Automatic hidden danger processing method based on artificial intelligence technology, OCR algorithm model and AI recognition

Abstract

The invention discloses an automatic hidden danger processing method based on an artificial intelligence technology, an OCR algorithm model and AI identification. The method comprises the following steps: Step 1, constructing an OCR algorithm model; step 2, development of an AI identification module; 3, a hidden danger processing mechanism is designed; and step 4, integrating and testing the system. Text information in the image is automatically processed and analyzed through an AI recognition technology, and then automatic recognition and processing of potential hazards are achieved. The OCR algorithm model is responsible for converting text information in an image into an editable and searchable digital format, and the artificial intelligence technology is responsible for deeply analyzing and understanding the information so as to find potential hidden dangers.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower plant filling assessment, and in particular to a method for automatically handling hidden dangers based on artificial intelligence technology combined with an OCR algorithm model and AI identification. Background Art

[0002] The commonly used hidden danger treatment methods in the existing industry are mainly the following: 1. Empirical method: By checking against relevant standards and regulations for hidden dangers, relying on the experience of personnel to conduct on-site observation, analysis and judgment, the accident hazards existing at the construction site are discovered. Although this method is simple and easy, it is limited by the experience level of personnel and may result in omissions or misjudgments.

[0003] 2. Analogy method: Use the same or similar information of engineering systems, working conditions, and labor safety and health statistics to make analogies, analyses, and evaluations to discover potential safety hazards in the system. This method is characterized by the ability to use existing experience to quickly identify potential hazards in similar scenarios, but it also requires rich experience and professional knowledge support.

[0004] 3. Safety checklist: The safety checklist method is a method of listing a series of items in a checklist for analysis to determine whether the status of the system and the site meets the safety requirements, discover the safety hazards in the system through inspection, and propose improvement measures. This method is easy to operate and the inspection items are clear, which helps to achieve the standardization and normalization of safety inspections. It is easy to distinguish responsibilities, and requirements for improvement measures can also be put forward and tested. However, the inspection results are easily affected by the experience, knowledge and skill level of the inspectors. Different inspectors have different understandings of the standards and experience levels, and have different judgments on the same hidden danger. If the inspectors are inexperienced or careless, some potential accident hazards may be missed, especially those deep-seated and hidden hazards. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a method for automatically handling hidden dangers based on artificial intelligence technology combined with OCR algorithm model and AI identification, aiming to solve the low efficiency and low accuracy of existing hidden danger detection technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for automatically handling hidden dangers based on artificial intelligence technology combined with OCR algorithm model fusion AI identification includes the following steps: Step 1: OCR algorithm model construction, including: Step 1.1, data preprocessing: clean and standardize the input data to improve the training efficiency and performance of the neural network; Data preprocessing achieves data standardization and data validity through data cleaning / data integration, including: Data cleaning: 1. Missing value processing: delete records with missing values, use mean, median, mode, etc. to fill missing values, or use regression, interpolation and other methods for prediction and filling.

[0007] 2. Outlier processing: Identify outliers through statistical methods, such as box plots, z-scores, etc., and decide whether to keep, delete or modify them.

[0008] 3. Noise data processing: Use binning, regression, clustering and other methods to smooth the data and reduce noise.

[0009] Data Integration: 1. Entity recognition: Identify and uniformly represent real-world entities from different data sources to solve problems such as homonyms and synonyms.

[0010] 2. Redundancy processing: Detect and delete duplicate records or attributes in the data to reduce data redundancy.

[0011] 3. Numerical conflict handling: Solve the problem of inconsistent values ​​in different data sources, such as unit conversion, data standardization, etc.; Step 1.2, using the hidden Markov model forward algorithm, backward algorithm and Viterbi algorithm propagation: the input data is propagated forward through the network layer by layer, and the output of each neuron is calculated; Step 1.3, calculate the loss: calculate the loss function value based on the result of the output layer and the true label; Step 1.4, construct a neural network based on the data obtained in Step 1.2 and Step 1.3; Step 2: AI recognition module development; including: Step 2.1. Develop a recognition module based on artificial intelligence technology to analyze and understand the text information recognized by OCR; Step 2.2, AI recognition; collect data related to the target recognition task through data collection and preprocessing, and perform preprocessing such as cleaning and labeling; perform feature extraction to extract useful feature information from the preprocessed data; use the labeled data to adjust the model parameters to optimize the recognition effect; tune the model according to the evaluation results, adjust the model structure, and optimize the algorithm parameters; deploy the trained model to the actual application scenario; Step 3: Design of hidden danger handling mechanism, including: hidden danger identification algorithm, handling process and notification mechanism.

[0012] Step 4: System integration and testing: Integrate the OCR algorithm model, AI recognition module and hidden danger handling mechanism into a system, and conduct comprehensive testing and optimization.

[0013] The forward algorithm of the hidden Markov model in Step 1.2 above includes: Initial value: α1(i) = πibi(o1), i = 1, 2, ..., N; Recursion: αt+1(i) = [∑j=1N αt(j)aji]bi(ot+1), i = 1, 2, ..., N; t = 1, 2, ..., T-1; Termination: P(O|λ) = ∑i=1N αT(i); Parameter explanation: Initialize the forward probability α1(i). For all states i, α1(i) = πibi(o1), where πi is the initial state probability and bi(o1) is the probability of observing o1 in state i. For t = 1, 2, ..., T-1, recursively calculate αt+1(i): \alpha_{t+1}(i) = \left[\sum_{j=1}^{N} \alpha_t(j) a_{ji}\right] b_i(o_{t+1}) \]; Among them, at(j) is the forward probability of state j at time t, aji is the transition probability of state j to state i, and bi(ot+1) is the probability of observing ot+1 in state i; Calculate the probability P(O|λ) of the observation sequence O under model λ: P(O|λ) = ∑i=1N αT(i); Among them, αT(i) is the forward probability of state i at time T, and N is the total number of states; πii is the initial state probability; α1(i) is the initial forward probability; at(j) is the forward probability of state j at time t, aji is the transition probability from state j to state i, bi(ot+1) is the probability of observing ot+1 in state i; P(O|λ) is the probability of calculating the observation sequence O under model λ; Among them, αT(i) is the forward probability of state i at time T, and N is the total number of states.

[0014] The backward algorithm of the hidden Markov model in Step 1.2 above includes: Baum-Welch algorithm, a type of EM algorithm): Initialize model parameters λ = (A, B, π); Iterate until convergence: Step E: Based on the current model parameters and the observation sequence O, calculate the joint probability γt(i, j) that the state it at each time t is qi and the state it+1 at time t+1 is qj and the probability ξt(i) that the state it is qi; Step M: Update the model parameters A, B, π according to the probability obtained in step E; Estimate the parameters of the hidden state sequence of the HMM given some or all of the observation sequence: (π): initial state probability; A state transition probability; B and observation probability.

[0015] The hidden Markov model Viterbi algorithm in Step 1.2 above includes: Initialization: δ1(i) = πibi(o1), ψ1(i) = 0, i = 1, 2, ..., N; Recursion: δt(j) = maxi δt-1(i)aijbj(ot), ψt(j) = arg maxi [δt-1(i)aij], j =1, 2, ..., N; t = 2, 3, ..., T; Termination: iT* = arg maxi [δT(i)]; Parameter analysis: Define δt(i) as the maximum probability that the hidden state is i and produces the observation sequence O_1, O_2, ..., O_t at time t, that is, \delta_t(i) = \max_{q_1, q_2, \ldots, q_{t-1}} P(q_1, q_2, \ldots,q_{t-1}, q_t = i, O_1, O_2, \ldots, O_t | \lambda) \] where λ is the model parameter of HMM, including the initial state probability, state transition probability and observation probability; Path backtracking: it* = ψt+1; Parameter analysis: Where a_{ij} is the probability of transitioning from state i to state j; b_j(O_t) is the probability that state j produces the observation value O_t; at the same time, the path information is saved; that is, ψ_t(j) is the previous optimal state of state j at time t.

[0016] The present invention provides a method for automatically handling hidden dangers based on artificial intelligence technology combined with an OCR algorithm model and AI recognition, which automatically processes and analyzes text information in an image through AI recognition technology, thereby realizing automatic recognition and processing of potential hidden dangers. The OCR algorithm model is responsible for converting the text information in the image into an editable and searchable digital format, while the artificial intelligence technology is responsible for in-depth analysis and understanding of this information to discover potential hidden dangers. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 It is a schematic diagram of a model of a neural network of the present invention; Figure 2 It is a schematic diagram of the hierarchical division of AI technology research and development in the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention clearer, the following content will systematically and completely describe the specific technical solutions of the present invention in combination with the drawings provided according to the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0019] A method for automatically handling hidden dangers based on artificial intelligence technology combined with OCR algorithm model fusion AI identification includes the following steps: Step 1: OCR algorithm model construction, including: Step 1.1, data preprocessing: clean and standardize the input data to improve the training efficiency and performance of the neural network; Data preprocessing achieves data standardization and data validity through data cleaning / data integration, including: Data cleaning: 1. Missing value processing: delete records with missing values, use mean, median, mode, etc. to fill missing values, or use regression, interpolation and other methods for prediction and filling.

[0020] 2. Outlier processing: Identify outliers through statistical methods, such as box plots, z-scores, etc., and decide whether to keep, delete or modify them.

[0021] 3. Noise data processing: Use binning, regression, clustering and other methods to smooth the data and reduce noise.

[0022] Data Integration: 1. Entity recognition: Identify and uniformly represent real-world entities from different data sources to solve problems such as homonyms and synonyms.

[0023] 2. Redundancy processing: Detect and delete duplicate records or attributes in the data to reduce data redundancy.

[0024] 3. Numerical conflict handling: Solve the problem of inconsistent values ​​in different data sources, such as unit conversion, data standardization, etc.; Step 1.2, using the hidden Markov model forward algorithm, backward algorithm and Viterbi algorithm propagation: the input data is propagated forward through the network layer by layer, and the output of each neuron is calculated; Step 1.3, calculate the loss: calculate the loss function value based on the result of the output layer and the true label; Step 1.4, construct a neural network based on the data obtained in Step 1.2 and Step 1.3; Step 2: AI recognition module development; including: Step 2.1. Develop a recognition module based on artificial intelligence technology to deeply analyze and understand the text information recognized by OCR. This module needs to have strong natural language processing capabilities and knowledge graph construction capabilities to achieve a comprehensive understanding and in-depth mining of text information. Combined with artificial intelligence technology, deeply analyze and understand the text information recognized by OCR to discover potential risks and anomalies. Step 2.2, AI recognition; analyze input data (such as images, sounds, text, etc.), automatically identify and classify target objects or information; collect data related to target recognition tasks through data acquisition and preprocessing, and perform preprocessing such as cleaning and labeling; perform feature extraction, extract useful feature information from preprocessed data; use labeled data to adjust model parameters to optimize recognition effect; tune the model according to the evaluation results, adjust the model structure, and optimize the algorithm parameters; deploy the trained model to actual application scenarios, such as mobile APP, web pages, servers, etc. Provide user-friendly interfaces and interactive methods to allow users to easily use AI recognition functions; Step 3: Design of hidden danger handling mechanism, including hidden danger identification algorithm, handling process and notification mechanism. Once the hidden danger is identified, the system can automatically trigger the corresponding handling mechanism to achieve rapid response and handling of the hidden danger.

[0025] The hazard identification algorithm is a comprehensive application of multiple technologies, aiming to automatically identify and evaluate potential safety hazards.

[0026] Scenario 1: Sort out the characteristics and patterns of hidden dangers based on a large amount of data. It can handle complex data relationships and improve the accuracy and efficiency of identification. It can be applied to various types of hidden danger identification tasks, such as construction safety hazards, equipment failure hazards, etc.

[0027] Scenario 2: Judge based on pre-defined rules and logic. Monitor the device status according to preset security rules and thresholds, and treat any abnormalities as hidden dangers.

[0028] Scenario 3: Identify hidden dangers based on multiple data sources (such as sensor data, image data, text data, etc.). At the construction site, multiple data sources such as video surveillance, sensor monitoring, and personnel behavior analysis can be combined to identify construction safety hazards through data fusion algorithms.

[0029] Step 4, system integration and testing: integrate the OCR algorithm model, AI recognition module and hidden danger handling mechanism, and conduct comprehensive testing and optimization. Once a hidden danger is identified, the system can automatically trigger the corresponding handling mechanism, such as alarm, recording, sending notifications, etc., to achieve rapid response and handling of hidden dangers. Ensure that the system can run stably and meet actual needs.

[0030] The forward algorithm of the hidden Markov model in Step 1.2 above includes: Initial value: α1(i) = πibi(o1), i = 1, 2, ..., N; Recursion: αt+1(i) = [∑j=1N αt(j)aji]bi(ot+1), i = 1, 2, ..., N; t = 1, 2, ..., T-1; Termination: P(O|λ) = ∑i=1N αT(i); Parameter explanation: Initialize the forward probability α1(i). For all states i, α1(i) = πibi(o1), where πi is the initial state probability and bi(o1) is the probability of observing o1 in state i. For t = 1, 2, ..., T-1, recursively calculate αt+1(i): \alpha_{t+1}(i) = \left[\sum_{j=1}^{N} \alpha_t(j) a_{ji}\right] b_i(o_{t+1}) \]; Among them, at(j) is the forward probability of state j at time t, aji is the transition probability of state j to state i, and bi(ot+1) is the probability of observing ot+1 in state i; Calculate the probability P(O|λ) of the observation sequence O under model λ: P(O|λ) = ∑i=1N αT(i); Among them, αT(i) is the forward probability of state i at time T, and N is the total number of states; πii is the initial state probability; α1(i) is the initial forward probability; at(j) is the forward probability of state j at time t, aji is the transition probability from state j to state i, bi(ot+1) is the probability of observing ot+1 in state i; P(O|λ) is the probability of calculating the observation sequence O under model λ; Among them, αT(i) is the forward probability of state i at time T, and N is the total number of states.

[0031] The backward algorithm of the hidden Markov model in Step 1.2 above includes: Baum-Welch algorithm, a type of EM algorithm): Initialize model parameters λ = (A, B, π); Iterate until convergence: Step E: Based on the current model parameters and the observation sequence O, calculate the joint probability γt(i, j) that the state it at each time t is qi and the state it+1 at time t+1 is qj and the probability ξt(i) that the state it is qi; Step M: Update the model parameters A, B, π according to the probability obtained in step E; Estimate the parameters of the hidden state sequence of the HMM given some or all of the observation sequence: (π): initial state probability; A state transition probability (A, usually expressed as a matrix); B and observation probability.

[0032] The hidden Markov model Viterbi algorithm in Step 1.2 above includes: Initialization: δ1(i) = πibi(o1), ψ1(i) = 0, i = 1, 2, ..., N; Recursion: δt(j) = maxi δt-1(i)aijbj(ot), ψt(j) = arg maxi [δt-1(i)aij], j =1, 2, ..., N; t = 2, 3, ..., T; Termination: iT* = arg maxi [δT(i)]; Parameter analysis: Define δt(i) as the maximum probability that the hidden state is i and produces the observation sequence O_1, O_2, ..., O_t at time t, that is, \delta_t(i) = \max_{q_1, q_2, \ldots, q_{t-1}} P(q_1, q_2, \ldots,q_{t-1}, q_t = i, O_1, O_2, \ldots, O_t | \lambda) \] where λ is the model parameter of HMM, including the initial state probability, state transition probability and observation probability; Path backtracking: it* = ψt+1; Parameter analysis: Where a_{ij} is the probability of transitioning from state i to state j; b_j(O_t) is the probability that state j produces the observation value O_t; at the same time, the path information is saved; that is, ψ_t(j) is the previous optimal state of state j at time t.

[0033] Example: 1. Environment Configuration Dataset preparation: Collect pictures and video data containing various hidden danger scenes, such as equipment failures in industrial production, safety hazards at construction sites, and violations on traffic roads.

[0034] Annotate the dataset to clearly identify the type, location, and severity of hazards in each image or video.

[0035] System deployment: Deploy the hidden danger identification system on the server or cloud platform, including the OCR algorithm module, AI recognition module, and decision-making and feedback module.

[0036] Configure the necessary hardware and software environment to ensure that the system can run stably.

[0037] Simulation tool selection: Select appropriate simulation software or platform, such as MATLAB, TensorFlow, etc., to simulate the data processing and recognition process in actual scenarios.

[0038] 2. Implementation Process Data preprocessing: The collected data sets are preprocessed, including image denoising, enhancement, grayscale, binarization and other operations to improve the recognition rate of the OCR algorithm.

[0039] Segment the preprocessed image and extract the areas that need OCR recognition (such as equipment numbers, safety logos, etc.).

[0040] OCR recognition: Use the OCR algorithm to perform text recognition on the segmented area and extract key information.

[0041] Compare the recognition results with the annotation information to evaluate the accuracy of OCR recognition.

[0042] AI recognition: The OCR recognition results and the original image are input into the AI ​​recognition module.

[0043] The AI ​​model uses deep learning algorithms to analyze images and identify potential hidden dangers.

[0044] Mark the recognition results and compare them with the annotation information to evaluate the accuracy of AI recognition.

[0045] Decision making and feedback: Based on the results of AI recognition, the system makes decisions, such as issuing alarms and generating reports.

[0046] Simulate feedback mechanisms to record key indicators such as system response time and alarm accuracy.

[0047] Iterative Optimization: Based on the experimental results, the OCR algorithm and AI recognition model are iteratively optimized to improve recognition accuracy and efficiency.

[0048] Repeat the above steps until the system performance reaches the expected target.

[0049] 3. Realize data and technical effects OCR recognition accuracy: Experimental data show that the OCR algorithm has a high recognition accuracy on pre-processed images, and the average recognition rate can reach more than 95%.

[0050] For text recognition in complex backgrounds, the recognition rate has also been significantly improved by optimizing algorithms and increasing training data.

[0051] AI recognition accuracy: The AI ​​recognition module can identify hidden dangers more accurately after combining the OCR recognition results and the original image information.

[0052] Experimental data show that the average accuracy of AI recognition can reach over 90%, especially in specific hidden danger scenarios, the recognition rate is even higher.

[0053] System response time: After receiving the image data, the system can quickly perform pre-processing, OCR recognition and AI recognition, and make decisions in a short time.

[0054] Test data show that the average response time of the system is less than 1 second, meeting the real-time requirements.

[0055] Comprehensive effect: By combining OCR algorithm with AI recognition technology, the hidden danger identification system can significantly improve the efficiency and accuracy of hidden danger identification.

[0056] The system can automatically process large amounts of image data, issue alarms and generate reports in a timely manner, providing strong protection for the company's safe production.

[0057] At the same time, the system has good adaptability and scalability, and can be customized and optimized according to different scenarios and needs.

Claims

1. A method for automatically handling hidden dangers based on artificial intelligence technology combined with OCR algorithm model fusion AI identification, characterized in that: The following steps are involved: Step 1: OCR algorithm model construction; Step 2. Development of AI recognition module; Step 3: Design of hidden danger handling mechanism, including: hidden danger identification algorithm, handling process and notification mechanism. Step 4: System integration and testing; The OCR algorithm model, AI recognition module and hidden danger handling mechanism are systematically integrated and comprehensively tested and optimized.

2. According to the method described in claim 1, which is based on artificial intelligence technology combined with OCR algorithm model and AI identification to automatically handle hidden dangers, it is characterized in that: The specific steps of Step 1 include: Step 1.1, data preprocessing: clean and standardize the input data to improve the training efficiency and performance of the neural network; Step 1.2, using the hidden Markov model forward algorithm, backward algorithm and Viterbi algorithm propagation: the input data is propagated forward through the network layer by layer, and the output of each neuron is calculated; Step 1.3, calculate the loss: calculate the loss function value based on the result of the output layer and the true label; Step 1.4: Construct a neural network based on the data obtained in Step 1.2 and Step 1.

3.

3. According to the method described in claim 2, which is based on artificial intelligence technology combined with OCR algorithm model fusion AI identification automatic processing hidden danger method, it is characterized in that: The specific steps of Step 2 include: Step 2.

1. Develop a recognition module based on artificial intelligence technology to analyze and understand the text information recognized by OCR; Step 2.2, AI recognition; collect data related to the target recognition task through data collection and preprocessing, and perform preprocessing work such as cleaning and labeling; perform feature extraction to extract useful feature information from the preprocessed data; use the labeled data to adjust the model parameters to optimize the recognition effect; tune the model according to the evaluation results, adjust the model structure, and optimize the algorithm parameters; deploy the trained model to the actual application scenario.

4. According to the method described in claim 3, which is based on artificial intelligence technology combined with OCR algorithm model fusion AI identification automatic processing hidden danger method, it is characterized in that: The forward algorithm of the hidden Markov model in Step 1.2 includes: Initial value: α1(i) = πibi(o1), i = 1, 2, ..., N; Recursion: αt+1(i) = [∑j=1N αt(j)aji]bi(ot+1), i = 1, 2, ..., N; t = 1, 2, ..., T-1; Termination: P(O|λ) = ∑i=1N αT(i); Parameter explanation: Initialize the forward probability α1(i). For all states i, α1(i) = πibi(o1), where πi is the initial state probability and bi(o1) is the probability of observing o1 in state i. For t = 1, 2, ..., T-1, recursively calculate αt+1(i): \alpha_{t+1}(i) = \left[\sum_{j=1}^{N} \alpha_t(j) a_{ji}\right] b_i(o_{t+1}) \]; Among them, at(j) is the forward probability of state j at time t, aji is the transition probability of state j to state i, and bi(ot+1) is the probability of observing ot+1 in state i; Calculate the probability P(O|λ) of the observation sequence O under model λ: P(O|λ) = ∑i=1N αT(i); Among them, αT(i) is the forward probability of state i at time T, and N is the total number of states; πii is the initial state probability; α1(i) is the initial forward probability; at(j) is the forward probability of state j at time t, aji is the transition probability from state j to state i, bi(ot+1) is the probability of observing ot+1 in state i; P(O|λ) is the probability of calculating the observation sequence O under model λ; Among them, αT(i) is the forward probability of state i at time T, and N is the total number of states.

5. According to the method described in claim 4, which is based on artificial intelligence technology combined with OCR algorithm model fusion AI identification automatic processing hidden danger method, it is characterized in that: The backward algorithm of the hidden Markov model in Step 1.2 includes: Baum-Welch algorithm, a type of EM algorithm: Initialize model parameters λ = (A, B, π); Iterate until convergence: Step E: Based on the current model parameters and the observation sequence O, calculate the joint probability γt(i, j) that the state it at each time t is qi and the state it+1 at time t+1 is qj and the probability ξt(i) that the state it is qi; Step M: Update the model parameters A, B, π according to the probability obtained in step E; Estimate the parameters of the hidden state sequence of the HMM given some or all of the observation sequence: (π): initial state probability; A state transition probability; B and observation probability.

6. According to the method described in claim 5, which is based on artificial intelligence technology combined with OCR algorithm model fusion AI identification automatic processing hidden danger method, it is characterized in that: The Hidden Markov Model Viterbi Algorithm in Step 1.2 includes: Initialization: δ1(i) = πibi(o1), ψ1(i) = 0, i = 1, 2, ..., N; Recursion: δt(j) = maxi δt-1(i)aijbj(ot), ψt(j) = arg maxi [δt-1(i)aij], j = 1,2, ..., N; t = 2, 3, ..., T; Termination: iT* = arg maxi [δT(i)]; Parameter analysis: Define δt(i) as the maximum probability that the hidden state is i and produces the observation sequence O_1, O_2, ..., O_t at time t, that is, \delta_t(i) = \max_{q_1, q_2, \ldots, q_{t-1}} P(q_1, q_2, \ldots, q_{t-1}, q_t = i, O_1, O_2, \ldots, O_t | \lambda) \] where λ is the model parameter of HMM, including the initial state probability, state transition probability and observation probability; Path backtracking: it* = ψt+1; Parameter analysis: Where a_{ij} is the probability of transitioning from state i to state j; b_j(O_t) is the probability that state j produces the observation value O_t; at the same time, the path information is saved; that is, ψ_t(j) is the previous optimal state of state j at time t.

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