Dynamic electronic work order generation method and device, electronic equipment and storage medium

Abstract

This disclosure provides a method, apparatus, electronic device, and storage medium for generating dynamic electronic work tickets, relating to the field of power operation safety management technology. It collects multi-source environmental perception data from the work site in real time and dynamically determines environmental variable thresholds and process adjustment strategies based on historical accident correlation data through an adaptive decision model. This enables dynamic reconstruction of standardized work process templates. Simultaneously, during process execution, it enhances voice interaction command processing through a robust voice interaction mechanism by incorporating environmental noise spectrum characteristics. Furthermore, it integrates environmental perception data, compliance verification results, and voice interaction records during step execution, establishing causal relationships to generate dynamic electronic work tickets with a complete data traceability chain. Therefore, it can solve the problems of poor environmental adaptability, low interaction accuracy, and lack of data traceability in existing technologies due to static processes that fail to incorporate environmental variables into the process decision logic.

Description

Technical Field

[0001] This disclosure relates to the field of power operation safety management technology, and in particular to a method and apparatus for generating dynamic electronic work tickets, electronic equipment and storage medium. Background Technology

[0002] Electronic work permits, as a core management tool for power safety, are widely used in high-risk work scenarios such as working at heights, hot work, and hoisting. With the deepening of digital transformation, related technologies, through the collaborative operation of process engines, mobile terminals, and basic sensors, have built a digital system covering permit issuance, step execution, and archiving traceability. Specifically, this system relies on an interactive mode of preset templates and manual confirmation to achieve standardized recording of work processes and online control of key links.

[0003] However, existing electronic work permit systems typically use static process templates and lack a dynamic mapping mechanism between environmental variables and decision-making logic, causing safety measures to lag behind changes in operating conditions. Specifically, in high-noise or extreme weather environments, traditional voice interaction suffers from a high rate of mishearing commands due to a lack of spectrum adaptation capabilities, and sensor data is only used for post-event alarms rather than pre-event process reconstruction. This disconnect between perception and execution not only leads to frequent invalid operations and process stagnation, but also, due to the lack of causal correlation between environmental parameters and verification results, results in a lack of a complete chain of evidence for accident tracing, making it difficult to guarantee inherent safety under complex operating conditions. Summary of the Invention

[0004] This disclosure provides a method and apparatus for generating dynamic electronic work tickets, as well as an electronic device and storage medium. Its main objective is to at least partially solve one of the technical problems in related technologies.

[0005] According to a first aspect of this disclosure, a method for generating dynamic electronic work tickets is provided, comprising:

[0006] Real-time collection of multi-source environmental perception data from the work site, and dynamic determination of environmental variable thresholds and corresponding process adjustment strategies under the current work scenario through an adaptive decision model based on historical accident correlation data; Based on the comparison results between multi-source environmental perception data and environmental variable thresholds, the preset standardized work process template is dynamically reconstructed to generate a dynamic work process that includes new safety verification steps or adjustments to execution parameters. During the execution of dynamic work processes, the environmental noise spectrum characteristics are analyzed in real time, and the voice interaction commands are enhanced through a robust voice interaction mechanism to complete the step confirmation. By integrating environmental perception data, compliance verification results, and voice interaction records at each step of execution, a dynamic electronic work ticket with a complete data traceability chain is generated.

[0007] According to a second aspect of this disclosure, a dynamic electronic work ticket generation device is provided, comprising: The data acquisition unit is used to collect multi-source environmental perception data from the work site in real time, and dynamically determine the environmental variable thresholds and corresponding process adjustment strategies under the current work scenario based on historical accident correlation data through an adaptive decision model. The reconstruction unit is used to dynamically reconstruct the preset standardized work process template based on the comparison results of multi-source environmental perception data and environmental variable thresholds, and generate a dynamic work process that includes new safety verification steps or adjusted execution parameters. The analysis unit is used to analyze the environmental noise spectrum characteristics in real time during the execution of dynamic work processes, and enhance the voice interaction commands through a robust voice interaction mechanism to complete the step confirmation. The generation unit is used to integrate the environmental perception data, compliance verification results and voice interaction records at each step of execution, and generate a dynamic electronic work ticket with a complete data traceability chain.

[0008] According to a third aspect of this disclosure, an electronic device is provided, comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0009] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0010] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0011] The dynamic electronic work ticket generation method, device, electronic equipment, and storage medium disclosed herein collect multi-source environmental perception data from the work site in real time and dynamically determine environmental variable thresholds and process adjustment strategies through an adaptive decision-making model based on historical accident correlation data. This enables dynamic reconstruction of standardized work process templates. Simultaneously, during process execution, robust voice interaction mechanisms are used to enhance voice interaction command processing by incorporating environmental noise spectrum characteristics. Furthermore, environmental perception data, compliance verification results, and voice interaction records during step execution are causally correlated and integrated to generate dynamic electronic work tickets with a complete data traceability chain. Therefore, this addresses the problems in existing technologies where static processes fail to incorporate environmental variables into the process decision-making logic, voice ticketing interaction fails in high-noise environments, and electronic work tickets only record operation steps without associating them with environmental parameters and verification results. These problems result in poor environmental adaptability, low interaction accuracy, and lack of data traceability in work processes. The goal is to achieve dynamic adjustment of work processes driven by environmental perception, improve the accuracy and reliability of voice interaction in high-noise environments, construct a causal data traceability chain for the entire work process, and form a fully adaptive closed loop of environmental perception-decision-execution-traceability, achieving a dynamic balance between work safety and efficiency.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0013] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein: Figure 1 A flowchart illustrating a method for generating dynamic electronic work tickets according to an embodiment of this disclosure; Figure 2 This is a schematic diagram of the structure of a dynamic electronic work ticket generation device provided in an embodiment of the present disclosure; Figure 3 A schematic block diagram of an example electronic device provided for embodiments of this disclosure. Detailed Implementation

[0014] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0015] The following description, with reference to the accompanying drawings, outlines a method and apparatus for generating dynamic electronic work tickets, an electronic device, and a storage medium according to embodiments of the present disclosure.

[0016] Figure 1 This is a flowchart illustrating a method for generating dynamic electronic work tickets according to an embodiment of this disclosure.

[0017] like Figure 1 As shown, the method includes the following steps: Step 101: Collect multi-source environmental perception data from the work site in real time, and dynamically determine the environmental variable thresholds and corresponding process adjustment strategies under the current work scenario through an adaptive decision model based on historical accident correlation data.

[0018] In the embodiments of this disclosure, various environmental sensors deployed at the work site continuously and in real time collect multi-source environmental perception data at the work site according to a preset sampling frequency. After the collection is completed, the raw data is preprocessed by denoising and completion, and invalid data is removed before the valid data is transmitted to the decision analysis module. The decision analysis module first retrieves the pre-stored historical accident correlation data, which contains core information such as the correlation characteristics between environmental parameters and work accidents under different work scenarios, accident causes, and corresponding handling logic. This data is used as the basis for the reasoning and learning of the adaptive decision model. Then, the adaptive decision model combines the scenario characteristics of the current work scenario to perform autonomous learning and reasoning calculations, and finally dynamically determines the environmental variable thresholds that are suitable for the current work scenario. At the same time, it matches the process adjustment strategies that correspond one-to-one with each threshold interval. The strategy clarifies the specific adjustment method that the work process needs to be executed when the environmental parameters reach the corresponding threshold. In practical applications, multi-source environmental perception data can include physical parameters such as wind speed, environmental noise, and equipment temperature. Historical accident correlation data can be statistical data on the correlation between different wind speed values ​​and high-altitude fall accidents in high-risk power operations. The adaptive decision model can be a reinforcement learning model. For substation high-altitude operations, the model dynamically determines the wind speed threshold to 5.0 m / s by combining historical data. For wind farm high-altitude operations, the wind speed threshold is optimized to 4.2 m / s. The corresponding process adjustment strategy is to insert a safety rope for reinforcement when the wind speed parameter exceeds the corresponding threshold.

[0019] By collaborating between edge computing and computing terminals, real-time acquisition and preprocessing of multi-source environmental perception data are achieved, ensuring data validity and transmission efficiency. Simultaneously, based on historical accident-related data, an adaptive decision-making model dynamically matches environmental variable thresholds and process adjustment strategies, overcoming the limitations of fixed thresholds. This allows both thresholds and strategies to accurately adapt to the current operational scenario, effectively solving the problems of static processes and failure to incorporate environmental variables into process decision-making logic in existing technologies. This provides scenario-based and dynamic decision-making basis for adjusting operational processes.

[0020] Step 102: Based on the comparison results between multi-source environmental perception data and environmental variable thresholds, dynamically reconstruct the preset standardized work process template to generate a dynamic work process that includes new safety verification steps or adjusted execution parameters.

[0021] In the embodiments of this disclosure, real-time multi-source environmental perception data transmitted by the environmental perception module and environmental variable thresholds output by the decision analysis module under the current work scenario are synchronously acquired through a data interface. Then, various types of environmental perception data are compared with the corresponding environmental variable thresholds one by one in real time to generate multi-dimensional threshold comparison results. These results clearly define core judgment information such as whether each environmental parameter exceeds the threshold and the extent of the exceedance. Next, the process reconstruction module retrieves the standardized work process template pre-stored in the system and matches it with the process adjustment strategy output by the decision analysis module corresponding to the comparison results. According to the strategy, the standardized work process template is dynamically reconstructed. The reconstruction operation specifically includes adding safety verification steps to the template according to actual needs, or adjusting the values ​​of the original work execution parameters in the template, and finally generating a dynamic work process adapted to the current work site environment. In practical application scenarios, for standardized process templates for high-altitude operations, if the real-time wind speed data exceeds the dynamically determined wind speed threshold, the process reconstruction module adds safety verification steps such as wind speed retesting and safety rope reinforcement checks to the template; if the real-time equipment temperature data exceeds the corresponding threshold, the execution time parameter of the voltage testing operation in the template is extended, thereby completing the dynamic reconstruction of the template and generating a dynamic work process.

[0022] The process refactoring module enables real-time comparison of environmental data with dynamic thresholds. Based on the comparison results and corresponding strategies, the standardized process template is dynamically refactored in a targeted manner, allowing the work process to be adjusted in a timely manner according to changes in the on-site environment. This effectively solves the problem of static work processes in existing technologies that cannot adapt to changes in the on-site environment. It gives the work process the characteristic of dynamic adjustment driven by environmental perception, and improves the matching degree between the work process and the actual on-site working conditions.

[0023] Step 103: During the execution of the dynamic work process, the environmental noise spectrum characteristics are analyzed in real time, and the voice interaction commands are enhanced through a robust voice interaction mechanism to complete the step confirmation.

[0024] In the embodiments of this disclosure, the process execution module synchronizes the real-time execution nodes of the dynamic work process to the voice interaction module. The voice interaction module retrieves real-time noise data of the work site from the environmental perception module and performs spectrum analysis processing on it to extract the spectral characteristics of the ambient noise, such as the main frequency band and sound pressure level, in real time. Subsequently, the module activates the built-in robust voice interaction mechanism to perform targeted enhancement processing on the voice interaction commands during the work process based on the extracted noise spectrum characteristics. Through feature separation of noise and valid commands, and signal gain and frequency adaptation of valid commands, the voice interaction commands are optimized. Finally, the enhanced voice interaction commands are accurately identified and their validity is verified. Based on the verification results, the current execution step of the dynamic work process is confirmed by voice, and the confirmation signal is synchronously fed back to the process execution module to advance the subsequent work steps. In practical applications, when the noise sound pressure level at the work site reaches 85dB+ and the main frequency band is 200-500Hz, the robust voice interaction mechanism will filter the frequency band accordingly and improve the effective signal gain of the voice interaction commands. This will enhance the processing and accurate recognition of confirmation commands such as "safety belt check completed" and "safety rope reinforcement completed", thereby achieving effective confirmation of the corresponding work steps.

[0025] By analyzing the environmental noise spectrum characteristics in real time through the voice interaction module and relying on a robust voice interaction mechanism to perform targeted enhancement processing on voice interaction commands, the problem of voice interaction commands being easily interfered with and having low recognition accuracy in high-noise environments is effectively solved. This improves the accuracy and reliability of dynamic work process step confirmation and ensures the continuous and smooth execution of the work process.

[0026] Step 104: Integrate the environmental perception data, compliance verification results, and voice interaction records at each step to generate a dynamic electronic work ticket with a complete data traceability chain.

[0027] In the embodiments of this disclosure, firstly, according to the execution sequence of the dynamic work process, the corresponding data for each step is retrieved from each module, including real-time multi-source environmental perception data collected by the environmental perception module, the step compliance verification results output by the process execution module, and the full voice interaction record retained by the voice interaction module. All data carries a unique step execution timestamp and step identifier. Subsequently, based on the time and logical dimensions of step execution, the module integrates the three types of data for the same work step, using environmental perception data as the environmental background cause of step execution, compliance verification results as the effect of step execution, and voice interaction records as confirmation evidence of step execution, establishing a one-to-one structured causal relationship among the three. Finally, based on the integrated structured data, a dynamic electronic work ticket is generated according to a preset standardized data format. This electronic work ticket has a built-in complete data traceability chain, which enables full-dimensional data traceability of each work step from environmental background and execution compliance to interaction confirmation. In practical application scenarios, for the climbing steps of high-altitude operations, the module will integrate environmental perception data such as wind speed of 5.8m / s and environmental noise of 82dB when the step is executed, compliance verification results of the pre-climb protection check, and voice interaction records of "protection check completed, request to climb" and "climbing permitted". After causally linking the three types of data, the module will embed them into the corresponding step items of the dynamic electronic work ticket to form a traceable and complete data record.

[0028] The dynamic electronic ticket generation module integrates the time-series, logical, and causal relationships of all-dimensional data on work steps, and generates dynamic electronic work tickets with complete data traceability chains based on the integrated data. This effectively solves the problem in existing technologies where electronic work tickets only record operation steps and do not associate environmental parameters and verification results. It makes the electronic work ticket a complete data carrier for the entire work process, greatly improving the traceability and completeness of work data, and providing accurate causal data for work risk analysis and accident tracing.

[0029] The dynamic electronic work ticket generation method disclosed herein collects multi-source environmental perception data from the work site in real time and dynamically determines environmental variable thresholds and process adjustment strategies through an adaptive decision-making model based on historical accident correlation data. This enables dynamic reconstruction of standardized work process templates. Simultaneously, during process execution, it enhances voice interaction command processing through a robust voice interaction mechanism by incorporating environmental noise spectrum characteristics. Furthermore, it integrates environmental perception data, compliance verification results, and voice interaction records during step execution, generating a dynamic electronic work ticket with a complete data traceability chain. Therefore, it solves the problems in existing technologies where static processes fail to incorporate environmental variables into the process decision-making logic, voice ticketing interaction fails in high-noise environments, and electronic work tickets only record operation steps without associating them with environmental parameters and verification results. These problems lead to poor environmental adaptability, low interaction accuracy, and lack of data traceability in work processes. The method achieves dynamic adjustment of work processes driven by environmental perception, improves the accuracy and reliability of voice interaction in high-noise environments, constructs a causal data traceability chain for the entire work process, and forms a fully adaptive closed loop of environmental perception-decision-execution-traceability, achieving a dynamic balance between work safety and efficiency.

[0030] In the embodiments involved in this application, there are various feasible specific implementation methods. To clearly and completely illustrate the technical solutions of this disclosure, the implementation methods listed below are merely exemplary and do not constitute a limitation on the scope of protection of this disclosure. That is, in addition to the implementation methods described below, other implementation methods that can be obtained by those skilled in the art based on the technical content disclosed in this disclosure through reasonable logical analysis, reasoning, or limited experimentation should also be covered within the scope of protection of this disclosure. The following specifically describes some exemplary implementation methods: As a specific implementation of this disclosure, based on the basic solution, multi-source environmental perception data of the work site is collected in real time, and environmental variable thresholds and corresponding process adjustment strategies under the current work scenario are dynamically determined through an adaptive decision model based on historical accident correlation data. This is further specified as follows: acquiring wind speed data collected by a wind speed sensor, temperature data collected by an equipment temperature sensor, and noise decibel data collected by a noise sensor, and inputting the multi-source environmental perception data into a reinforcement learning model; extracting equipment type feature vectors for the current work scenario, and calling accident rate curve data matching the current equipment type from a pre-stored historical accident correlation database; using the reinforcement learning model to calculate the optimal reward function based on the accident rate curve data, and dynamically outputting environmental variable thresholds adapted to the current work scenario and process adjustment strategies including newly added safety verification step identifiers.

[0031] Specifically, the system's environmental perception module acquires multi-source data through dedicated sensors, including real-time wind speed data from wind speed sensors, equipment body temperature data from equipment temperature sensors, and ambient noise decibel data from noise sensors. After normalizing and preprocessing these three types of raw data, the standardized multi-source environmental perception data is input into a pre-defined reinforcement learning model. The decision analysis module analyzes the current work scenario using feature extraction algorithms, extracting equipment type feature vectors containing equipment category, work site, and equipment operating conditions. Then, a vector matching algorithm searches the historical accident association database to retrieve accident rate curve data with a similarity ≥90% to the feature vector. This data represents a continuous curve showing the probability of work accidents corresponding to different environmental parameter values. The reinforcement learning model uses the accident rate curve data as state input, with the optimization objectives of maximizing work safety and minimizing process redundancy. It iteratively calculates the optimal reward function using the Bellman equation. When the reward function converges to a pre-defined stable range, the model dynamically outputs the threshold values ​​for environmental variables in wind speed, temperature, and noise dimensions. Simultaneously, it outputs a process adjustment strategy containing a unique numerical identifier for a newly added safety verification step, which maps one-to-one with the step information in the system's process library. In some optional embodiments, the feature vector similarity matching threshold can be dynamically adjusted according to the job risk level, and the reinforcement learning model can also be replaced by a deep Q-network or similar model, all of which can achieve the above effects.

[0032] By precisely defining the categories of multi-source environmental perception data and performing standardized preprocessing, the effectiveness of the model input data is improved. By combining precise matching of equipment type feature vectors with iterative calculation of the optimal reward function, the output environmental variable thresholds are highly adapted to the actual risk characteristics of the current work scenario. The process adjustment strategy with a unique identifier achieves precise docking with the system process library, which greatly improves the execution efficiency of subsequent process reconstruction instructions.

[0033] As a specific implementation of this disclosure, based on the basic scheme, the preset standardized operation process template is dynamically reconstructed according to the comparison results of multi-source environmental sensing data and environmental variable thresholds to generate a dynamic operation process that includes adding safety verification steps or adjusting execution parameters. It is further defined as follows: the real-time collected wind speed data is compared with the wind speed threshold in the dynamically determined environmental variable thresholds, and when the wind speed data is detected to be greater than the wind speed threshold, the wind speed detection step and the safety reinforcement inspection step are automatically inserted before the key steps in the preset standardized operation process template; and / or, the real-time collected equipment temperature data is compared with the temperature threshold, and when the equipment temperature data is detected to exceed the preset temperature range, the execution time parameter of the voltage verification step is dynamically adjusted from the standard time to the extended time.

[0034] Specifically, the process refactoring module establishes a dedicated real-time comparison link for wind speed and temperature data. It first continuously compares the real-time wind speed sampling data transmitted by the environmental perception module with dynamically determined wind speed thresholds. When wind speed data for three consecutive sampling cycles exceeds the threshold, the module automatically parses the steps in the standardized work process template, identifies key steps such as climbing and high-altitude operations, and inserts wind speed detection and safety reinforcement checks with mandatory pre-selection markers before their execution nodes. Simultaneously, the module performs interval matching comparison between real-time equipment temperature data and preset temperature ranges defined by temperature thresholds. When temperature data exceeds this range, it directly retrieves the parameter configuration item for the voltage testing step in the template, dynamically adjusting its execution duration from the system standard duration to an extended duration (1.2-2 times the standard duration). This adjustment is immediately synchronized to the process execution module. In some optional embodiments, the wind speed exceeding the limit trigger condition can be set to exceed the limit for a single sampling cycle and last for more than 5 seconds. The extended voltage testing duration can also be adjusted in gradients according to the degree of temperature exceeding the limit, both achieving precise process refactoring.

[0035] By setting multiple sampling period comparison trigger conditions for wind speed data, invalid process adjustments caused by abnormal data in a single frame are avoided. Safety verification steps are inserted before key steps to achieve proactive prevention and control of operational risks. By comparing temperature ranges and adjusting the voltage testing time, the accuracy requirements of operations under high-temperature conditions are adapted, making the process reconstruction operation more in line with the actual on-site conditions and improving the reconstructiveness and effectiveness.

[0036] As a specific implementation of this disclosure, based on the basic scheme, the environmental noise spectrum characteristics are analyzed in real time, and the voice interaction command is enhanced through a robust voice interaction mechanism to complete the step confirmation. Further, the method is as follows: Environmental audio signals from the work site are collected in real time using a microphone array, and frequency domain analysis of the environmental audio signals is performed using spectrum masking technology to identify high-noise interference frequency bands; a dynamic spectrum mask is generated based on the identified high-noise interference frequency bands, and the dynamic spectrum mask is applied to a voiceprint enhancement model to filter noise in the interference frequency bands, while simultaneously extracting the operator's voiceprint features for identity matching; when the operator's identity is confirmed to be legitimate and the environmental noise decibel data is greater than a preset threshold, the broadcast frequency of the voice interaction command is dynamically adjusted to a preset sensitive frequency band, and the voice duration is compressed through the voiceprint enhancement model to generate enhanced voice interaction commands to complete the step confirmation.

[0037] Specifically, the voice interaction module uses a 4-channel microphone array to collect environmental audio signals from the work site in real time at a sampling rate of 16kHz. After converting the analog audio signals to digital signals, it uses spectral masking technology to perform frequency domain analysis on the digital audio signals. By calculating the difference between the energy proportion of each frequency band and the noise threshold, it accurately identifies high-noise interference frequency bands in the environment. Based on the frequency range of the identified interference bands, the module generates a dynamic spectral mask and loads it into the input layer of the voiceprint enhancement model to filter noise signals in the corresponding frequency bands. At the same time, it extracts the operator's voiceprint features from the voice interaction commands and performs cosine similarity matching with the pre-stored 128-dimensional MFCC voiceprint feature vector to complete the identity verification. When the identity matching is successful and the real-time noise decibel data is greater than the preset threshold of 85dB, the module dynamically adjusts the broadcast frequency of the voice interaction commands to the preset sensitive frequency band of the human ear (2000-3000Hz). Simultaneously, it compresses the voice duration by 50% using the feature compression algorithm of the voiceprint enhancement model, ultimately generating denoised, up-frequency, and short enhanced voice interaction commands to complete the confirmation of work steps. In some optional embodiments, the number of microphone array channels can be adjusted to 6 channels, and the voiceprint feature matching algorithm can be replaced with Euclidean distance matching, both of which can achieve enhanced processing of voice commands.

[0038] By using spectrum masking technology to accurately filter high-noise interference frequency bands and combining it with voiceprint feature identity matching, not only is noise removal achieved, but also unauthorized personnel's misoperation of instructions is avoided. By adjusting the broadcast frequency to the frequency band sensitive to the human ear and compressing the voice duration, the recognition efficiency and accuracy of voice interaction commands are further improved in high-noise environments, ensuring the accuracy of step confirmation.

[0039] As a specific embodiment of this disclosure, based on the basic scheme, frequency domain analysis of the environmental audio signal is performed using spectrum masking technology to identify high noise interference frequency bands. This is further defined as follows: beamforming processing is performed on the multi-channel environmental audio signal acquired by the microphone array to separate the target sound source direction signal from the background noise signal; a fast Fourier transform is performed on the separated background noise signal to obtain a real-time noise spectrum, and the frequency band with concentrated energy peaks in the real-time noise spectrum is identified as the high noise interference frequency band; an adaptive spectrum masking matrix is ​​constructed based on the center frequency and bandwidth parameters of the high noise interference frequency band, and the adaptive spectrum masking matrix is ​​applied to the attention mechanism layer of the voiceprint enhancement model to suppress the masking effect of the high noise interference frequency band on voice interaction commands.

[0040] Specifically, the voice interaction module first performs delay-superposition beamforming on the multi-channel environmental audio signals collected by the microphone array. By calibrating the time delay and amplitude weight of each channel signal, it accurately separates the speech signal from the target sound source direction from the irregular background noise signal, removing the target speech signal and retaining the pure background noise signal. Then, it performs a 1024-point Fast Fourier Transform on the separated background noise signal, converting the time-domain signal to a frequency-domain signal and generating a real-time noise spectrum with a resolution of 15.625Hz. By traversing the frequency and energy axes of the spectrum, frequency bands with an energy percentage exceeding 30% of the total energy and continuous distribution are identified as high-noise interference bands, and their center frequency and bandwidth parameters are extracted. Based on these parameters, the module constructs a two-dimensional adaptive spectrum mask matrix, assigning a value of 0 to the corresponding position of the interference band and a value of 1 to the non-interference band. The matrix is ​​embedded into the attention mechanism layer of the voiceprint enhancement model, reducing the model's attention weight for the interference band to a preset low value. In some optional embodiments, beamforming can be replaced by an adaptive minimum variance distortionless response algorithm, and the number of fast Fourier transform points can be adjusted to 2048 points, both of which can achieve accurate identification and suppression of interference frequency bands.

[0041] Beamforming enables precise separation of target speech from background noise, improving the purity of the data source for noise spectrum analysis. Combined with Fast Fourier Transform, high-noise interference frequency bands are quantized and located. An adaptive spectrum mask matrix is ​​applied to the model's attention mechanism layer, achieving precise and adaptive suppression of interference frequency bands. This significantly reduces the masking effect of noise on voice interaction commands, laying a high-purity signal foundation for subsequent voiceprint extraction and command recognition.

[0042] As a specific implementation of this disclosure, based on the basic solution, the environmental perception data, compliance verification results, and voice interaction records at each step of execution are causally linked and integrated to generate a dynamic electronic work ticket with a complete data traceability chain. This is further defined as follows: after the operator confirms the completion of the current step through enhanced voice interaction commands, the device sensor application programming interface is automatically invoked to calculate the position deviation data, and the position deviation data is used as the compliance verification result; environmental perception data at the moment of execution of the current step is captured in real time, and the environmental perception data, compliance verification results, and corresponding voice interaction records are packaged to generate a structured data block; a hash algorithm is executed on the structured data block to generate an tamper-proof hash value, and the tamper-proof hash value is embedded in the dynamic electronic work ticket, forming a complete data traceability chain containing environmental parameters, verification results, and operation records.

[0043] Specifically, after receiving the enhanced voice interaction command confirmation signal from the voice interaction module, the dynamic electronic ticket generation module automatically calls the device sensor application interface through a standardized interface protocol to read the coordinates of the actual operation location and the standard location. It then calculates the position deviation data using the Euclidean distance algorithm and uses this as the compliance verification result for the current step, marking it with a compliance threshold. Based on the unique timestamp and identifier of the step, the module captures the environmental perception data in real time. It then packages the environmental perception data, the position deviation compliance verification result, and the complete voice interaction record of the corresponding step into a JSON-formatted structured data block with a unique index, following the logic of "environmental parameters - verification result - interaction record." Subsequently, the module executes the SHA-256 hash algorithm on this data block to generate a fixed-length, tamper-proof hash value. This hash value and the data block are then synchronously embedded into the corresponding step entry of the dynamic electronic work ticket, bound to the ticket's basic information, forming a complete data traceability chain with layered connections. In some optional embodiments, the position deviation can be calculated using the Manhattan distance algorithm, the hash algorithm can be the SM3 national cryptographic algorithm, and the structured data block can be in Protobuf format, all of which achieve data association and tamper-proof effects.

[0044] By calling the device sensor API to calculate the position deviation as the compliance verification result, the compliance judgment is supported by objective device detection data, which improves the accuracy and objectivity of the verification result. By generating tamper-proof hash values ​​through hash algorithm and embedding them into electronic work tickets, it not only achieves accurate causal binding of the three types of data, but also prevents the tampering of ticket data from the encryption level, making the data traceability chain unique and tamper-proof, which greatly improves the credibility of work traceability.

[0045] As a specific implementation of this disclosure, based on the basic solution, the environmental perception data, compliance verification results and voice interaction records at each step of execution are causally linked and integrated to generate a dynamic electronic work ticket with a complete data traceability chain. It is further limited to: when the wind speed data in the environmental perception data is detected to be greater than the first preset threshold and the position deviation data in the compliance verification results is greater than the second preset threshold, a high-risk event identifier is automatically marked in the dynamic electronic work ticket.

[0046] Specifically, the dynamic electronic ticket generation module integrates the three types of data through causal correlation and constructs structured data blocks. It also incorporates a dual-threshold joint judgment logic, pre-retrieving the wind speed threshold dynamically determined in step 101 as the first preset threshold and the system-preconfigured position deviation compliance threshold as the second preset threshold. The module extracts wind speed data from the structured data blocks in real time and compares it with the first preset threshold, and extracts position deviation data and compares it with the second preset threshold. When both comparison results exceed the corresponding thresholds, the module automatically generates a high-risk event identifier containing the risk trigger type, trigger timestamp, and threshold exceedance range. This identifier uses a dedicated character encoding format and is bound to the unique identifier of the work step. The module then embeds this high-risk event identifier into the risk labeling field of the corresponding step on the dynamic electronic work ticket and simultaneously labels it in the global risk summary area of ​​the electronic ticket, achieving precise association between risk points and work data. In some optional embodiments, the identifier generation can be triggered only after two consecutive data collection cycles of dual threshold exceedance, or the high-risk event identifier can be graded and coded according to the exceedance range, both of which can achieve precise marking of composite risks.

[0047] By using a dual-threshold judgment logic based on wind speed and location deviation to trigger high-risk event identification, the system achieves accurate identification and targeted marking of complex risks at the work site. This allows dynamic electronic work tickets to directly present risk triggering nodes during the work process, providing accurate risk location basis for subsequent work risk review and accident root cause analysis, and further enhancing the risk analysis value of the data traceability chain.

[0048] It should be noted that the embodiments of this disclosure may include multiple steps. For ease of description, these steps are numbered, but these numbers are not a limitation on the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of this disclosure do not limit this.

[0049] Corresponding to the above-described method for generating dynamic electronic work tickets, this disclosure also proposes a device for generating dynamic electronic work tickets. Since the device embodiments of this disclosure correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to the method embodiments described above, and will not be repeated here.

[0050] Figure 2 This is a schematic diagram of the structure of a dynamic electronic work ticket generation device provided in an embodiment of the present disclosure, as shown below. Figure 2 As shown, it includes: The acquisition unit 21 is used to collect multi-source environmental perception data of the work site in real time, and dynamically determine the environmental variable thresholds and corresponding process adjustment strategies under the current work scenario through an adaptive decision model based on historical accident correlation data. The reconstruction unit 22 is used to dynamically reconstruct the preset standardized work process template based on the comparison results of multi-source environmental perception data and environmental variable thresholds, and generate a dynamic work process that includes new safety verification steps or adjusted execution parameters. Analysis unit 23 is used to analyze the environmental noise spectrum characteristics in real time during the execution of dynamic work processes, and enhance the voice interaction commands through a robust voice interaction mechanism to complete the step confirmation. The generation unit 24 is used to integrate the environmental perception data, compliance verification results and voice interaction records at each step of execution by causal correlation to generate a dynamic electronic work ticket with a complete data traceability chain.

[0051] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0052] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0053] Figure 3 A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0054] like Figure 3 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An I / O (Input / Output) interface 305 is also connected to the bus 304.

[0055] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0056] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the dynamic electronic work ticket generation method. For example, in some embodiments, the dynamic electronic work ticket generation method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the aforementioned dynamic electronic work ticket generation method by any other suitable means (e.g., by means of firmware).

[0057] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0058] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0059] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0060] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0061] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0062] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service system that addresses the shortcomings of traditional physical hosts and VPS (Virtual Private Server) services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0063] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0064] The various numerical designations such as "first," "second," etc., used in this disclosure are merely for ease of description and are not intended to limit the scope of the embodiments of this disclosure, nor do they indicate a sequential order.

[0065] At least one of the features described in this disclosure can also be described as one or more, and multiple features can be two, three, four or more, and this disclosure does not impose any limitations. In the embodiments of this disclosure, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no sequential order or size order among the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0066] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0067] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for generating dynamic electronic work tickets, characterized in that, include: Real-time collection of multi-source environmental perception data from the work site, and dynamic determination of environmental variable thresholds and corresponding process adjustment strategies under the current work scenario through an adaptive decision model based on historical accident correlation data; Based on the comparison results between the multi-source environmental perception data and the environmental variable thresholds, the preset standardized work process template is dynamically reconstructed to generate a dynamic work process that includes new safety verification steps or adjusted execution parameters. During the execution of the dynamic work process, the environmental noise spectrum characteristics are analyzed in real time, and the voice interaction commands are enhanced through a robust voice interaction mechanism to complete the step confirmation. By integrating environmental perception data, compliance verification results, and voice interaction records at each step of execution, a dynamic electronic work ticket with a complete data traceability chain is generated.

2. The method according to claim 1, characterized in that, The process involves real-time acquisition of multi-source environmental perception data from the work site, and dynamic determination of environmental variable thresholds and corresponding process adjustment strategies for the current work scenario using an adaptive decision model based on historical accident correlation data. The system acquires wind speed data from a wind speed sensor, temperature data from a device temperature sensor, and noise decibel data from a noise sensor, and inputs the multi-source environmental perception data into a reinforcement learning model. Extract the equipment type feature vector of the current work scenario, and call the accident rate curve data that matches the current equipment type from the pre-stored historical accident association database; The reinforcement learning model is used to calculate the optimal reward function based on the accident rate curve data, and dynamically outputs environmental variable thresholds that are adapted to the current work scenario and process adjustment strategies that include identifiers for newly added safety verification steps.

3. The method according to claim 1, characterized in that, The step of dynamically reconstructing a preset standardized work process template based on the comparison results between the multi-source environmental perception data and the environmental variable thresholds, and generating a dynamic work process that includes adding safety verification steps or adjusting execution parameters, includes: The real-time collected wind speed data is compared with the wind speed threshold in the dynamically determined environmental variable threshold. When the wind speed data is detected to be greater than the wind speed threshold, the wind speed detection step and the safety reinforcement inspection step are automatically inserted before the key steps in the preset standardized operation process template. And / or, The real-time collected equipment temperature data is compared with the temperature threshold, and when the equipment temperature data is detected to exceed the preset temperature range, the execution time parameter of the voltage testing step is dynamically adjusted from the standard time to an extended time.

4. The method according to claim 1, characterized in that, The real-time analysis of environmental noise spectrum characteristics, and the enhancement of voice interaction commands through a robust voice interaction mechanism to complete step confirmation, include: The ambient audio signal at the work site is acquired in real time by a microphone array, and the frequency domain analysis of the ambient audio signal is performed using spectrum masking technology to identify high noise interference frequency bands. A dynamic spectrum mask is generated based on the identified high-noise interference frequency bands, and the dynamic spectrum mask is applied to the voiceprint enhancement model to filter noise in the interference frequency bands. At the same time, the voiceprint features of the operator are extracted for identity matching. Once the operator's identity is confirmed to be legitimate and the ambient noise decibel data is greater than a preset threshold, the broadcast frequency of the voice interaction command is dynamically adjusted to a preset sensitive frequency band, and the voice duration is compressed through the voiceprint enhancement model to generate an enhanced voice interaction command to complete the step confirmation.

5. The method according to claim 4, characterized in that, The step of using spectral masking technology to perform frequency domain analysis on the environmental audio signal to identify high-noise interference frequency bands includes: Beamforming processing is performed on the multi-channel ambient audio signals acquired by the microphone array to separate the target sound source direction signal from the background noise signal; A fast Fourier transform is performed on the separated background noise signal to obtain a real-time noise spectrum, and the frequency bands with concentrated energy peaks in the real-time noise spectrum are identified as high noise interference frequency bands. An adaptive spectral mask matrix is ​​constructed based on the center frequency and bandwidth parameters of the high noise interference band, and the adaptive spectral mask matrix is ​​applied to the attention mechanism layer of the voiceprint enhancement model to suppress the masking effect of the high noise interference band on voice interaction commands.

6. The method according to claim 1, characterized in that, The process of integrating environmental perception data, compliance verification results, and voice interaction records at each step to generate a dynamic electronic work order with a complete data traceability chain includes: After the operator confirms the completion of the current step through enhanced voice interaction commands, the device's sensor application programming interface is automatically invoked to calculate the position deviation data, and the position deviation data is used as the compliance verification result. Real-time capture of environmental perception data at the moment of execution of the current step, and package the environmental perception data, compliance verification results and corresponding voice interaction records into a structured data block; A hash algorithm is executed on the structured data block to generate a tamper-proof hash value, and the tamper-proof hash value is embedded in the dynamic electronic work ticket to form a complete data traceability chain that includes environmental parameters, verification results, and operation records.

7. The method according to claim 6, characterized in that, Also includes: When the wind speed data in the environmental perception data is detected to be greater than the first preset threshold and the position deviation data in the compliance verification results is greater than the second preset threshold, a high-risk event identifier is automatically marked in the dynamic electronic work ticket.

8. A dynamic electronic work ticket generation device, characterized in that, include: The data acquisition unit is used to collect multi-source environmental perception data from the work site in real time, and dynamically determine the environmental variable thresholds and corresponding process adjustment strategies under the current work scenario based on historical accident correlation data through an adaptive decision model. The reconstruction unit is used to dynamically reconstruct the preset standardized work process template based on the comparison results between the multi-source environmental perception data and the environmental variable threshold, and generate a dynamic work process that includes new safety verification steps or adjusted execution parameters. The analysis unit is used to analyze the environmental noise spectrum characteristics in real time during the execution of the dynamic work process, and enhance the voice interaction commands through a robust voice interaction mechanism to complete the step confirmation. The generation unit is used to integrate the environmental perception data, compliance verification results and voice interaction records at each step of execution, and generate a dynamic electronic work ticket with a complete data traceability chain.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-7.

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