A steam cabinet alarm control system based on pattern recognition

Abstract

The present invention discloses a steamer alarm control system based on pattern recognition, which belongs to the field of computer data processing technology. The system includes: a data acquisition module, a model construction module, an auxiliary monitoring module, a model optimization module, and a pattern judgment module. The present invention uses the data acquisition module to comprehensively acquire the historical working status data and real-time internal monitoring data of the steamer and perform preprocessing; the model construction module builds a pattern recognition model with preliminary recognition capabilities based on the preprocessed data; at the same time, the auxiliary monitoring module continuously monitors and generates characteristic values ​​of detailed changes, and provides a model optimization module to continuously polish the pattern recognition model to generate a pattern recognition optimization model, thereby comprehensively collecting and analyzing various types of steamer operation information, accurately capturing various state changes during its operation, and then realizing accurate recognition and judgment of the steamer working mode, solving the problem of insufficient comprehensive intelligent monitoring of steamers in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of computer data processing, and in particular to a steam cabinet alarm control system based on pattern recognition. Background Art

[0002] With the booming catering industry and the continuous expansion of industrialized food processing, steamers are widely used as efficient and stable cooking and processing equipment. From traditional restaurants and bars that mass-produce staples and steamed dishes to food processing plants that steam a variety of ingredients, steamers are used very frequently. However, early steamers lacked effective monitoring during operation, often leading to safety accidents due to abnormal steam pressure, dry boiling due to water shortages, and overheating. These accidents not only damaged the equipment but also endangered the lives of operators and caused economic losses. To ensure production safety and improve equipment stability, steamer alarm control technology was developed. It is designed to monitor key equipment operating parameters in real time, provide timely warnings of abnormalities, and ensure reliable operation of the steamer.

[0003] Steamer alarm control has made significant progress. Sensor technology has become increasingly mature, with high-precision pressure, temperature, and water level sensors now widely integrated into steamer systems. These sensors can accurately collect operational data with minimal error, providing a solid foundation for precise alarms. Furthermore, the intelligence of control modules has significantly increased. Microprocessor-based control systems can rapidly process and analyze sensor data. If data deviates from normal thresholds, alerts are immediately issued through various means, including audible and visual alarms, pop-up displays, and even remote push notifications to managers' phones. Some advanced steamers can also automatically implement emergency control measures, such as adjusting steam intake and activating water replenishment, achieving initial automated operation and maintenance.

[0004] However, in the process of implementing the technical solutions of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0005] Traditional steamers typically only offer basic heating and steaming functions, and the lack of intelligent monitoring systems exposes numerous operational shortcomings. This lack of intelligent monitoring makes it difficult to accurately monitor dynamic changes within the steamer, making it difficult to detect potential faults. When the steamer encounters an abnormality, operators lack timely access to relevant information, which can easily lead to steaming failures and negatively impact food quality. Prolonged abnormal operation can even damage the equipment, increasing repair costs and extending downtime.

[0006] In summary, there is a problem that the intelligent monitoring of the steam cabinet is not comprehensive enough. Summary of the Invention

[0007] The present invention is proposed in view of the above-mentioned problems. Therefore, the problem to be solved by the present invention is that the existing technology has difficulty in accurately controlling the dynamic changes inside the steam cabinet and timely handling abnormal conditions, and there is a problem that the intelligent monitoring of the steam cabinet is not comprehensive enough.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: a steam cabinet alarm control system based on pattern recognition, comprising: a data acquisition module, a model building module, an auxiliary monitoring module, a model optimization module, and a pattern judgment module;

[0009] The data acquisition module is used to obtain historical working status data of the steam cabinet, collect internal monitoring data of the steam cabinet in real time, and pre-process the historical working status data and internal monitoring data of the steam cabinet;

[0010] The model building module is used to build a pattern recognition model based on the pre-processed historical working status data;

[0011] The auxiliary monitoring module is used to obtain detailed change characteristic values ​​based on the pre-processed steam cabinet internal monitoring data, wherein the detailed change characteristic values ​​include: temperature change characteristic values, pressure change characteristic values, water level change characteristic values, steam flow change characteristic values, and power change characteristic values;

[0012] Create an empty data receiving buffer and continuously store the detail change feature value through the data receiving buffer;

[0013] When the amount of data accumulated in the data receiving buffer reaches a first threshold, extracting an array corresponding to the detail change feature value, sending the array corresponding to the detail change feature value to the model optimization module, and clearing the data receiving buffer;

[0014] The model optimization module is used to obtain an array corresponding to the detail change characteristic value, and generate a pattern recognition optimization model according to the array corresponding to the detail change characteristic value;

[0015] The pattern judgment alarm module is used to obtain the current internal monitoring data of the steam cabinet and input the current internal monitoring data of the steam cabinet into the pattern recognition optimization model. When the pattern recognition optimization model outputs that the working mode of the steam cabinet is normal mode, the internal monitoring data of the steam cabinet is continuously monitored. When the output working mode of the steam cabinet is abnormal mode, an alarm is triggered.

[0016] As a preferred solution of the steam cabinet alarm control system based on pattern recognition described in the present invention, the calculation formula of the temperature change characteristic value is:

[0017]

[0018] in, is the temperature value at time t1, is the temperature value at time t2, Tavg is the average temperature, Trange is the allowable difference of normal temperature fluctuation, e is a natural constant, and π is the circumference of a circle.

[0019] As a preferred solution of the steam cabinet alarm control system based on pattern recognition described in the present invention, the calculation formula of the pressure change characteristic value is:

[0020]

[0021] Where, is the pressure value at time t1, is the pressure value at time t2, Pavg is the average pressure, and Prange is the allowable difference of normal pressure fluctuation.

[0022] As a preferred solution of the steam cabinet alarm control system based on pattern recognition described in the present invention, the calculation formula of the water level change characteristic value is:

[0023]

[0024] Where, is the water level value at time t1, is the water level value at time t2, Havg is the average water level, and Hrange is the allowable difference of normal fluctuation of water level.

[0025] As a preferred solution of the steam cabinet alarm control system based on pattern recognition described in the present invention, the calculation formula of the steam flow change characteristic value is:

[0026]

[0027] Where, is the steam flow rate at time t1, is the steam flow rate value at time t2, Favg is the average steam flow rate over a period of time, and Frange is the allowable difference in normal fluctuation of steam flow rate.

[0028] As a preferred solution of the steam cabinet alarm control system based on pattern recognition described in the present invention, the calculation formula of the power change characteristic value is:

[0029]

[0030] Where, is the power value at time t1, is the power value at time t2, Wavg is the historical average power, and Wrange is the allowable difference in power fluctuation.

[0031] As a preferred solution of the steam cabinet alarm control system based on pattern recognition described in the present invention, the specific process of constructing the pattern recognition model is as follows:

[0032] Extracting normal working state data and abnormal working state data of the steam cabinet from the historical working state data;

[0033] Preprocessing normal working state data and abnormal working state data;

[0034] Divide the preprocessed dataset into training set and validation set;

[0035] Use the training set to train the multi-layer perceptron architecture and set the initial learning rate X 101 , momentum coefficient X2 and number of iterations X 301 , through the back-propagation algorithm, the connection weights between the neurons in each layer of the model are continuously adjusted, so that the loss function value of the model on the validation set is gradually reduced;

[0036] When the validation set loss stops decreasing after N consecutive iterations, training is stopped and the model parameters at this time are saved as the pattern recognition model.

[0037] As a preferred solution of the steam cabinet alarm control system based on pattern recognition described in the present invention, the formula of the pattern recognition model is as follows:

[0038] Assume that the multilayer perceptron has L layers, the number of neurons in the input layer is n0, and the corresponding input vector represents the preprocessed historical working status feature data. The number of neurons in the hidden layer l (1≤l≤L-1) is nl, the number of neurons in the output layer is nL, and the output vector y = (y1, y2) represents the probability value of the steam cabinet being in normal and abnormal working modes respectively;

[0039] For the hidden layer l, the activation value hl of its neurons is calculated as follows:

[0040] hl=σ(Wlhl- 1 +bl);

[0041] Where Wl is the weight matrix connecting the l-1th layer and the lth layer neurons, with a dimension of nl×nl-1, bl is the bias vector of the lth layer, with a dimension of nl, σ is the ReLU activation function, that is, σ(z)=max(0,z), z refers to the weighted input of the neuron, z=Wlhl- 1 +bl;

[0042] The calculation from the input layer to the hidden layer 1 is: h 1 =σ(W 1 x+b 1 );

[0043] After passing through multiple hidden layers, it reaches the output layer. The calculation of the output layer is normalized using the Softmax function. The pattern recognition model obtains the final prediction result yj through the following formula:

[0044]

[0045] Among them, e is a natural constant, zj is the first transfer function, is the output layer weight matrix W L The j-th row transpose vector of , bj is the bias of the corresponding neuron in the output layer, j = 1, 2, z k The k in the output layer is the index used to traverse the neurons, z k is the second transfer function, is the output layer weight matrix W L The row vector transpose of the connection weights corresponding to the k-th output layer neuron, bk is the bias of the output layer corresponding to the k-th output layer neuron, nL is the number of output layer neurons, nL=2, k traverses from 1 to nL.

[0046] As a preferred solution of the steam cabinet alarm control system based on pattern recognition described in the present invention, generating a pattern recognition optimization model according to an array corresponding to the detail change characteristic value includes the following steps:

[0047] After receiving the arrays corresponding to the detail change feature values ​​from the auxiliary monitoring module, these arrays are first normalized to obtain the feature value arrays;

[0048] The standardized eigenvalue array is input into the constructed pattern recognition model as the input data for secondary training. It is combined with the historical working status data, and the training set and test set are divided again. The secondary training process based on dynamic learning rate adjustment is started, and the learning rate is set to X. 102 , every time after X 302 After iterations, if the accuracy of the pattern recognition model on the test set is improved by less than X4, the learning rate is decayed to X5 times the original value.

[0049] At the same time, the loss function value on the test set is continuously monitored and weighted adjustments are performed. When the loss function value no longer decreases after X6 consecutive iterations, the secondary training is stopped to obtain the pattern recognition optimization model.

[0050] As a preferred solution of the steam cabinet alarm control system based on pattern recognition described in the present invention, the specific process of continuously monitoring the loss function value on the test set and performing weighted adjustment is:

[0051] During the secondary training process, when a sample is marked as an abnormal mode, the loss weight Lweighted of the corresponding sample is increased to twice that of the normal sample in the loss function calculation, that is:

[0052] Lweighted = w*Lce;

[0053] Among them, Lce is the original cross entropy loss and w is the weight coefficient.

[0054] The beneficial effects of the present invention are as follows: the data acquisition module of the present invention comprehensively acquires the historical working status data and real-time internal monitoring data of the steamer, and performs preprocessing. At the same time, the auxiliary monitoring module continuously monitors and generates characteristic values ​​of detail changes, and the model construction module and the optimization module continuously polish the pattern recognition model, thereby comprehensively collecting and analyzing various types of information on the operation of the steamer, accurately capturing various state changes in its operation, and then realizing accurate identification and judgment of the working mode of the steamer, effectively solving the problem of insufficient comprehensive intelligent monitoring of the steamer in the existing technology.

[0055] The present invention continuously updates the process of data acquisition, model optimization and pattern judgment in a cycle, that is, continuously obtains new monitoring data for model optimization, and then applies the optimized model to real-time judgment, thereby ensuring that the system can always adapt to various changes in the operation of the steamer, thereby realizing long-term stable operation of the system and effective monitoring of the entire life cycle of the steamer.

[0056] The present invention standardizes the array of detail change eigenvalues ​​in the model optimization module and then inputs the result into the constructed model for secondary training. It also adopts a dynamic learning rate adjustment strategy and a weighted adjustment loss function, so that the model can keep up with the real-time operating status changes of the steam cabinet and continuously optimize its own performance, thereby achieving the effect of improving the model's sensitivity and accuracy in identifying abnormal working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 This is a structural diagram of a steam cabinet alarm control system based on pattern recognition in Example 1. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0060] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0061] The embodiment of the present application solves the problem of insufficiently comprehensive intelligent monitoring of steamers in the prior art by providing a steamer alarm control system based on pattern recognition. The data acquisition module comprehensively collects the historical working status data and real-time internal monitoring data of the steamer, and performs preprocessing to ensure the quality and availability of the data. The auxiliary monitoring module then uses specific formulas to calculate the detailed change characteristic values ​​of key parameters such as temperature, pressure, water level, steam flow, and power, accurately capturing the subtle state fluctuations in the operation of the steamer. Finally, with the help of the model building module and the optimization module, the pattern recognition model is constructed and continuously improved, enabling it to deeply explore the complex patterns and potential laws in the data. Accurate identification and judgment of the steamer working mode is achieved.

[0062] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0063] Example 1

[0064] Reference Figure 1 , which is the first embodiment of the present invention, provides a steam cabinet alarm control system based on pattern recognition, the system comprising: a data acquisition module, a model building module, an auxiliary monitoring module, a model optimization module, and a pattern judgment module;

[0065] The data acquisition module is used to obtain the historical working status data of the steam cabinet, collect the internal monitoring data of the steam cabinet in real time, and pre-process the historical working status data and the internal monitoring data of the steam cabinet;

[0066] A model building module is used to build a pattern recognition model based on the pre-processed historical working status data;

[0067] An auxiliary monitoring module is used to obtain detailed change characteristic values ​​based on the pre-processed internal monitoring data of the steam cabinet. The detailed change characteristic values ​​include: temperature change characteristic values, pressure change characteristic values, water level change characteristic values, steam flow change characteristic values, and power change characteristic values;

[0068] Create an empty data receiving buffer and continuously store the detail change feature value through the data receiving buffer;

[0069] When the amount of data accumulated in the data receiving buffer reaches a first threshold, extracting an array corresponding to the detail change feature value, sending the array corresponding to the detail change feature value to the model optimization module, and clearing the data receiving buffer;

[0070] A model optimization module is used to obtain an array corresponding to the detail change characteristic value and generate a pattern recognition optimization model according to the array corresponding to the detail change characteristic value;

[0071] The pattern judgment alarm module is used to obtain the current internal monitoring data of the steam cabinet and input the current internal monitoring data of the steam cabinet into the pattern recognition optimization model. When the pattern recognition optimization model outputs that the working mode of the steam cabinet is normal mode, the internal monitoring data of the steam cabinet is continuously monitored. When the output working mode of the steam cabinet is abnormal mode, an alarm is triggered.

[0072] In this embodiment, the data acquisition module is closely connected with various sensors of the steam cabinet. It not only collects the historical working status data of the steam cabinet in an all-round manner and deeply mines the normal and abnormal operating rules contained therein, but also collects the key monitoring data inside the steam cabinet in real time. The preprocessing includes data cleaning, normalization and other technologies.

[0073] The auxiliary monitoring module captures subtle changes inside the steam cabinet in real time, and transmits the collected detail change feature values ​​to the model optimization module in a timely manner, promoting continuous optimization of the model and continuously improving the accuracy of judgment.

[0074] The mode judgment alarm module quickly and accurately determines the working mode of the steamer based on the output results of the optimized model. Once an abnormal situation is detected, the alarm mechanism is immediately triggered to notify the operator to deal with it in time, realizing all-round and intelligent monitoring and management of the steamer. The safety and reliability of the steamer operation are significantly improved, which can not only effectively avoid problems such as food waste and equipment damage caused by malfunctions, but also reduce the high costs and failure risks derived from frequent maintenance.

[0075] Furthermore, the calculation formula of the temperature change characteristic value is:

[0076]

[0077] in, is the temperature value at time t1, is the temperature at time t2, Tavg is the average temperature, Trange is the allowable normal temperature fluctuation, e is a natural constant, and π is the circumference of the circle. For steamers, which require strict temperature control, this feature can detect subtle signs of temperature runaway in advance, effectively preventing poor steaming or equipment failure caused by temperature anomalies.

[0078] Furthermore, the calculation formula of the pressure change characteristic value is:

[0079]

[0080] Where, is the pressure value at time t1, is the pressure value at time t2, Pavg is the average pressure, and Prange is the allowable normal pressure fluctuation. Accurate pressure fluctuation characteristics ensure timely detection of even the slightest pressure anomaly, ensuring a uniform and stable steaming process and preventing undercooked or overcooked food due to pressure issues.

[0081] Furthermore, the calculation formula of the water level change characteristic value is:

[0082]

[0083] Where, is the water level value at time t1, is the water level at time t2, Havg is the average water level, and Hrange is the allowable normal water level fluctuation. The steamer water level is directly related to steam production and steaming time. Accurate water level characteristic values ​​help promptly detect problems such as water tank leaks and abnormal water replenishment, ensuring a smooth steaming process and preventing damage to the equipment due to water shortage or excessive water overflow.

[0084] Furthermore, the calculation formula of the steam flow rate change characteristic value is:

[0085]

[0086] Where, is the steam flow rate at time t1, is the steam flow rate at time t2, Favg is the average steam flow rate over the period, and Frange is the allowable normal fluctuation in steam flow rate. Stable steam flow ensures even heating of food. This characteristic value instantly detects any anomalies in steam flow rate, such as steam generator failure or pipe blockage, ensuring steaming quality.

[0087] Furthermore, the calculation formula of the power variation characteristic value is:

[0088]

[0089] Where, is the power value at time t1, is the power value at time t2, Wavg is the historical average power, and Wrange is the allowable power fluctuation. Accurate power characteristic values ​​can promptly detect problems such as heating element aging and unstable power supply voltage, ensuring efficient and stable operation of the steamer, reducing energy consumption and maintenance costs.

[0090] Furthermore, the specific process of constructing the pattern recognition model is as follows:

[0091] Extracting normal working state data and abnormal working state data of the steam cabinet from the historical working state data;

[0092] Preprocessing normal working state data and abnormal working state data;

[0093] Divide the preprocessed dataset into training set and validation set;

[0094] Use the training set to train the multi-layer perceptron architecture and set the initial learning rate X 101 , momentum coefficient X2 and number of iterations X 301 , through the back-propagation algorithm, the connection weights between the neurons in each layer of the model are continuously adjusted, so that the loss function value of the model on the validation set is gradually reduced, including cross entropy loss, mean square error loss, etc.;

[0095] When the validation set loss stops decreasing after N consecutive iterations, training is stopped and the model parameters at this time are saved as the pattern recognition model.

[0096] Furthermore, the formula of the pattern recognition model is as follows:

[0097] Assume that the multilayer perceptron has L layers, the number of neurons in the input layer is n0, and the corresponding input vector represents the preprocessed historical working status feature data. The number of neurons in the hidden layer l (1≤l≤L-1) is nl, the number of neurons in the output layer is nL, and the output vector y = (y1, y2) represents the probability value of the steam cabinet being in normal and abnormal working modes respectively;

[0098] For the hidden layer l, the activation value hl of its neurons is calculated as follows:

[0099] hl=σ(Wlhl- 1 +bl);

[0100] Where Wl is the weight matrix connecting the l-1th layer and the lth layer neurons, with a dimension of nl×nl-1, bl is the bias vector of the lth layer, with a dimension of nl, σ is the ReLU activation function, that is, σ(z)=max(0,z), z refers to the weighted input of the neuron, z=Wlhl- 1 +bl;

[0101] The calculation from the input layer to the hidden layer 1 is: h 1 =σ(W 1 x+b 1 );

[0102] After passing through multiple hidden layers, it reaches the output layer. The calculation of the output layer is normalized using the Softmax function. The pattern recognition model obtains the final prediction result yj through the following formula:

[0103]

[0104] Among them, e is a natural constant, zj is the first transfer function, is the output layer weight matrix W L The j-th row transpose vector of , bj is the bias of the corresponding neuron in the output layer, j = 1, 2, z k The k in the output layer is the index used to traverse the neurons, z k is the second transfer function, is the output layer weight matrix W L The row vector transpose of the connection weights corresponding to the k-th output layer neuron, bk is the bias of the output layer corresponding to the k-th output layer neuron, nL is the number of output layer neurons, nL=2, k traverses from 1 to nL.

[0105] Among them, the input layer receives the preprocessed historical data feature vector x and the first layer weight matrix W 1 Do the dot product and add the bias vector b 1 , and then use the ReLU activation function to get the activation value h of the hidden layer 1 1 The subsequent hidden layer l repeats this process, using the previous layer activation value hl- 1 Calculated with the weight matrix Wl and the bias vector bl and activated by ReLU to obtain hl. After passing through multiple hidden layers, it finally reaches the output layer. First, according to h L-1 and the output layer weight matrix W L , the bias vector bj is used to calculate the transfer function zj, and then the Softmax function is used to convert these transfer function values ​​into the probability value yj of the steam cabinet being in normal or abnormal mode, j = 1, 2, where j = 1 corresponds to the normal mode probability and j = 2 corresponds to the abnormal mode probability.

[0106] It needs to be explained that the ReLU activation function gives neurons nonlinear processing capabilities, can extract complex features, and solve linear inseparability problems; the Softmax function normalizes the output of the output layer neurons, intuitively presenting the probability of the steamer being in normal or abnormal mode, facilitating decision-making.

[0107] Furthermore, generating a pattern recognition optimization model according to an array corresponding to the detail change eigenvalues ​​includes the following steps:

[0108] After receiving the arrays corresponding to the detail change feature values ​​from the auxiliary monitoring module, these arrays are first normalized to obtain the feature value arrays;

[0109] The standardized eigenvalue array is input into the constructed pattern recognition model as the input data for secondary training. It is combined with the historical working status data, and the training set and test set are divided again. The secondary training process based on dynamic learning rate adjustment is started, and the learning rate is set to X. 102 , every time after X 302 In the iteration, if the accuracy of the pattern recognition model on the test set is improved by less than X4, the learning rate is decayed to X5 times the original value;

[0110] At the same time, the loss function value on the test set is continuously monitored and weighted adjustments are performed. When the loss function value no longer decreases after X6 consecutive iterations, the secondary training is stopped to obtain the pattern recognition optimization model.

[0111] The standardized detail change feature value array is mixed with the previous historical working status data in a certain ratio (such as 50%:50%) and randomly divided into training set and test set. 102 Start the second training, every time X is completed 302 In the next iteration, the model accuracy is evaluated on the test set. If the improvement is less than X4 (such as 0.5%), the new learning rate = original learning rate * X5 (such as 0.9), which prompts the model to fine-tune parameters with smaller steps and continue optimization.

[0112] Furthermore, the specific process of continuously monitoring the loss function value on the test set and making weighted adjustments is as follows:

[0113] During the secondary training process, when a sample is marked as an abnormal mode, the loss weight Lweighted of the corresponding sample is increased to twice that of the normal sample in the loss function calculation, that is:

[0114] Lweighted = w*Lce;

[0115] Among them, Lce is the original cross entropy loss and w is the weight coefficient.

[0116] In this embodiment, when calculating the loss function each time, for the optimized pattern recognition optimization model, if the true label of the sample indicates an abnormal pattern (such as uncontrolled steam cabinet temperature, abnormal pressure, etc.), the weight coefficient of the sample in the cross-entropy loss calculation is set to 2, that is, the loss value is doubled. This prompts the pattern recognition optimization model to prioritize reducing the loss of such samples when adjusting parameters, thereby more accurately capturing abnormal feature patterns and improving the ability to distinguish abnormalities. Normal samples are retained and participate in the training of the pattern recognition optimization model according to the conventional cross-entropy loss calculation to maintain the balance of overall data learning.

[0117] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0121] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0122] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A steam cabinet alarm control system based on pattern recognition, characterized in that: include: Data acquisition module, model building module, auxiliary monitoring module, model optimization module, pattern judgment module; The data acquisition module is used to obtain historical working status data of the steam cabinet, collect internal monitoring data of the steam cabinet in real time, and pre-process the historical working status data and internal monitoring data of the steam cabinet; The model building module is used to build a pattern recognition model based on the pre-processed historical working status data; The auxiliary monitoring module is used to obtain detailed change characteristic values ​​based on the pre-processed steam cabinet internal monitoring data, wherein the detailed change characteristic values ​​include: temperature change characteristic values, pressure change characteristic values, water level change characteristic values, steam flow change characteristic values, and power change characteristic values; Create an empty data receiving buffer and continuously store the detail change feature value through the data receiving buffer; When the amount of data accumulated in the data receiving buffer reaches a first threshold, extracting an array corresponding to the detail change feature value, sending the array corresponding to the detail change feature value to the model optimization module, and clearing the data receiving buffer; The model optimization module is used to obtain an array corresponding to the detail change characteristic value, and generate a pattern recognition optimization model according to the array corresponding to the detail change characteristic value; The mode judgment alarm module is used to obtain the current internal monitoring data of the steam cabinet and input the current internal monitoring data of the steam cabinet into the pattern recognition optimization model. When the pattern recognition optimization model outputs that the working mode of the steam cabinet is normal mode, the internal monitoring data of the steam cabinet is continuously monitored. When the output steam cabinet working mode is abnormal mode, an alarm is triggered; The calculation formula for the temperature change characteristic value is: ； in, yes The temperature value at the moment, yes The temperature value at the moment, is the average temperature, is the allowable difference for normal temperature fluctuations, e is a natural constant, and π is the circumference of a circle.

2. A steam cabinet alarm control system based on pattern recognition as claimed in claim 1, characterized in that: The calculation formula of the pressure change characteristic value is: ； Where, yes The pressure value at the moment, yes The pressure value at the moment, is the average pressure, is the allowable difference of normal pressure fluctuation, e is a natural constant, and π is the circumference of a circle.

3. The steam cabinet alarm control system based on pattern recognition as claimed in claim 1, characterized in that: The calculation formula of the water level change characteristic value is: ； Where, yes The water level value at the moment, yes The water level value at the moment, is the average water level, is the allowable difference of normal fluctuation of water level, e is a natural constant, and π is the circumference of a circle.

4. The steam cabinet alarm control system based on pattern recognition as claimed in claim 1, characterized in that: The calculation formula for the steam flow rate change characteristic value is: ； Where, yes The steam flow value at the moment, yes The steam flow value at the moment, is the average steam flow rate over a period of time, It is the allowable difference of normal fluctuation of steam flow, e is a natural constant, and π is the circumference of a circle.

5. The steam cabinet alarm control system based on pattern recognition as claimed in claim 1, characterized in that: The calculation formula of the power change characteristic value is: ； Where, yes The power value at the moment, yes The power value at the moment, is the historical average power, is the allowable difference in power fluctuation, e is a natural constant, and π is the circumference of a circle.

6. The steam cabinet alarm control system based on pattern recognition as claimed in claim 1, characterized in that: The specific process of constructing the pattern recognition model is as follows: Extracting normal working state data and abnormal working state data of the steam cabinet from the historical working state data; Preprocessing normal working state data and abnormal working state data; Divide the preprocessed dataset into training set and validation set; Use the training set to train the multi-layer perceptron architecture and set the initial learning rate , momentum coefficient and the number of iterations , through the back-propagation algorithm, the connection weights between the neurons in each layer of the model are continuously adjusted, so that the loss function value of the model on the validation set is gradually reduced; When the validation set loss stops decreasing after N consecutive iterations, training is stopped and the model parameters at this time are saved as the pattern recognition model.

7. The steam cabinet alarm control system based on pattern recognition as claimed in claim 1, characterized in that: The formula of the pattern recognition model is as follows: Assume that the multilayer perceptron has Layer, the number of neurons in the input layer is , corresponding to the input vector Represents the historical working status feature data after preprocessing, the hidden layer , , the number of neurons is , the number of neurons in the output layer is , the output vector , respectively represent the probability values ​​of the steam cabinet being in normal and abnormal working modes; For the hidden layer , the activation value of its neuron The calculation is as follows: ； in, Is connected Layer and The weight matrix of the layer neurons has the dimension , It is The bias vector of the layer, dimension is , is the ReLU activation function, that is , z refers to the weighted input of the neuron, ; The calculation from the input layer to the hidden layer 1 is: ; After passing through multiple hidden layers, it reaches the output layer. The output layer is normalized using the Softmax function. The pattern recognition model obtains the final prediction result through the following formula : ； Among them, e is a natural constant, is the first transit function, , is the output layer weight matrix No. Row transposed vector, is the bias of the corresponding neuron in the output layer, , The k in is the index used to traverse the neurons in the output layer. is the second transfer function, , is the output layer weight matrix The row vector transpose corresponding to the connection weights of the k-th output layer neuron, is the bias of the output layer corresponding to the k-th output layer neuron, is the number of neurons in the output layer, , k ranges from 1 to Traverse.

8. The steam cabinet alarm control system based on pattern recognition as claimed in claim 6, characterized in that: Generating a pattern recognition optimization model based on an array corresponding to the detail change eigenvalues ​​includes the following steps: After receiving the arrays corresponding to the detail change feature values ​​from the auxiliary monitoring module, these arrays are first normalized to obtain the feature value arrays; The standardized eigenvalue array is input into the constructed pattern recognition model as the input data for secondary training. It is combined with the historical working status data, and the training set and test set are divided again. The secondary training process based on dynamic learning rate adjustment is started, and the learning rate is set to , every time you pass iterations, if the accuracy of the pattern recognition model on the test set is less than , then the learning rate decays to the original times; At the same time, the loss function value on the test set is continuously monitored and weighted adjustments are made. When the iteration no longer decreases, the secondary training is stopped and the pattern recognition optimization model is obtained.

9. The steam cabinet alarm control system based on pattern recognition as claimed in claim 7, characterized in that: The specific process of continuously monitoring the loss function value on the test set and making weighted adjustments is as follows: During the secondary training process, when a sample is marked as an abnormal mode, the loss weight of the corresponding sample in the loss function calculation is The improvement is twice that of the normal sample, that is: ； in, is the original cross entropy loss, and w is the weight coefficient.

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