication date: 2024-04-19

Abstract

The application relates to a credible adaptation method, a device, equipment and a medium of a situation awareness system, wherein the credible adaptation method of the situation awareness system comprises the steps of acquiring trigger information of an initial data source, generating corresponding awareness instructions, acquiring acquisition information of other visual angle data sources, integrating the acquisition information through a situation awareness model, generating corresponding situation assessment results, pushing a corresponding first decision window, generating corresponding priority data samples according to the first decision window, determining the priority data source when the same trigger information of the initial data source is acquired, generating credible weight values according to the trigger times of the trigger information, pushing a corresponding second decision window, and generating corresponding electric control component execution instructions; by introducing priority data samples and a credibility weight mechanism, the system realizes the dynamic credibility evaluation of each data source, and overcomes the defects that the traditional system is easy to be misled and generates inaccurate monitoring decisions when facing multi-source information.

Description

Trusted adaptation method, device, equipment and medium of situation awareness system

Technical Field

The present invention relates to the field of trusted adaptation of situation awareness systems, and in particular, to a method, an apparatus, a device, and a medium for trusted adaptation of a situation awareness system.

Background

At present, a single monitoring system of the whole room is poor in monitoring effect, mainly shows that a single sensor is limited in coverage range, and is difficult to comprehensively capture complex environmental information in the room. Due to the singleness of the system, the diversified monitoring requirements cannot be effectively processed, so that obvious defects exist in the aspects of abnormal event identification, real-time feedback and overall perception;

The existing situation awareness system is adopted by the monitoring system of the whole room to meet the monitoring requirements of effective treatment diversity, but the existing situation awareness system lacks an evaluation mechanism for the credibility of the data sources, so that the system cannot accurately judge the reliability of different data sources, the system is easy to be misled or generate inaccurate monitoring decisions when processing multi-source information, the intelligent perception and decision level of the system are reduced, and the inaccurate monitoring decisions can directly cause the problem of poor user experience.

Disclosure of Invention

In order to solve the problem that the existing situation awareness system lacks an evaluation mechanism for the credibility of a data source so that the system is easy to be misled or inaccurate monitoring decision is generated when the multi-source information is processed, the application provides a credible adaptation method, device, equipment and medium of the situation awareness system.

The first object of the present application is achieved by the following technical solutions:

A trusted adaptation method of a situational awareness system, the trusted adaptation method of a situational awareness system comprising:

Acquiring trigger information of an initial data source, and generating a corresponding perception instruction, wherein the trigger information comprises active trigger information of a user and passive trigger information of an environment;

Acquiring acquisition information of other visual angle data sources according to the perception instruction, integrating the acquisition information through a situation perception model, and generating a corresponding situation assessment result;

Pushing a corresponding first decision window according to the situation assessment result, wherein the first decision window is used for responding to the decision of a user and generating corresponding decision information, and the decision information at least comprises situation assessment result feedback information and data source priority information; generating a corresponding electric control component execution instruction according to the situation assessment result feedback information;

Generating a corresponding priority data sample according to the data source priority information, determining a priority data source according to the priority data sample when the trigger information with the same initial data source is acquired, generating a trusted weight value according to the triggering times of the trigger information, and pushing a corresponding second decision window according to the trusted weight value and the acquisition information of the priority data source to generate a corresponding electric control component execution instruction.

By adopting the technical scheme, the credibility adaptation method has remarkable effect on solving the difficult problem of the traditional monitoring system in credibility evaluation of different data sources. By introducing priority data samples and a credibility weight mechanism, the system realizes the dynamic credibility evaluation of each data source. The priority data samples are helpful for determining key data sources, and the credible weight values are dynamically generated according to the triggering times of the triggering information, so that the reliability of different data sources is accurately measured. The technical effect overcomes the defect that the traditional system is easy to be misled and generates inaccurate monitoring decisions when facing multi-source information. The reliability of the data source is judged through a flexible mechanism, the system is more robust when processing multi-source information, and an effective solution is provided for improving the accuracy and the reliability of monitoring decisions.

The present application may be further configured in a preferred example to: generating a corresponding priority data sample according to the data source priority information, determining a priority data source according to the priority data sample when the trigger information with the same initial data source is acquired, generating a trusted weight value according to the triggering times of the trigger information, and pushing a corresponding second decision window according to the trusted weight value and the acquisition information of the priority data source to generate a corresponding execution instruction of an electric control component, wherein the method comprises the following steps:

determining a key field of the data source priority information, matching the key field with an identification field of the data source, and generating a corresponding priority data sample;

When the trigger information with the same initial data source is obtained, determining a priority data source according to the priority data sample;

Generating a trusted weight value according to the triggering times of the triggering information and the decision information of the first decision window or the decision information of the second decision window;

Judging whether the credible weight value is larger than a first preset threshold value, if not, acquiring acquisition information of other visual angle data sources with preset quantity, acquiring acquisition information of priority data sources, generating a corresponding situation assessment result through a situation perception model, pushing a corresponding second decision window, and generating a corresponding electric control component execution instruction;

if yes, judging whether the credible weight value is larger than a second preset threshold value, if not, acquiring acquisition information of a priority data source, generating a corresponding situation assessment result through a situation perception model, pushing a corresponding second decision window, and generating a corresponding electric control component execution instruction;

if yes, judging whether the credible weight value is larger than a third preset threshold value, if not, directly pushing a corresponding second decision window, and generating a corresponding electric control component execution instruction;

if yes, directly generating a corresponding electric control component execution instruction.

By adopting the technical scheme, the key field of the data source priority information is determined, and the key field and the identification field of the data source are matched, so that the corresponding priority data sample is generated. When the initial data sources of the same trigger information are acquired, the priority data sources are rapidly determined according to the priority data samples, and then a credible weight value is generated according to the trigger times of the trigger information and the decision information of the first decision window or the second decision window. By setting a preset threshold, the system intelligently judges the magnitude of the credible weight value, and then different decision paths are adopted. Under the condition of low credibility, acquiring acquisition information of other view angle data sources and priority data sources, generating a situation assessment result through a situation awareness model, and pushing a second decision window to generate a corresponding electric control component execution instruction. Under the condition of high credibility, corresponding electric control component executing instructions are generated directly according to the acquisition information of the priority data source or by pushing only the second decision window. The technical effect emphasizes the dynamic adaptation of the credibility of the data source, and improves the intelligent perception and decision level of the system in different situations, thereby improving the accuracy and the practicability of the monitoring system.

The present application may be further configured in a preferred example to: in the step of generating the trusted weight value according to the triggering times of the triggering information, the method specifically comprises the following steps:

the trusted weight value satisfies the following formula:

Wherein A is a trusted weight value, n is the triggering times of triggering information, S is the number value of all data sources, W is a constant, C is a constant larger than W, the difference between C and W is not more than 1, and f is the decision times of opposite decision results generated by a user through a second decision window.

By adopting the technical scheme, the credible weight value can be dynamically generated according to the decision times of the opposite decision result generated by the user through the second decision window. Through the mechanism, the system can flexibly capture feedback opinions of the user, so that the credible weight value is weakened. Specifically, when the user makes an opposite decision, the system correspondingly reduces the magnitude of the trusted weight value, reflecting that the trust level of the user's decision on the system is reduced. The design enhances the sensitivity of the system to user feedback, and is beneficial to more accurately adjusting the credible weight value, so that the adaptability and accuracy of the system to situation awareness and decision making in a complex environment are improved.

The present application may be further configured in a preferred example to: the step of acquiring triggering information of an initial data source and generating a corresponding perception instruction, wherein the triggering information comprises active triggering information of a user and passive triggering information of an environment comprises the following steps:

Acquiring trigger information of an initial data source, wherein the trigger information comprises active trigger information of a user and passive trigger information of an environment; analyzing active triggering information or/and environment passive triggering information of a user, extracting keyword information, and matching identity identification information of other visual angle data sources associated with the keyword information to generate perception instructions for controlling the other visual angle data sources associated with the keyword information.

By adopting the technical scheme, aiming at active triggering information of a user or passive triggering information of an environment, the system analyzes and extracts keyword information in the active triggering information or the passive triggering information of the environment. The keyword information is used to match identification information of other perspective data sources associated therewith to generate perception instructions for controlling the other perspective data sources. Through the flow, the system realizes intelligent processing of the trigger information, maps active trigger of a user or passive trigger of an environment into specific instructions for other data sources, and realizes targeted perception of multi-source information. The method is beneficial to improving the flexibility and the intelligence of the system, so that the system can be more accurately adapted to different trigger situations, and the effectiveness of the system in situation awareness is enhanced.

The present application may be further configured in a preferred example to: acquiring acquisition information of other visual angle data sources according to the perception instruction, integrating the acquisition information through a situation perception model, and generating a corresponding situation assessment result, wherein the method comprises the following steps of:

acquiring acquisition information of corresponding other visual angle data sources according to the perception instruction;

Judging whether repeated items exist in the acquired information, if so, deleting any repeated item;

if not, judging whether the acquired information has missing items, if so, filling the average value and/or the median into the missing items;

If not, at least standardizing the scale range, the date and time format, the text field and the data type of the acquired information;

And extracting key features of the standardized acquired information, matching and substituting the key features into a corresponding situation awareness model, wherein the situation awareness model comprises a machine learning model, a deep learning model and a space-time data analysis model, and generating a corresponding situation assessment result.

By adopting the technical scheme, the system performs a series of preprocessing operations to ensure the quality and consistency of acquired information, so that the system can effectively apply different situation awareness models while uniformly processing multi-source data, the understanding and predicting capability of the system on complex information is improved, and the efficiency of the whole situation awareness system is enhanced.

The present application may be further configured in a preferred example to: the step of pushing a corresponding first decision window according to the situation assessment result, wherein the first decision window is used for responding to a user's decision and generating corresponding decision information, and the decision information at least comprises situation assessment result feedback information and data source priority information, and comprises the following steps:

Generating a corresponding prediction chart model according to the situation assessment result;

calculating prediction result data according to the prediction chart model;

pushing a first decision window, wherein the first decision window is used for visualizing and differentially labeling the predicted result data;

And detecting interaction coordinates of the first decision window, and generating corresponding decision information.

By adopting the technical scheme, the system can intuitively present a complex situation assessment result through the predictive graph model, and realize more personalized situation understanding and decision through user interaction. In the whole process, the system fully utilizes a visualization means, improves the perception and understanding capability of a user on information, and simultaneously generates richer decision information through interaction of the user and a first decision window so as to provide more accurate basis for subsequent decisions.

The second object of the present application is achieved by the following technical solutions: a trusted adaptation device of a situational awareness system, the trusted adaptation device of a situational awareness system comprising:

The first generation module is used for acquiring triggering information of an initial data source and generating a corresponding perception instruction, wherein the triggering information comprises active triggering information of a user and passive triggering information of an environment;

the second generation module is used for acquiring acquisition information of other visual angle data sources according to the perception instruction, integrating the acquisition information through a situation perception model and generating a corresponding situation assessment result;

The pushing module is used for pushing a corresponding first decision window according to the situation assessment result, wherein the first decision window is used for responding to the decision of a user and generating corresponding decision information, and the decision information at least comprises situation assessment result feedback information and data source priority information;

the third generation module is used for generating a corresponding electric control component execution instruction according to the situation assessment result feedback information;

And the fourth generation module is used for generating a corresponding priority data sample according to the data source priority information, determining a priority data source according to the priority data sample when the triggering information with the same initial data source is acquired, generating a trusted weight value according to the triggering times of the triggering information, and pushing a corresponding second decision window according to the trusted weight value and the acquisition information of the priority data source so as to generate a corresponding electric control component execution instruction.

The third object of the present application is achieved by the following technical solutions:

A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the trusted adaptation method of a situational awareness system as described above when executing the computer program.

The fourth object of the present application is achieved by the following technical solutions:

a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of a trusted adaptation method of a situation awareness system as described above.

In summary, the present application includes at least one of the following beneficial technical effects:

1. By introducing the credibility adaption method, the scheme successfully solves the problem that the traditional monitoring system has difficulty in evaluating credibility of different data sources. The introduction of priority data samples helps the system to define key data sources, and the dynamic generation of the trusted weight values is based on user feedback and the triggering times of triggering information to more accurately quantify the trust level of each data source. The integration effectively improves the flexibility of the system on multi-source information, so that the system can be more intelligently adapted to different monitoring scenes. Meanwhile, the introduction of the credibility evaluation enhances the sensitivity of the system to the credibility of the data, thereby improving the accuracy and the instantaneity of the monitoring decision. The integrated design enables the system to be more robust when facing complex and dynamic monitoring environments, and provides more reliable and reliable monitoring decision support for users;

2. Through dynamically generating the perception instruction, the system can adaptively guide the behaviors of other visual angle data sources according to different trigger information, and personalized control of the data acquisition process is realized. Secondly, through intelligent preprocessing step, the system can carry out effective data cleaning and standardization before information integration, and the quality and consistency of data are improved. Most importantly, by introducing multiple situation awareness models, the system not only can adapt to different types of data, but also can more comprehensively understand the association between the data, and improves the awareness and understanding level of complex environments. The comprehensive processing method ensures that the system is more flexible and has strong adaptability, and provides more accurate and comprehensive situation awareness support for users;

3. Visual interaction design of the prediction chart model and the first decision window is introduced, so that visual presentation of situation assessment results and effective interaction between a user and the system are successfully realized. The design enables a user to more intuitively understand and participate in the decision making process through the visual interface, and improves the user friendliness and practicability of the system.

Drawings

FIG. 1 is a flow chart of a trusted adaptation method of a situation awareness system in an embodiment of the application.

FIG. 2 is a flowchart of the implementation of step S50 in a trusted adaptation method of a situation awareness system in an embodiment of the present application;

FIG. 3 is a flowchart of the implementation of step S10 in the trusted adaptation method of the situation awareness system in one embodiment of the present application;

FIG. 4 is a flowchart of another implementation of step S20 in the trusted adaptation method of the situation awareness system in one embodiment of the present application;

FIG. 5 is a flowchart of the implementation of step S30 in the trusted adaptation method of the situation awareness system in one embodiment of the present application;

FIG. 6 is a schematic block diagram of a trusted adaptation device of a situation awareness system in accordance with an embodiment of the present application;

Fig. 7 is a schematic diagram of an apparatus in an embodiment of the application.

Detailed Description

The present application will be described in further detail with reference to the accompanying drawings.

In an embodiment, as shown in fig. 1, the application discloses a trusted adaptation method of a situation awareness system, which specifically comprises the following steps:

s10, acquiring triggering information of an initial data source, and generating a corresponding perception instruction, wherein the triggering information comprises active triggering information of a user and passive triggering information of an environment;

In this embodiment, the trigger information includes active trigger of the user, which reflects subjective needs and intention of the user, and passive trigger of the environment, which is the perception of the environment change by the system. By integrating the two types of trigger information, the system can comprehensively know the change conditions of the user demands and the surrounding environment, so that corresponding perception instructions are generated, and targeted guidance is provided for subsequent data processing and decision making.

Specifically, the initial data source refers to a starting point of system perception and monitoring, including active triggering information and passive environment triggering information of a user, which are starting points of system acquisition information, the initial data source may be a certain camera, or a network engine for searching by the user, the perception instruction is an instruction dynamically generated according to the triggering information of the initial data source, the instruction is used for guiding the behavior of other visual angle data sources to realize personalized control and intelligent perception of the system on multi-source information, the active user triggering information may be an operation intentionally initiated by the user based on requirements, interests or targets, the purpose is to acquire specific information or guide the system to perform related perception and decision, the passive environment triggering information is the perception of the system on the change of the surrounding environment, and may be derived from the change of sensor data, external events or other environmental conditions, the purpose is to capture the change of the environmental state and trigger corresponding perception and response operation, for example, when the user inputs a field to search or the room temperature of a voice question room, the system generates a perception instruction for controlling the detection of the data source.

S20, acquiring acquisition information of other visual angle data sources according to a perception instruction, integrating the acquisition information through a situation perception model, and generating a corresponding situation assessment result;

In the embodiment, after acquiring the acquired information of other visual angle data sources, the system integrates multiple situation awareness models, so that the acquired information can be more comprehensively and accurately understood, a more refined situation assessment result is generated, and the awareness and understanding level of the complex environment is improved;

Specifically, the other view angle data sources refer to a plurality of independent data sources which are additionally covered by the system except the initial data source and are used for providing information of different view angles of the same event or situation, so that comprehensive perception of complex situations is realized, the situation perception model is an intelligent model comprehensively applying technologies such as machine learning, deep learning, space-time data analysis and the like and is used for carrying out integrated analysis on multi-source data, so that comprehensive evaluation results of the complex situations are generated, the situation evaluation results are comprehensive evaluation of the current event or situation obtained by the system after comprehensive processing and intelligent analysis, and comprehensive information of the multi-source data is covered.

S30, pushing a corresponding first decision window according to the situation assessment result, wherein the first decision window is used for responding to the decision of a user and generating corresponding decision information, and the decision information at least comprises situation assessment result feedback information and data source priority information; in this embodiment, based on the generated situation assessment result, the system pushes a first decision window, which is aimed at responding to the decision requirement of the user. The window not only provides real-time visual presentation of the assessment results, but also generates comprehensive decision information including situation assessment result feedback information and data source priority information. The user can get in depth about the current situation through interaction with the first decision window, thereby making decisions and action plans more effectively. The whole process realizes the efficient interaction between the user and the system through visual interface design and comprehensive decision information feedback, and improves the real-time performance and user experience of the decision;

Specifically, when the situation evaluation result is generated, the first decision window may be visualized, and a chart model such as a line graph, a bar chart and the like may be selectively used for displaying, at this time, the first decision window may have at least keys of "agree", "anti" and "select optimal observation angle", through which the system may recognize subjective consciousness of the user, represent the thought decision of the user, and when the user responds to "select optimal observation angle", the system may push all relevant data sources to the user for observation, for example, when the user inputs a field to search or a voice ask room temperature, after the system generates a sensing instruction for controlling the data source to perform detection, the system may push temperature detection in a bedroom, temperature detection in a living room, temperature detection in a kitchen and the like, and if the user wants to acquire temperature detection data in the bedroom, the system may directly judge that the temperature detection data in the corresponding bedroom can be pushed to the user according to the assigned weight value as long as the user inquires the room temperature.

S40, feeding back information according to situation assessment results, and generating corresponding electric control component execution instructions;

In this embodiment, this means that the system recognizes the possible risk, abnormality or condition requiring attention by analyzing the result of situation assessment, and accordingly triggers the generation of an electronic control unit execution instruction to timely alert or prompt the user or related personnel to support rapid decision and action

Specifically, the situation assessment result feedback information is feedback generated by the system based on comprehensive analysis of multi-source data, provides details and key information for comprehensive assessment of the current event or situation, provides valuable guidance for user decision, and the execution instruction of the electric control component refers to corresponding operation or action of the specific electric control component in the electronic control system according to the instruction issued by the system or the user. This may involve adjusting voltage, controlling current, changing device state, etc. to achieve the function of the system or the needs of the user.

S50, generating a corresponding priority data sample according to the priority information of the data source, determining the priority data source according to the priority data sample when the trigger information of the initial data source is the same, generating a credible weight value according to the triggering times of the trigger information, and pushing a corresponding second decision window according to the credible weight value and the acquisition information of the priority data source to generate a corresponding electric control component execution instruction.

In this embodiment, by generating priority data samples according to the data source priority information, the system can determine the priority data source when acquiring the same trigger information as the initial data source. Based on the triggering times of the triggering information, the system generates corresponding credibility weight values for quantifying the trust level of the data source. By combining the credible weight value and the acquisition information of the priority data source, the system pushes a second decision window which is used for generating a corresponding electric control component execution instruction, so that the credibility and the priority of the data source are more accurately considered in the decision process, and the response capability of the system to complex situations is improved;

Specifically, for example, when the room temperature is too high or too low, a certain warning effect can be generated, for example, when the camera monitors that a person arrives at a gate, the system pushes image data collected by the camera to a user through the first decision window, in addition, the camera has a plurality of view angles, such as a cat eye view angle, a door Liang Shijiao, a fingerprint data source and the like, the user subjectively judges that the person is a friend of the user, agrees to let the person enter and exit and select an optimal observation angle to observe the person, if the fingerprint data source is the optimal observation angle, the system directly pushes the fingerprint data to the user next time, the gate is opened after the user agrees, if the agreeing times are more, the system can directly open the gate without agreeing by the user, and meanwhile, the user can actively update the trusted weight value.

More specifically, the credibility adaptation method achieves a remarkable effect on the problem of difficulty in evaluating credibility of different data sources of a traditional monitoring system. By introducing priority data samples and a credibility weight mechanism, the system realizes the dynamic credibility evaluation of each data source. The priority data samples are helpful for determining key data sources, and the credible weight values are dynamically generated according to the triggering times of the triggering information, so that the reliability of different data sources is accurately measured. The technical effect overcomes the defect that the traditional system is easy to be misled and generates inaccurate monitoring decisions when facing multi-source information. The reliability of the data source is judged through a flexible mechanism, the system is more robust when processing multi-source information, and an effective solution is provided for improving the accuracy and the reliability of monitoring decisions.

In an embodiment, as shown in fig. 2, in step S50, that is, according to the data source priority information, a corresponding priority data sample is generated, when the trigger information that is the same as the initial data source is obtained, a priority data source is determined according to the priority data sample, a trusted weight value is generated according to the number of triggers of the trigger information, and according to the trusted weight value and the collection information of the priority data source, a corresponding second decision window is pushed to generate a corresponding execution instruction of the electronic control unit, which specifically includes:

s501, determining a key field of the data source priority information, matching the key field with an identification field of the data source, and generating a corresponding priority data sample;

in this embodiment, determining key fields of data source priority information is a critical step in the system that involves identifying core elements that affect priority among multiple data sources. The system establishes corresponding priority data samples by definitely defining key fields and then matching the fields with the identification fields of each data source, specifically, the identification fields are information elements for uniquely identifying and distinguishing different data sources, and each data source can be accurately identified and associated by the field system, so that the corresponding relation among the data sources is established.

S502, when the trigger information with the same initial data source is obtained, determining a priority data source according to the priority data sample;

in this embodiment, the process identifies data sources with higher priority based on the comparison and matching of the key fields, so as to ensure that the specific data sources are more emphasized or prioritized in the decision process, and improve the processing accuracy of the system on the related information and the rationality of the priority;

In particular, priority data sources are data sources that are used in a system by pre-established priority data samples to identify data sources that have higher weights or priorities under specific circumstances to more specifically treat and utilize those specific data sources in the decision making process.

S503, generating a credible weight value according to the triggering times of the triggering information and the decision information of the first decision window or the decision information of the second decision window;

In this embodiment, the frequency of the information and the feedback of the early decision window are triggered to quantify the trust level of the data source, so that the system can more intelligently adjust the trust degree of different information;

Specifically, the number of triggers refers to the frequency at which specific trigger information is excited or occurs within a certain time, reflecting the number of occurrences of the information. Decision information refers to decision results in the first or second decision window, including analysis and advice of the current situation by the system for guiding further actions by the user or the system.

S504, judging whether the credible weight value is larger than a first preset threshold value, if not, acquiring acquisition information of other preset number of view angle data sources, acquiring acquisition information of priority data sources, generating a corresponding situation assessment result through a situation perception model, pushing a corresponding second decision window, and generating a corresponding electric control component execution instruction;

in this embodiment, when the system determines whether the trusted weight value exceeds the first preset threshold, if the trusted weight value does not reach the first preset threshold, the system may acquire the acquired information of the other view angle data sources in a preset number, and acquire the acquired information of the priority data source at the same time. By applying the situation awareness model, the system generates a corresponding situation assessment result and pushes the situation assessment result to a second decision window. In the window, the system generates a corresponding electric control part execution instruction, so that a user can more comprehensively understand the current situation and provide detailed information and warning for decision making. The process is helpful to improve the accuracy and timeliness of decision making by integrating more data source information when the system detects lower credibility;

Specifically, the section is a first-stage trust level of the trusted weight value, the first preset threshold is a trust threshold set in advance by the system and is used for judging whether the trusted weight value reaches the trust level recognized by the system, and the second decision window is an interface for decision generated according to new data source information and situation assessment results after the first decision window, so that more detailed and deep information is provided to support a user to make a more comprehensive decision.

S505, if so, judging whether the credible weight value is larger than a second preset threshold value, if not, acquiring acquisition information of a priority data source, generating a corresponding situation assessment result through a situation perception model, pushing a corresponding second decision window, and generating a corresponding electric control component execution instruction;

in this embodiment, the second level of trust of the trusted weight value is a second preset threshold, which is a trust threshold set in advance by the system, and the trust level can be evaluated without matching with the collected information of the other view data sources.

S506, if so, judging whether the credible weight value is larger than a third preset threshold value, and if not, directly pushing a corresponding second decision window to generate a corresponding electric control component execution instruction;

in this embodiment, the third level of trust of the trusted weight value is a third preset threshold value, which is a trust threshold value set in advance by the system, and the second decision window can be directly pushed at the trust level.

S507, if yes, directly generating a corresponding electric control component execution instruction.

In this embodiment, the fourth level of trust of the trusted weight value is a level of trust at which a corresponding execution instruction of the electronic control unit can be generated, where the preset threshold is adjustable, and likewise the level of trust of the trusted weight value is adjustable.

More specifically, a key field of the data source priority information is determined, and the key field and an identification field of the data source are matched, thereby generating a corresponding priority data sample. When the initial data sources of the same trigger information are acquired, the priority data sources are rapidly determined according to the priority data samples, and then a credible weight value is generated according to the trigger times of the trigger information and the decision information of the first decision window or the second decision window. By setting a preset threshold, the system intelligently judges the magnitude of the credible weight value, and then different decision paths are adopted. Under the condition of low credibility, acquiring acquisition information of other view angle data sources and priority data sources, generating a situation assessment result through a situation awareness model, and pushing a second decision window to generate a corresponding electric control component execution instruction. Under the condition of high credibility, corresponding electric control component executing instructions are generated directly according to the acquisition information of the priority data source or by pushing only the second decision window. The technical effect emphasizes the dynamic adaptation of the credibility of the data source, and improves the intelligent perception and decision level of the system in different situations, thereby improving the accuracy and the practicability of the monitoring system.

In one embodiment, in step S503, that is, in the step of generating the trusted weight value according to the triggering number of the triggering information, specifically:

The trusted weight value satisfies the following formula:

In this embodiment, when n is 1 and f is 0, A isWhen n is 2 and f is 0, A is/>When n is 2 and f is 1, A isBecause/>Is a value less than 1, so the confidence weight value must be reduced whenever the user makes an opposite decision.

Specifically, the trusted weight value can be dynamically generated according to the number of decisions of the opposite decision result generated by the user through the second decision window. Through the mechanism, the system can flexibly capture feedback opinions of the user, so that the credible weight value is weakened. Specifically, when the user makes an opposite decision, the system correspondingly reduces the magnitude of the trusted weight value, reflecting that the trust level of the user's decision on the system is reduced. The design enhances the sensitivity of the system to user feedback, and is beneficial to more accurately adjusting the credible weight value, so that the adaptability and accuracy of the system to situation awareness and decision making in a complex environment are improved.

In one embodiment, as shown in fig. 3, in step S10, trigger information of an initial data source is acquired, and a corresponding sensing instruction is generated, where the trigger information includes active trigger information of a user and passive trigger information of an environment, and the steps specifically include: s101, acquiring trigger information of an initial data source, wherein the trigger information comprises active trigger information of a user and passive trigger information of an environment; in this embodiment, the trigger information of the initial data source is obtained, which covers the interaction between the user and the system through the active operation and the passive event spontaneously occurring in the environment, so that the system can fully sense the user demand and the environment change, and a foundation is provided for the subsequent data processing and decision.

S102, analyzing the active triggering information of the user or/and the passive triggering information of the environment, extracting keyword information, and matching the identity recognition information of other visual angle data sources associated with the keyword information to generate sensing instructions for controlling the other visual angle data sources associated with the keyword information.

In this embodiment, when analyzing the active trigger information of the user or the passive trigger information of the environment, the system matches the keyword information with the identity identification information of the other view angle data sources by extracting the keyword information. This process generates sensory instructions for controlling other perspective data sources associated with the keyword information, enabling the system to specifically collect and process specific information to more effectively meet user needs or respond to environmental changes.

Specifically, aiming at active triggering information of a user or passive triggering information of an environment, the system analyzes and extracts keyword information in the active triggering information or the passive triggering information of the environment. The keyword information is used to match identification information of other perspective data sources associated therewith to generate perception instructions for controlling the other perspective data sources. Through the flow, the system realizes intelligent processing of the trigger information, maps active trigger of a user or passive trigger of an environment into specific instructions for other data sources, and realizes targeted perception of multi-source information. The method is beneficial to improving the flexibility and the intelligence of the system, so that the system can be more accurately adapted to different trigger situations, and the effectiveness of the system in situation awareness is enhanced.

In an embodiment, as shown in fig. 4, in step S20, that is, acquiring acquisition information of other view angle data sources according to a sensing instruction, integrating the acquisition information through a situation sensing model, and generating a corresponding situation evaluation result, the method specifically includes:

S201, acquiring acquisition information of other corresponding visual angle data sources according to a perception instruction;

In this embodiment, according to the perception instruction, the system acquires acquisition information of other view angle data sources associated with the perception instruction. This means that the system gathers information about tasks or demands from multiple data sources as defined in the sense instructions to obtain a more comprehensive, multi-angle view for subsequent analysis, processing or decision making processes.

S202, judging whether repeated items exist in the acquired information, if so, deleting any repeated item;

In this embodiment, when determining whether a duplicate exists in the acquired information, the system checks the acquired information, and if the same data record or duplicate exists, the system performs a delete operation, retaining any duplicate therein. The method is helpful for reducing data redundancy, ensuring that the system is not affected by repeated data when analyzing and processing information, and improving the accuracy and efficiency of the data.

S203, if not, judging whether the acquired information has missing items, if so, filling the average value and/or the median into the missing items;

In this embodiment, if no duplicate items are found in the collected information in the determination, the system will further check whether there are missing items. If there are missing data items, the system will take action, typically by filling in the mean and/or median, to supplement the integrity of the corresponding missing items. This helps to preserve the integrity of the data, enabling the system to more accurately use the complete data set in subsequent analysis and processing.

S204, if not, at least standardizing the scale range, the date and time format, the text field and the data type of the acquired information;

In this embodiment, if no duplicate or missing items are found in the determination for the collected information, the system will perform a normalization operation to ensure that the collected information remains consistent in terms of scale range, date and time format, text fields, and data type. Normalization helps to improve the consistency and comparability of the data, ensuring that the system can efficiently manipulate and understand the data in subsequent processing and analysis.

S205, extracting key features of the standardized acquired information, matching and substituting the key features into a corresponding situation awareness model, wherein the situation awareness model comprises a machine learning model, a deep learning model and a space-time data analysis model, and generating a corresponding situation assessment result.

In this embodiment, after normalization, the system extracts key features of the acquired information and matches these features with the corresponding situational awareness model. These models may include machine learning models, deep learning models, spatiotemporal data analysis models, and the like. By substituting these models, the system is able to perform advanced data analysis and processing to generate corresponding situation assessment results. This process helps the system to understand the current context more deeply, providing more valuable information for subsequent decisions.

Specifically, after acquiring the acquired information of other view data sources, the system integrates multiple situation awareness models, wherein the models can comprise a machine learning model, a deep learning model, a time-space data analysis model and the like. The machine learning model can predict and classify future situations by learning patterns and rules of historical data. The deep learning model performs feature extraction and learning through a multi-level neural network structure, and models complex data relations. The space-time data analysis model focuses on processing space-time variation relations, and considers time sequence and geospatial information of data. The comprehensive application of the models enables the system to understand the acquired information more comprehensively and accurately, generates more refined situation assessment results, and improves the perception and understanding level of the complex environment.

More specifically, the system performs a series of preprocessing operations to ensure the quality and consistency of the acquired information, so that the system can effectively apply different situation awareness models while uniformly processing the multi-source data, and the understanding and predicting capability of the system on the complex information is improved, thereby enhancing the efficiency of the whole situation awareness system.

In one embodiment, as shown in fig. 5, in step S30, that is, according to the situation assessment result, a corresponding first decision window is pushed, where the first decision window is used to respond to a decision of a user and generate corresponding decision information, and the decision information includes at least situation assessment result feedback information and data source priority information, and the steps specifically include:

S301, generating a corresponding prediction chart model according to situation assessment results;

In this embodiment, according to the situation evaluation result, the system generates a corresponding prediction chart model. This means that the system builds a model that can predict future trends or key indicators by analyzing and evaluating current situation, as well as previous data trends. Such predictive graph models may provide information that is more insight to users or system decision makers, helping them make more informed decisions.

Specifically, the predictive graph model is a model that generates a visual graph to predict future development trends or key indexes by analyzing situation assessment results and historical data trends.

S302, calculating prediction result data according to a prediction chart model;

In this embodiment, according to the prediction graph model, the system performs a calculation operation to obtain prediction result data. The system generates a numerical prediction result for future development trend or key index by applying a prediction model and combining the current situation and the historical data trend, and provides useful information for users or system decisions.

Specifically, the predicted result data is a numerical value calculated according to a predicted graph model, and reflects the predicted estimation of the system on the future development trend or key index.

S303, pushing a first decision window, wherein the first decision window is used for visualizing and distinguishing and labeling predicted result data;

In this embodiment, pushing the first decision window is an operation of presenting the calculated predicted result data on the user interface, and the first decision window is specially designed for visualization, so that the user can intuitively observe and distinguish the labeled predicted result data. Through this window, the user can clearly understand the prediction of future development trend by the system in a chart, graph or other visual mode, and can better understand and make decisions.

S304, detecting interaction coordinates of the first decision window, and generating corresponding decision information.

In this embodiment, detecting the interaction coordinates of the first decision window refers to capturing, by the system, position information of the interaction between the user and the prediction result data by monitoring the interaction operation of the user on the visual interface, such as mouse clicking, scrolling or other interaction means. The system generates corresponding decision information from these interaction coordinates, which may include specific data points, trends, or other relevant information of interest to the user, providing the user with more insight into making more informed decisions.

More specifically, the system can intuitively present complex situation assessment results through a predictive graph model, and more personalized situation understanding and decision making are realized through user interaction. In the whole process, the system fully utilizes a visualization means, improves the perception and understanding capability of a user on information, and simultaneously generates richer decision information through interaction of the user and a first decision window so as to provide more accurate basis for subsequent decisions.

It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.

In an embodiment, a trusted adapting device of a situation awareness system is provided, where the trusted adapting device of the situation awareness system corresponds to the trusted adapting method of the situation awareness system in the above embodiment one by one. As shown in fig. 6, the trusted adapting device of the situation awareness system includes a first generating module, a second generating module, a pushing module, a third generating module and a fourth generating module. The functional modules are described in detail as follows:

the trusted adapting device of the situation awareness system comprises:

Optionally, the fourth generating module includes:

The matching unit is used for determining a key field of the data source priority information, matching the key field with an identification field of the data source and generating a corresponding priority data sample;

the determining unit is used for determining the priority data source according to the priority data sample when the trigger information with the same initial data source is acquired;

the first generation unit is used for generating a credible weight value according to the triggering times of the triggering information and the decision information of the first decision window or the decision information of the second decision window;

The first judging unit is used for judging whether the credible weight value is larger than a first preset threshold value, if not, acquiring acquisition information of other visual angle data sources with preset quantity and acquisition information of priority data sources, generating a corresponding situation assessment result through a situation awareness model, pushing a corresponding second decision window, and generating a corresponding electric control component execution instruction;

The second judging unit is used for judging whether the credible weight value is larger than a second preset threshold value or not if yes, acquiring the acquisition information of the priority data source, generating a corresponding situation assessment result through a situation perception model, pushing a corresponding second decision window and generating a corresponding electric control component execution instruction;

The third judging unit is used for judging whether the credible weight value is larger than a third preset threshold value or not if yes, directly pushing a corresponding second decision window to generate a corresponding electric control component executing instruction if not;

the second generating unit is used for directly generating a corresponding electric control component execution instruction if the first generating unit is used for directly generating the corresponding electric control component execution instruction;

optionally, the first generating module includes:

The first acquisition unit is used for acquiring trigger information of an initial data source, wherein the trigger information comprises active trigger information of a user and passive trigger information of an environment;

the analysis unit is used for analyzing the active triggering information of the user or/and the passive triggering information of the environment, extracting keyword information, and matching the identity recognition information of other visual angle data sources associated with the keyword information so as to generate a perception instruction for controlling the other visual angle data sources associated with the keyword information;

Optionally, the second generating module includes:

The second acquisition unit is used for acquiring acquisition information of other corresponding visual angle data sources according to the perception instruction;

A fourth judging unit, configured to judge whether the acquired information has a duplicate item, and if yes, delete any duplicate item;

A fifth judging unit, configured to judge whether the acquired information has a missing item if not, and if yes, fill the average value and/or the median into the missing item;

The normalization unit is used for normalizing at least the scale range, the date and time format, the text field and the data type of the acquired information if not;

the matching unit is used for extracting key features of the standardized acquired information, matching and substituting the key features into a corresponding situation awareness model, wherein the situation awareness model comprises a machine learning model, a deep learning model and a space-time data analysis model, and generating a corresponding situation assessment result;

Optionally, the pushing module includes:

the third generation unit is used for generating a corresponding prediction chart model according to the situation assessment result;

the calculating unit is used for calculating prediction result data according to the prediction chart model;

the pushing unit is used for pushing a first decision window, and the first decision window is used for visualizing and differentially labeling the predicted result data;

and the fourth generation unit is used for detecting the interaction coordinates of the first decision window and generating corresponding decision information.

For specific limitations on the trusted adaptation means of the situation awareness system, reference may be made to the above limitation on the trusted adaptation method of the situation awareness system, which is not described in detail here. The modules in the trusted adaptation device of the situation awareness system may be implemented in whole or in part by software, hardware and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 7. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements a method of trusted adaptation of a situational awareness system.

In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:

In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.

It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.

The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.

Claims

1. A trusted adaptation method of a situation awareness system, characterized in that the trusted adaptation method of a situation awareness system comprises:

2. The method according to claim 1, wherein the step of generating a corresponding priority data sample according to the data source priority information, determining a priority data source according to the priority data sample when the trigger information of the same initial data source is acquired, generating a trusted weight value according to the trigger number of the trigger information, pushing a corresponding second decision window according to the trusted weight value and the acquisition information of the priority data source, and generating a corresponding execution instruction of the electronic control unit comprises:

3. The method for trusted adaptation of a situation awareness system according to claim 2, wherein in the step of generating a trusted weight value according to the triggering times of the triggering information, specifically:

the trusted weight value satisfies the following formula:

4. The method for trusted adaptation of a situation awareness system according to claim 1, wherein the step of obtaining trigger information of an initial data source to generate a corresponding awareness instruction, the trigger information including active trigger information of a user and passive trigger information of an environment includes:

Acquiring trigger information of an initial data source, wherein the trigger information comprises active trigger information of a user and passive trigger information of an environment;

Analyzing active triggering information or/and environment passive triggering information of a user, extracting keyword information, and matching identity identification information of other visual angle data sources associated with the keyword information to generate perception instructions for controlling the other visual angle data sources associated with the keyword information.

5. The method for trusted adaptation of a situation awareness system according to claim 1, wherein the step of acquiring the acquired information of other view angle data sources according to the awareness instructions, integrating the acquired information through a situation awareness model, and generating a corresponding situation assessment result includes:

6. The method for trusted adaptation of a situation awareness system according to claim 1, wherein the step of pushing a corresponding first decision window according to the situation assessment result, where the first decision window is used to respond to a decision of a user and generate corresponding decision information, where the decision information includes at least situation assessment result feedback information and data source priority information includes: generating a corresponding prediction chart model according to the situation assessment result;

calculating prediction result data according to the prediction chart model;

7. A trusted adaptation device of a situational awareness system, characterized in that the trusted adaptation device of a situational awareness system comprises:

8. The trusted adaptation device of a situational awareness system of claim 7, wherein said fourth generating module comprises:

The first generation module includes:

The second generation module includes:

The pushing module comprises:

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the trusted adaptation method of a situation awareness system according to any of claims 1 to 6 when executing the computer program.

10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the trusted adaptation method of a situation awareness system according to any of claims 1 to 6.