An integrated system and method for multi-parameter safety monitoring and early warning in confined space operations

Abstract

This invention discloses an integrated system and method for multi-parameter safety monitoring and early warning in confined space operations, relating to the field of parameter monitoring and early warning. This invention ensures the effectiveness and accuracy of data collection by each sensor device within the confined space by coating the surface of various sensing devices during confined space operations. Specifically, by optimizing maintenance decisions for the coating during the current confined space operation, the health of the coating is ensured to reach its optimal level at the end of each stage. The Bald Eagle optimization algorithm is used to iteratively adjust the material selection and maintenance operation decision data for each type of sensor device in the current operation stage. With each iteration, the health of the surface coating of various types of sensor devices at the end of the current operation stage increases, ensuring more accurate data collection by each type of sensor device during the current operation stage.

Description

Technical Field

[0001] This invention belongs to the field of parameter monitoring and early warning, and specifically relates to an integrated system and method for multi-parameter safety monitoring and early warning in confined space operations. Background Technology

[0002] Chinese Patent CN106297191B discloses an automatic monitoring and early warning system for life safety in confined spaces, including a power supply, a control host, and a sensor module. The sensor module can detect three parameters: harmful gas parameters, temperature parameters, and life parameters. When the temperature or harmful gas concentration in the confined space exceeds a set threshold and a living being is present, the alarm circuit of the control host is triggered, generating a corresponding audible and visual alarm through an audible and visual alarm, while simultaneously receiving and transmitting a distress signal wirelessly.

[0003] Due to the unique environment inside a confined space, sensing devices installed inside may be affected by the surrounding environment. For example, gas sensors may be affected by the penetration of interfering gas molecules; non-contact probes such as temperature and humidity sensors may be affected by condensation, thus impacting their accuracy. These factors affect the accuracy of the monitored data. Summary of the Invention

[0004] To address the problems in related technologies, this invention proposes an integrated system and method for multi-parameter safety monitoring and early warning in confined space operations, in order to overcome the aforementioned technical problems existing in the existing related technologies.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution:

[0006] This invention relates to an integrated method for multi-parameter safety monitoring and early warning in confined space operations, comprising the following steps:

[0007] S1. Set several types of sensing devices and corresponding protective coating types for monitoring the internal environment of the current limited space, and obtain the current set of sensing devices and the matrix of sensing device protective coating types.

[0008] S2. Collect historical data on confined space operations in conjunction with the sensor device protective coating type matrix and construct the final sensor device coating health mapping equation corresponding to each sensor device to obtain the final sensor device coating health mapping equation set.

[0009] S3. Optimize the material selection decision data and maintenance operation decision data for each type of sensor in each stage by combining the final sensor coating health mapping equation set, and obtain the final material selection decision data matrix and the final maintenance operation decision data matrix for the current operation stage.

[0010] S4. In conjunction with the final material selection decision data matrix of the current operation stage, the final maintenance operation decision data matrix of the current operation stage, and the current set of sensing devices, select the coating decision for the surface coating of each type of sensing device, and collect and issue warnings for the data in the current limited space.

[0011] This solution ensures the effectiveness and accuracy of data collection by applying a coating to the surface of each sensing device during confined space operations. Furthermore, by optimizing maintenance decisions for the coating during the current confined space operation, the solution guarantees that the coating health reaches its optimal level at the end of each stage.

[0012] Preferably, step S1 includes the following steps:

[0013] S11. Set several types of sensing devices to monitor the internal environment of the current limited space, and obtain the current sensing device set; set several corresponding protective coating types for each current sensing device in the current sensing device set, and obtain the sensing device protective coating type matrix; in conjunction with the sensing device protective coating type matrix and the current sensing device set, apply a coating to each current sensing device to obtain the current coated sensing device set.

[0014] S12. Divide the current confined space operation cycle into stages to obtain the current operation stage set; set several stage state variable types for each current operation stage to obtain the operation stage state variable type set;

[0015] S13. In conjunction with the set of state variable types for the operation stage and the matrix of protective coating types for the sensing equipment, set several operation decisions to obtain a material selection decision matrix and a maintenance operation decision set.

[0016] Applying a coating to the surface of a sensing device can reduce the impact of the unique environment within a confined space. Since the coating is continuously consumed during operation, appropriate measures need to be taken to reduce consumption and ensure the coating can last until the end of the operation. Therefore, by setting a set of state variable types for each operation stage to reflect the current state of the coating, a quantitative basis is provided for determining whether subsequent measures are necessary to ensure the normal operation of the sensing device. Furthermore, by setting a material selection decision matrix and a maintenance operation decision set, available coating remedial measures are identified, providing a selection space for choosing the optimal decision.

[0017] Preferably, the formula for calculating the degree of environmental threat in the set of state variable types for the operation phase described in S12 is denoted as the environmental threat degree formula; as follows:

[0018]

[0019] In the formula, b i1 b i2 These represent the data collected by the i-th type of sensing device within a limited space and their corresponding weight values, respectively.

[0020] By setting a formula for the degree of environmental threat, a calculation formula is provided for the subsequent calculation of the degree of environmental threat based on data collected by various sensing devices.

[0021] Preferably, step S2 includes the following steps:

[0022] S21. In conjunction with the set of state variable types for the operation stage, the current set of sensing devices, the current set of operation stages, the material selection decision matrix, and the maintenance operation decision set, collect several sets of historical data on the health status and environmental threat level of the surface coatings of various sensing devices at the initial and final moments of each stage during confined space operations, as well as equations of change over time, material selection decision data, and maintenance operation decision data, to obtain a historical surface coating health data matrix set, a historical environmental threat level change equation matrix, a historical material selection decision data matrix set, and a historical maintenance operation decision data matrix set;

[0023] S22. Using the historical surface coating health data matrix set, the historical environmental threat level change equation matrix, the historical material selection decision data matrix set, and the historical maintenance operation decision data matrix set, construct a coating health mapping model for each sensing device to obtain the final sensing device coating health mapping equation set.

[0024] Because the coating materials used on each type of sensing device are different, the corresponding coating health data varies depending on the operating environment, material selection decisions, and maintenance operation decisions. Therefore, by collecting multiple sets of historical data from confined space operations, a corresponding sensing device coating health mapping equation is constructed for the surface coating of each type of sensing device. This makes the mapping equation more accurate in mapping the surface coating health data of the corresponding sensing device, thereby ensuring more accurate optimization choices for each stage of the current confined space operation.

[0025] Preferably, step S22 includes the following steps:

[0026] S221. In conjunction with the historical surface coating health data matrix set, the historical environmental threat level change equation matrix, the historical material selection decision data matrix set, and the historical maintenance operation decision data matrix set, construct the initial sensing device coating health mapping equation set.

[0027] S222. Substitute the surface coating health data at the initial moment of the historical surface coating health data matrix set, the historical environmental threat level change equation matrix, the historical material selection decision data matrix set, and the historical maintenance operation decision data matrix set into the corresponding initial sensor device coating health mapping equation set for mapping, and obtain the initial mapping data matrix set of the historical surface coating health at the end of the period.

[0028] S223. Set the coating health data mapping error threshold; calculate the error data between the historical end surface coating health initial mapping data matrix set and the surface coating health data at the end of the stage corresponding to each type of sensing device in the historical surface coating health data matrix set, and obtain the end coating health data mapping error dataset.

[0029] When there is a data point in the dataset containing the final coating health data mapping error that is greater than or equal to the coating health data mapping error threshold, the initial sensing device coating health mapping equation corresponding to that data point is used as the sensing device coating health mapping equation to be adjusted. The adjustment of the sensing device coating health mapping equation to be adjusted continues until there is no data point in the dataset containing the final coating health data mapping error that is greater than or equal to the coating health data mapping error threshold. Otherwise, no adjustment is required.

[0030] By using the environmental threat level change equation, the surface coating health data at the initial stage, material selection decision data, and maintenance operation decision data as independent variables in the initial sensor device coating health mapping equation, it is possible to subsequently map the environmental threat level change equation, the surface coating health data at the initial stage, the material selection decision data, and the maintenance operation decision data to the health data at the stage reception time. By substituting the collected historical data into the corresponding constructed initial sensor device coating health mapping equation for pre-mapping, it is possible to detect whether the mapping accuracy of the constructed initial sensor device coating health mapping equation meets the requirements, and thus determine whether the initial sensor device coating health mapping equation needs to be adjusted.

[0031] Preferably, step S3 includes the following steps:

[0032] S31. In conjunction with the current set of post-coating sensing devices and the current set of operation stages, a current operation stage is set. Before the start of the current operation stage, data from multiple time points is collected using various types of sensing devices to obtain a historical sensing data matrix. In conjunction with the historical sensing data matrix, data within a limited space at multiple time points in the current operation stage is predicted to obtain a current operation sensing prediction data matrix.

[0033] S32. Based on the current operation sensor prediction data matrix and the environmental threat level formula, fit the equation for the change of environmental threat level over time during the current operation phase to obtain the environmental threat level change equation for the current operation phase; collect the surface coating health data of each sensor in the current coating sensor set at the initial moment of the current operation phase to obtain the initial surface coating health dataset for the current operation phase; then, in conjunction with the material selection decision matrix and maintenance operation decision set, preset the material selection decision data and maintenance operation decision data corresponding to each type of sensor in the current operation phase to obtain the initial material selection decision dataset and the initial maintenance operation decision dataset for the current phase.

[0034] S33. Substitute each data point from the current stage initial material selection decision dataset, the current stage initial maintenance operation decision dataset, the current operation stage initial surface coating health dataset, and the current operation stage environmental threat level change equation into the corresponding mapping equation in the final sensor device coating health mapping equation set to obtain the current operation stage final surface coating health dataset; optimize the current operation stage final surface coating health dataset to obtain the current operation stage final material selection decision dataset, the current operation stage final maintenance operation decision dataset, and the current operation stage final optimized surface coating health dataset.

[0035] S34. In conjunction with the current job stage set, take the next stage of the current job stage as the current job stage; then take the final optimized surface coating health dataset of the current job stage as the initial surface coating health dataset of the current job stage, and repeat S31, S32, and S33 until the current job stage is the last stage in the current job stage set.

[0036] By combining the final material selection decision dataset and the final maintenance operation decision dataset of the current operation stage obtained in each repetition process, the final material selection decision data matrix and the final maintenance operation decision data matrix of the current operation stage are obtained.

[0037] By optimizing the decisions regarding the coating at each stage, the surface coating health is maximized at the end of each stage, thereby ensuring the maximum uptime of various types of sensing devices. After all stages are completed, the accuracy of the data collected by each type of sensing device throughout the entire confined space operation process is optimized.

[0038] Preferably, in S31, a BP neural network model is used to predict the data within a limited space at multiple time points during the current operation phase;

[0039] Backpropagation (BP) neural networks use the backpropagation algorithm to update weights, which can automatically find the optimal parameters in the global scope, thereby improving the performance of the model. Based on this, by using BP neural networks to predict data in a limited space at multiple time points in the current operation phase, the quality of the predicted data is guaranteed.

[0040] Preferably, optimizing the final surface coating health dataset for the current operation stage in S33 includes the following steps:

[0041] S331. Set the value range of each initial material selection decision data and initial maintenance operation decision data in the current stage initial material selection decision dataset and the current stage initial maintenance operation decision dataset to obtain the current material selection decision data value range set and the current maintenance operation decision data value range set;

[0042] Decision data is used to adjust the bald eagle population; the maximum number of iterations for adjusting the bald eagle population using the decision data is set to [value missing]. And the current iteration number is These are respectively denoted as the maximum number of iterations for decision adjustment and the current number of iterations for decision adjustment; the search space dimension of the decision data adjustment for the bald eagle population is... same;

[0043] S332. Based on the current material selection decision data value range set and the current maintenance operation decision data value range set, set the decision data to adjust the initial position of each bald eagle in the bald eagle population to obtain the second initial position matrix set;

[0044] S333. Construct the fitness function of the bald eagle population based on the decision data;

[0045] S334. Begin iteration. Before each iteration, set the current iteration count of the decision adjustment to 1. During the first iteration, use the decision data to adjust the fitness function of the bald eagle population to calculate the fitness value of the initial position of each bald eagle in the second initial position matrix set, thus obtaining the third fitness value set. Take the largest fitness value in the third fitness value set and the corresponding initial position of the bald eagle as the third global best fitness and the third global best position, respectively. Update the initial position of each bald eagle in the second initial position matrix set according to the third global best fitness and the third global best position. After the update is completed, increment the current iteration count of the decision adjustment by 1 and proceed to the next iteration.

[0046] In each iteration, the fitness function of the vulture population adjusted by the decision data is used to calculate the fitness value of each vulture in the vulture population adjusted by the decision data updated in the previous iteration, resulting in a fourth fitness value set. The largest fitness value in the fourth fitness value set and the corresponding vulture position are taken as the fourth global best fitness and the fourth global best position, respectively. The position of each vulture in the vulture population adjusted by the decision data updated in the previous iteration is updated according to the fourth global best fitness and the fourth global best position. After the update is completed, the current iteration number of the decision adjustment is incremented by 1 and the next iteration is started.

[0047] S335, when If the condition is met, stop the iteration and obtain the second final global optimal position and the second final global optimal fitness; otherwise, continue the iteration until... Up to this point; the position components of the second final global optimal position in the dimensions of material selection decision data and maintenance operation decision data are respectively used as the final material selection decision dataset and the final maintenance operation decision dataset of the current operation stage; combined with the final material selection decision dataset, the final maintenance operation decision dataset of the current operation stage and the final sensor coating health mapping equation set, the final optimized surface coating health dataset of the current operation stage is obtained;

[0048] By employing the Bald Eagle optimization algorithm, the material selection decision data and maintenance operation decision data corresponding to each type of sensing device in the current operation phase are iteratively adjusted multiple times, and the sum of the health of the surface coatings of various types of sensing devices at the end of the current operation phase is used as the fitness function. Therefore, as the iteration progresses, the health of the surface coatings of various types of sensing devices at the end of the current operation phase becomes higher and higher, thereby ensuring that the data collected by various types of sensing devices in the current operation phase is more accurate.

[0049] Preferably, step S4 includes the following steps:

[0050] S41. In conjunction with the current work stage set, select the material selection decision data and maintenance operation decision data for each stage in the current confined space operation based on the final material selection decision data matrix and the final maintenance operation decision data matrix of the current work stage.

[0051] S42. After selection, each device in the current coated sensing device set is used to collect various types of data at each stage of the current confined space operation to obtain the current operation data matrix; based on the current operation data matrix, it is determined whether an early warning is needed.

[0052] When the current confined space operation actually begins, the optimized final material selection decision data matrix and the final maintenance operation decision data matrix of the current operation stage provide a basis for decision-making in the current confined space operation process, thereby ensuring the accuracy of the collected data, enabling accurate early warning, and timely reminding staff to take appropriate safety measures.

[0053] An integrated system for multi-parameter safety monitoring and early warning of confined space operations includes a current confined space operation equipment material setting module, a stage status setting module, a decision type setting module, a sensor coating health mapping equation construction module, a current confined space operation decision data optimization module, and a confined space parameter monitoring and early warning module.

[0054] The present invention has the following beneficial effects:

[0055] 1. In this invention, by applying a coating to the surface of each sensing device in a confined space operation, the effectiveness and accuracy of data collection by each sensing device in the confined space are ensured; wherein, by optimizing the maintenance decisions for the coating during the current confined space operation, the health of the coating is ensured to reach its optimal level at the end of each stage.

[0056] 2. In this invention, the Bald Eagle optimization algorithm is used to iteratively adjust multiple constant coefficients of the coating health mapping equation of the sensor device to be adjusted, and the mapping accuracy of the coating health mapping equation is used as the fitness function. Therefore, as the iteration proceeds, the mapping accuracy of the coating health mapping equation of the sensor device to be adjusted becomes higher and higher, and finally meets the mapping requirements.

[0057] 3. In this invention, by adopting the concept of dynamic programming, the surface coating health of each sensor at the end of each stage is used as the surface coating health of each sensor at the beginning of the next stage. Based on this, the material selection decision and maintenance operation decision are optimized to ensure that the health of the sensor coating reaches the best during the entire confined space operation.

[0058] 4. In this invention, the Bald Eagle optimization algorithm is used to iteratively adjust the material selection decision data and maintenance operation decision data corresponding to each type of sensing device in the current operation phase. As the iteration proceeds, the health of the surface coating of each type of sensing device at the end of the current operation phase becomes higher and higher, ensuring that the data collected by each type of sensing device in the current operation phase is more accurate.

[0059] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0060] To more clearly illustrate the technical solutions of the embodiments of the invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a flowchart illustrating the integrated method for multi-parameter safety monitoring and early warning in confined space operations according to the present invention.

[0062] Figure 2 This is a schematic diagram illustrating the process of constructing the final set of coating health mapping equations for the sensing device of this invention.

[0063] Figure 3 This is a schematic diagram illustrating the process of optimizing current material selection decision data and maintenance operation decision data according to the present invention;

[0064] Figure 4 This is a schematic diagram of a multi-parameter safety monitoring and early warning integrated system for confined space operations according to the present invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the invention, and not all embodiments. Based on the embodiments of the invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the invention.

[0066] Example 1

[0067] Please see Figure 1-3 This embodiment is an integrated method for multi-parameter safety monitoring and early warning in confined space operations, including the following steps:

[0068] S1. Set several types of sensing devices and corresponding protective coating types for monitoring the internal environment of the current limited space, and obtain the current set of sensing devices and the matrix of sensing device protective coating types.

[0069] S1 includes the following steps:

[0070] S11. Several types of sensing devices are set to monitor the environment inside the current confined space, resulting in a current sensing device set. The current sensing device set includes gas sensors, temperature sensors, and humidity sensors. Gas sensors can monitor the content of various gases inside the confined space, such as toxic gases, and issue an alarm when the toxic gas content exceeds a preset threshold. Temperature sensors can monitor the temperature inside the confined space and issue an alarm when the temperature exceeds a preset threshold. Humidity sensors can monitor the humidity inside the confined space and issue an alarm when the humidity exceeds a preset threshold. Several corresponding types are set for each type of current sensing device in the current sensing device set. The protective coating types are used to obtain a matrix of sensor device protective coating types. This matrix includes titanium dioxide coatings for gas sensors, which have good chemical stability and corrosion resistance, and can block gas molecule penetration to a certain extent. It can be prepared into a thin film using methods such as chemical vapor deposition. It also includes silicone resin coatings for temperature and humidity sensors, which have excellent heat resistance, weather resistance, and hydrophobic properties. A robust hydrophobic coating can be formed on the probe surface using electrostatic spraying. Using the sensor device protective coating type matrix and the current set of sensors, a coating is applied to each current sensor device to obtain the current coated set of sensors.

[0071] S12. Divide the current confined space operation cycle into stages to obtain the current operation stage set; set several stage state variable types for each current operation stage to obtain the operation stage state variable type set a = {a1, a2}, where a1, a2, and a3 represent the coating health and environmental threat level, respectively; the coating health can be quantified by the percentage reduction in coating thickness on the sensing device; the coating thickness can be measured by ultrasonic thickness measurement, etc.

[0072] The formula for calculating the degree of environmental threat in the set of state variable types for the operation phase described in S12 is denoted as the Environmental Threat Degree Formula; as follows.

[0073]

[0074] In the formula, b i1 b i2 These represent the data collected by the i-th type of sensing device within a limited space and their corresponding weight values, respectively.

[0075] S13. Based on the set of state variable types for the aforementioned work phase and the matrix of protective coating types for sensing devices, several operational decisions are set to obtain the material selection decision matrix a1′ and the maintenance operation decision set. a′ 2i This indicates setting the maintenance operation decision for the i-th type. This represents the total number of defined maintenance operation decision types; the values ​​in the maintenance operation decision set and the material selection decision matrix are all natural numbers. The maintenance operation decisions include cleaning, partial repair, and overall replacement, etc.; a1′ is as follows.

[0076]

[0077] Among them, a1′ ij This indicates that for the i-th type of sensing device, the j-th type of coating material is selected for coating. This represents the total number of coating material types specified for the i-th type of sensing device. This indicates the total number of sensor types configured for the current limited space;

[0078] S2. Collect historical data on confined space operations in conjunction with the sensor device protective coating type matrix and construct the final sensor device coating health mapping equation corresponding to each sensor device to obtain the final sensor device coating health mapping equation set.

[0079] S2 includes the following steps:

[0080] S21. In conjunction with the aforementioned set of state variable types for each work stage, the current set of sensing devices, the current set of work stages, the material selection decision matrix, and the maintenance operation decision set, collect several sets of historical data on the health status and environmental threat level of various sensing device surface coatings at the initial and final moments of each stage during confined space operations, along with equations showing the changes over time, material selection decision data, and maintenance operation decision data, to obtain a historical surface coating health data matrix set. Historical environmental threat level change equation matrix b2′, historical material selection decision data matrix set and historical maintenance operation decision data matrix set b1′ i b3′ i b4′ i Let represent the surface coating health matrix, material selection decision data matrix, and maintenance operation decision data matrix collected during the i-th historical data collection process of confined space operations, respectively. This represents the total number of groups involved in the historical data collection process within a confined space; b1′ i b2′, b3′i b4′ i They are as follows:

[0081]

[0082] Among them, b1′ ijk1 b1′ ijk2 They represent b1′ respectively i The surface coating health data of the j-th type of sensing device at the initial and final moments of the k-th stage; b2′ ik This represents the equation representing the change in environmental threat level during the k-th stage of a confined space operation based on the collected historical data from the i-th group; b3′ ijk Indicates b3′ i Material selection decision data for the j-th type of sensing device in the k-th stage; b4′ ijk Indicates b4′ i Maintenance operation decision data of the j-th type of sensing device in the k-th stage. This represents the total number of stages divided during the finite space operation for each history.

[0083] S22. Using the historical surface coating health data matrix set, the historical environmental threat level change equation matrix, the historical material selection decision data matrix set, and the historical maintenance operation decision data matrix set, construct a coating health mapping model for each sensing device to obtain the final sensing device coating health mapping equation set.

[0084] S22 includes the following steps:

[0085] S221. Using the historical surface coating health data matrix set, the historical environmental threat level change equation matrix, the historical material selection decision data matrix set, and the historical maintenance operation decision data matrix set, construct an initial sensing device coating health mapping equation set. c j Let the initial sensing device coating health mapping equation corresponding to the constructed j-th type of sensing device be as follows;

[0086] c′ j1 =c′ j2 (c3′,c′ j4 ,c′ j5 ,c′ j6 );

[0087] Where, c′ j1 For c j The dependent variable, c′, represents the health data of the surface coating of the j-th type of sensing device at the end of the stage. j2 c jMapping relationships, such as direct proportion, inverse proportion, and exponential relationships between independent and dependent variables; c3′, c′ j4 c′ j5 c′ j6 All are c j The independent variables represent the environmental threat level change equation for the stage, the health data of the surface coating of the j-th type of sensing device at the initial moment of the stage, the material selection decision data for the j-th type of sensing device in the stage, and the maintenance operation decision data, respectively.

[0088] S222. Substitute the surface coating health data at the initial moment of the historical surface coating health data matrix set, the historical environmental threat level change equation matrix, the historical material selection decision data matrix set, and the historical maintenance operation decision data matrix set into the corresponding initial sensor device coating health mapping equation set for mapping, and obtain the initial mapping data matrix set of the historical surface coating health at the end of the period. This represents the surface coating health data matrix at the end of each stage during the finite space operation process of the i-th historical data group obtained through mapping; as follows.

[0089]

[0090] in, express The surface coating health mapping data of the j-th type of sensing device at the end of the k-th stage;

[0091] S223. Set a coating health data mapping error threshold; calculate the error data between the historical end surface coating health initial mapping data matrix set and the surface coating health data at the end of each type of sensing device in the historical surface coating health data matrix set, to obtain the end coating health data mapping error dataset. This represents the error data between the initial mapping data matrix set of historical surface coating health at the end of the stage and the surface coating health data matrix set for the j-th type of sensing device at the end of the stage; the calculation formula is as follows.

[0092]

[0093] When there is a data point in the dataset containing the final coating health data mapping error that is greater than or equal to the coating health data mapping error threshold, the initial sensing device coating health mapping equation corresponding to that data point is used as the sensing device coating health mapping equation to be adjusted. The adjustment of the sensing device coating health mapping equation to be adjusted continues until there is no data point in the dataset containing the final coating health data mapping error that is greater than or equal to the coating health data mapping error threshold. Otherwise, no adjustment is required.

[0094] The adjustment of the coating health mapping equation of the sensor device to be adjusted in S223 includes the following steps:

[0095] S2231. Set the value ranges of several constant coefficients in the coating health mapping equation of the sensor device to be adjusted, and obtain the set of value ranges d1 for the coating health mapping constant coefficients; as follows.

[0096]

[0097] in, d' represents the lower limit and upper limit of the value of the i-th constant coefficient in the mapping equation of the health of the coating of the sensor device to be adjusted, respectively, and d' represents the total number of constant coefficients in the mapping equation of the health of the coating of the sensor device to be adjusted.

[0098] Construct a coating health mapping to adjust the bald eagle population; set the maximum number of iterations for the coating health mapping to adjust the bald eagle population to be [value missing]. And the current iteration number is These are denoted as the maximum number of iterations for mapping adjustment and the current number of iterations for mapping adjustment, respectively; the search space dimension of the coating health mapping adjustment for the vulture population is the same as that of d′;

[0099] S2232. Based on the set of values ​​for the coating health mapping constant coefficients, adjust the initial position of each bald eagle in the bald eagle population using the coating health mapping to obtain the first initial position matrix. as follows,

[0100]

[0101] in, e1 represents the position component of the initial position of the j-th bald eagle in the bald eagle population adjusted by the coating health mapping in the i-th constant coefficient dimension of the coating health mapping equation of the sensor device to be adjusted; e1 represents the size of the bald eagle population adjusted by the coating health mapping. The formula for generating is as follows:

[0102]

[0103] In the formula, rand 1ji Indicating targeting Generate random numbers between 0 and 1;

[0104] S2233. Construct the fitness function e1′ of the bald eagle population by mapping the coating health; as follows.

[0105]

[0106] In the formula, This means that a set of constant coefficients obtained in each iteration is substituted into the coating health mapping equation of the sensor device to be adjusted. Then, the surface coating health data at the initial moment of the historical surface coating health data matrix, the historical environmental threat level change equation matrix, the historical material selection decision data matrix set, and the corresponding data in the historical maintenance operation decision data matrix set are substituted into the coating health mapping equation of the sensor device to be adjusted to obtain the error between the data obtained and the actual data.

[0107] S2234. Begin iteration. Before each iteration, set the current iteration count of the mapping adjustment to 1. During the first iteration, use the coating health mapping adjustment fitness function e1′ to calculate the fitness value of the initial position of each bald eagle in the first initial position matrix, obtaining a first fitness value set. Take the largest fitness value in the first fitness value set and the corresponding initial position of the bald eagle as the first global best fitness and the first global best position, respectively. Update the initial position of each bald eagle in the first initial position matrix according to the first global best fitness and the first global best position. After the update is completed, increment the current iteration count of the mapping adjustment by 1 and proceed to the next iteration.

[0108] In each iteration, the fitness function e1′ of the bald eagle population adjusted by the coating health mapping is used to calculate the fitness value of the position of each bald eagle in the bald eagle population adjusted by the coating health mapping obtained in the previous iteration, resulting in a second fitness value set. The maximum fitness value in the second fitness value set and the corresponding bald eagle position are respectively taken as the second global best fitness and the second global best position. The position of each bald eagle in the bald eagle population adjusted by the coating health mapping obtained in the previous iteration is updated according to the second global best fitness and the second global best position. After the update is completed, the current iteration number of the mapping adjustment is incremented by 1 and the next iteration is started.

[0109] S2235, when If the first final global optimum is reached, stop the iteration and obtain the first final global optimum position and the first final global optimum fitness; otherwise, continue the iteration until... Up to this point; the first final global optimal fitness is used as the optimized end coating health data mapping error data; when the optimized end coating health data mapping error data is less than the coating health data mapping error threshold, each position component of the first final global optimal position is substituted into the coating health mapping equation of the sensor device to be adjusted and the corresponding mapping equation in the initial sensor device coating health mapping equation set is replaced; otherwise, return to S2234 to continue iterating until the optimized end coating health data mapping error data is less than the coating health data mapping error threshold;

[0110] As the number of iterations increases, the Vulture Optimization Algorithm dynamically adjusts its search strategy based on the current search situation, balancing global and local searches. It exhibits strong robustness and adaptability, effectively solving various complex numerical optimization problems, including continuous and discrete optimization problems. It is relatively insensitive to parameter selection; parameter changes within a certain range do not significantly affect the algorithm's performance. Based on these advantages, this scheme uses the Vulture Optimization Algorithm to iteratively adjust multiple constant coefficients of the mapping equation for the health of the sensor coating, using the mapping accuracy of this equation as the fitness function. Therefore, as the iterations proceed, the mapping accuracy of the mapping equation for the health of the sensor coating increases, ultimately satisfying the mapping requirements.

[0111] S3. Optimize the material selection decision data and maintenance operation decision data for each type of sensor in each stage by combining the final sensor coating health mapping equation set, and obtain the final material selection decision data matrix and the final maintenance operation decision data matrix for the current operation stage.

[0112] S3 includes the following steps:

[0113] S31. In conjunction with the current set of post-coating sensing devices and the current set of operation stages, a current operation stage is set. Before the start of the current operation stage, data from multiple time points is collected using various types of sensing devices to obtain a historical sensing data matrix. In conjunction with the historical sensing data matrix, data within a limited space at multiple time points in the current operation stage is predicted to obtain a current operation sensing prediction data matrix.

[0114] In S31, a BP neural network model is used to predict data within a limited space at multiple time points during the current operation phase.

[0115] S32. Based on the current operation sensor prediction data matrix and the environmental threat level formula, fit the equation for the change of environmental threat level over time during the current operation phase to obtain the environmental threat level change equation for the current operation phase; collect the surface coating health data of each sensor in the current coating sensor set at the initial moment of the current operation phase to obtain the initial surface coating health dataset for the current operation phase; then, in conjunction with the material selection decision matrix and maintenance operation decision set, preset the material selection decision data and maintenance operation decision data corresponding to each type of sensor in the current operation phase to obtain the initial material selection decision dataset and the initial maintenance operation decision dataset for the current phase.

[0116] S33. Substitute each data point from the current stage initial material selection decision dataset, the current stage initial maintenance operation decision dataset, the current operation stage initial surface coating health dataset, and the current operation stage environmental threat level change equation into the corresponding mapping equation in the final sensor device coating health mapping equation set to obtain the current operation stage final surface coating health dataset; optimize the current operation stage final surface coating health dataset to obtain the current operation stage final material selection decision dataset, the current operation stage final maintenance operation decision dataset, and the current operation stage final optimized surface coating health dataset.

[0117] S33 includes the following steps in optimizing the final surface coating health dataset for the current operation stage:

[0118] S331. Set the value range of each initial material selection decision data and initial maintenance operation decision data in the current stage initial material selection decision dataset and the current stage initial maintenance operation decision dataset to obtain the current material selection decision data value range set d2 and the current maintenance operation decision data value range set d3.

[0119]

[0120] in, These represent the lower limit and upper limit of the material selection decision data corresponding to the current sensing device of the i-th type, respectively; These represent the lower limit and upper limit of the maintenance operation decision data corresponding to the current sensing device of the i-th type, respectively;

[0121] Decision data is used to adjust the bald eagle population; the maximum number of iterations for adjusting the bald eagle population using the decision data is set to [value missing]. And the current iteration number is These are respectively denoted as the maximum number of iterations for decision adjustment and the current number of iterations for decision adjustment; the search space dimension of the decision data adjustment for the bald eagle population is... same;

[0122] S332. Based on the current material selection decision data value interval set and the current maintenance operation decision data value interval set, set the decision data to adjust the initial position of each bald eagle in the bald eagle population, and obtain the second initial position matrix set. e1 represents the initial position matrix of the i-th bald eagle in the bald eagle population adjusted by the decision data, and e2 represents the size of the bald eagle population adjusted by the decision data. as follows,

[0123]

[0124] in, They represent The positional components in the dimensions of material selection decision data and maintenance operation decision data corresponding to the current sensing device of type i are generated using the following formulas:

[0125]

[0126] In the formula, rand 2j1i rand 2j2i They represent respectively targeting Generate random numbers between 0 and 1;

[0127] S333. Construct the fitness function e′2 of the bald eagle population based on the decision data; as follows.

[0128]

[0129] Where f represents the surface coating health data of the i-th type of sensing device at the end time of the current operation phase;

[0130] S334. Begin iteration. Before each iteration, set the current iteration count of the decision adjustment to 1. During the first iteration, use the decision data to adjust the fitness function e′2 of the bald eagle population to calculate the fitness value of the initial position of each bald eagle in the second initial position matrix set, thus obtaining the third fitness value set. Take the largest fitness value in the third fitness value set and the corresponding initial position of the bald eagle as the third global best fitness and the third global best position, respectively. Update the initial position of each bald eagle in the second initial position matrix set according to the third global best fitness and the third global best position. After the update is completed, increment the current iteration count of the decision adjustment by 1 and proceed to the next iteration.

[0131] In each iteration, the fitness function e′2 of the bald eagle population is used to adjust the position of each bald eagle in the population based on the decision data updated in the previous iteration, resulting in a fourth fitness value set. The largest fitness value in the fourth fitness value set and the corresponding bald eagle position are taken as the fourth global best fitness and the fourth global best position, respectively. The position of each bald eagle in the population is updated based on the fourth global best fitness and the fourth global best position. After the update is completed, the current iteration number of the decision adjustment is incremented by 1 and the next iteration begins.

[0132] S335, when If the condition is met, stop the iteration and obtain the second final global optimal position and the second final global optimal fitness; otherwise, continue the iteration until... Up to this point; the position components of the second final global optimal position in the dimensions of material selection decision data and maintenance operation decision data are respectively used as the final material selection decision dataset and the final maintenance operation decision dataset of the current operation stage; combined with the final material selection decision dataset, the final maintenance operation decision dataset of the current operation stage and the final sensor coating health mapping equation set, the final optimized surface coating health dataset of the current operation stage is obtained;

[0133] S34. In conjunction with the current job stage set, take the next stage of the current job stage as the current job stage; then take the final optimized surface coating health dataset of the current job stage as the initial surface coating health dataset of the current job stage, and repeat S31, S32, and S33 until the current job stage is the last stage in the current job stage set.

[0134] By combining the final material selection decision dataset and the final maintenance operation decision dataset of the current operation stage obtained in each repetition process, the final material selection decision data matrix and the final maintenance operation decision data matrix of the current operation stage are obtained.

[0135] S4. In conjunction with the final material selection decision data matrix of the current operation stage, the final maintenance operation decision data matrix of the current operation stage, and the current set of sensing devices, select the coating decision for the surface coating of each type of sensing device, and collect and issue warnings for the data in the current limited space.

[0136] S4 includes the following steps:

[0137] S41. In conjunction with the current work stage set, select the material selection decision data and maintenance operation decision data for each stage in the current confined space operation based on the final material selection decision data matrix and the final maintenance operation decision data matrix of the current work stage.

[0138] S42. After selection, each device in the current coated sensing device set is used to collect various types of data at each stage of the current confined space operation to obtain the current operation data matrix; based on the current operation data matrix, it is determined whether an early warning is needed.

[0139] Example 2

[0140] Please see Figure 4 This embodiment discloses an integrated system for multi-parameter safety monitoring and early warning of confined space operations. The system can implement the methods of the above embodiments, including a current confined space operation equipment material setting module, a stage state setting module, a decision type setting module, a sensor equipment coating health mapping equation construction module, a current confined space operation decision data optimization module, and a confined space parameter monitoring and early warning module.

[0141] The current confined space operation equipment material setting module sets several types of sensing devices and corresponding protective coating types for monitoring the internal environment of the current confined space, thereby obtaining the current sensing device set and the sensing device protective coating type matrix.

[0142] The stage status setting module sets several stage status variable types for each current work stage, thus obtaining a set of work stage status variable types.

[0143] The decision type setting module sets several operational decisions for confined space operations, resulting in a material selection decision matrix and a maintenance operation decision set.

[0144] The sensor device coating health mapping equation construction module, together with the sensor device protective coating type matrix, the operation stage state variable type set, the material selection decision matrix, and the maintenance operation decision set, collects historical data on confined space operations and constructs the final sensor device coating health mapping equation corresponding to each type of sensor device, thus obtaining the final sensor device coating health mapping equation set.

[0145] The current confined space operation decision data optimization module, in conjunction with the final sensor equipment coating health mapping equation set, material selection decision matrix, and maintenance operation decision set, optimizes the material selection decision data and maintenance operation decision data corresponding to each type of sensor equipment in each stage before the start of the current operation stage, so as to obtain the final material selection decision data matrix and the final maintenance operation decision data matrix of the current operation stage.

[0146] The confined space parameter monitoring and early warning module, in conjunction with the final material selection decision data matrix of the current operation stage, the final maintenance operation decision data matrix of the current operation stage, and the current set of sensing devices, selects the coating decision for the surface coating of each type of sensing device, and collects and issues early warnings for the current confined space data.

[0147] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0148] The preferred embodiments of the invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention.

Claims

1. A multi-parameter safety monitoring and early warning integrated method for confined space operations, characterized in that, Includes the following steps: S1. Set several types of sensing devices and corresponding protective coating types for monitoring the internal environment of the current limited space, and obtain the current set of sensing devices and the matrix of sensing device protective coating types. Specifically, this includes: setting several types of sensing devices to monitor the internal environment of the current confined space, obtaining a current set of sensing devices; setting several corresponding protective coating types for each current sensing device in the current set of sensing devices, obtaining a matrix of sensing device protective coating types; applying a coating to each current sensing device in conjunction with the matrix of sensing device protective coating types and the current set of sensing devices, obtaining a current set of coated sensing devices; dividing the current confined space operation cycle into stages, obtaining a set of current operation stages; setting several stage state variable types for each current operation stage, obtaining a set of operation stage state variable types; and setting several operation decisions in conjunction with the set of operation stage state variable types and the matrix of sensing device protective coating types, obtaining a material selection decision matrix and a set of maintenance operation decisions. S2. Collect historical data on confined space operations in conjunction with the sensor device protective coating type matrix and construct the final sensor device coating health mapping equation corresponding to each sensor device to obtain the final sensor device coating health mapping equation set. S3. Optimize the material selection decision data and maintenance operation decision data for each type of sensor in each stage by combining the final sensor coating health mapping equation set, and obtain the final material selection decision data matrix and the final maintenance operation decision data matrix for the current operation stage. S4. In conjunction with the final material selection decision data matrix of the current operation stage, the final maintenance operation decision data matrix of the current operation stage, and the current set of sensing devices, select the coating decision for the surface coating of each type of sensing device, and collect and issue warnings for the data in the current limited space.

2. The integrated method for multi-parameter safety monitoring and early warning in confined space operations according to claim 1, characterized in that, S2 includes the following steps: S21. In conjunction with the set of state variable types for the operation stage, the current set of sensing devices, the current set of operation stages, the material selection decision matrix, and the maintenance operation decision set, collect several sets of historical data on the health of the surface coatings of various sensing devices at the initial and final moments of each stage during confined space operations, as well as equations of changes in environmental threat levels over time, material selection decision data, and maintenance operation decision data. Construct a coating health mapping model for each type of sensing device to obtain the final set of sensing device coating health mapping equations.

3. The integrated method for multi-parameter safety monitoring and early warning in confined space operations according to claim 2, characterized in that, S21 includes the following steps: S211. Construct an initial set of mapping equations for the health of the coating of the sensing device; S212. Substitute the equations of change of the health status and environmental threat level of the surface coatings of various sensing devices at the initial moment of each stage in S21, the material selection decision data, and the maintenance operation decision data into the corresponding initial sensing device coating health status mapping equations in the initial sensing device coating health status mapping equation set to obtain the initial mapping data matrix set of the surface coating health status at the end of history. S213. Set the coating health data mapping error threshold and adjust the coating health mapping equation of the sensor device to be adjusted.

4. The integrated method for multi-parameter safety monitoring and early warning in confined space operations according to claim 3, characterized in that: In S213, the Bald Eagle optimization algorithm is used to adjust the mapping equation of the coating health of the sensor device to be adjusted.

5. The integrated method for multi-parameter safety monitoring and early warning in confined space operations according to claim 3, characterized in that, S3 includes the following steps: S31. In conjunction with the current set of post-coating sensing devices and the current set of work stages, a current work stage is set. Before the start of the current work stage, various types of sensing devices are used to collect data at multiple time points and to predict the data in the limited space at multiple time points in the current work stage, so as to obtain the current work sensing prediction data matrix. S32. Based on the current operation sensor prediction data matrix, fit the equation of environmental threat level change over time during the current operation phase to obtain the environmental threat level change equation for the current operation phase; collect the surface coating health data of each sensor in the current coating sensor set at the initial moment of the current operation phase to obtain the initial surface coating health dataset for the current operation phase; then preset the material selection decision data and maintenance operation decision data corresponding to each type of sensor in the current operation phase to obtain the initial material selection decision dataset and the initial maintenance operation decision dataset for the current phase. S33. Substitute each data point from the current stage initial material selection decision dataset, the current stage initial maintenance operation decision dataset, the current operation stage initial surface coating health dataset, and the current operation stage environmental threat level change equation into the corresponding mapping equation in the final sensor device coating health mapping equation set to obtain the current operation stage final surface coating health dataset; optimize the current operation stage final surface coating health dataset to obtain the current operation stage final material selection decision dataset, the current operation stage final maintenance operation decision dataset, and the current operation stage final optimized surface coating health dataset. S34. In conjunction with the current job stage set, take the next stage of the current job stage as the current job stage; then take the final optimized surface coating health dataset of the current job stage as the initial surface coating health dataset of the current job stage, and repeat S31, S32, and S33 until the current job stage is the last stage in the current job stage set. The final material selection decision dataset and the final maintenance operation decision dataset of the current operation stage obtained in each repetition are combined to obtain the final material selection decision data matrix and the final maintenance operation decision data matrix of the current operation stage.

6. The integrated method for multi-parameter safety monitoring and early warning in confined space operations according to claim 5, characterized in that: In S31, a BP neural network model is used to predict the data within a limited space at multiple time points during the current operation phase.

7. The integrated method for multi-parameter safety monitoring and early warning in confined space operations according to claim 5, characterized in that, S33 includes the following steps in optimizing the final surface coating health dataset for the current operation stage: S331. Construct decision data to adjust the bald eagle population; set the maximum number of iterations for adjusting the bald eagle population using the decision data as follows: And the current iteration number is These are denoted as the maximum number of iterations for decision adjustment and the current number of iterations for decision adjustment, respectively. S332. Set decision data to adjust the initial position of each vulture in the vulture population to obtain the second initial position matrix set; S333. Construct the fitness function of the bald eagle population based on the decision data; S334. Start the iteration; in each iteration, use the decision data to adjust the fitness function of the vulture population, calculate the fitness value of the position of each vulture in the vulture population adjusted by the decision data updated in the previous iteration, and update the position of each vulture in the vulture population adjusted by the decision data updated in the previous iteration. S335, when If the condition is met, stop the iteration and obtain the second final global optimal position and the second final global optimal fitness; otherwise, continue the iteration until... Up to the point of time; the position components of the second final global optimal position in the dimensions of material selection decision data and maintenance operation decision data are respectively used as the final material selection decision dataset and the final maintenance operation decision dataset of the current operation stage; combined with the final material selection decision dataset, the final maintenance operation decision dataset, and the final sensor coating health mapping equation set, the final optimized surface coating health dataset of the current operation stage is obtained.

8. The integrated method for multi-parameter safety monitoring and early warning in confined space operations according to claim 7, characterized in that, S4 includes the following steps: S41. In conjunction with the current work stage set, select the material selection decision data and maintenance operation decision data for each stage in the current confined space operation based on the final material selection decision data matrix and the final maintenance operation decision data matrix of the current work stage. S42. After selection, each device in the current coated sensing device set is used to collect various types of data at each stage of the current confined space operation to obtain the current operation data matrix; based on the current operation data matrix, it is determined whether an early warning is needed.

9. A system for implementing the integrated method for multi-parameter safety monitoring and early warning of confined space operations as described in any one of claims 1-8.

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