Industrial park risk source level assessment method, device and electronic equipment

Abstract

The present invention provides an industrial park risk source level assessment method, device, and electronic device, comprising: obtaining risk source data to be assessed in the industrial park and classifying it into invariants, continuous variables, and mutations; constructing and training a neural network model; comprising: an input layer for inputting the risk source data; a feature extraction unit for extracting invariant features, continuity features, and mutation features; an output layer for obtaining alarm results for various types of risk sources based on the invariant features, continuity features, and mutation features; obtaining current risk source data to be assessed in the industrial park, inputting the trained neural network model, and evaluating the alarm results for each risk source. The present invention can effectively identify, assess, and control potential risks, ensure the safety of production operations, and formulate effective risk management strategies to achieve sustainable development.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and specifically relates to a method, device and electronic equipment for assessing the level of risk sources in an industrial park. Background Art

[0002] With the acceleration of industrialization, industrial parks, as crucial vehicles for economic development, are increasingly facing safety challenges. Chemical industrial parks, in particular, involve large quantities of hazardous chemicals and high-risk operations. Accidents can severely impact personnel safety, environmental protection, and even social stability. Therefore, establishing a scientific and systematic risk source assessment methodology is crucial for preventing and mitigating accidents and ensuring the sustainable development of industrial parks.

[0003] Risk assessments for industrial parks often involve a variety of processes, equipment, and chemicals. The interplay of these complex factors complicates risk assessments. Risk assessments rely on prior data and assumptions, but uncertainties in the real world can lead to biased predictions. For some highly specialized processes or new materials, the lack of established risk assessment methods and standards increases the technical difficulty of assessments. Human judgment and decision-making during risk assessments can be subjective and biased, impacting the objectivity and accuracy of assessments. Risks are constantly evolving and require continuous monitoring and updating. However, in practice, updating risk assessments can be neglected or delayed for various reasons. Summary of the Invention

[0004] In order to solve the problems of existing risk assessment being difficult and inaccurate, the present invention provides an industrial park risk source level assessment method, comprising:

[0005] S1: Obtain risk source data to be assessed in the industrial park and classify them into invariable variables, continuous variables, and sudden changes;

[0006] S2: Build and train a neural network model that takes risk source data as input and outputs warning results for various risk sources; including:

[0007] Input layer, used to input risk source data;

[0008] Feature extraction unit, including:

[0009] A first feature extraction unit is used to extract an invariant feature based on an invariant variable;

[0010] The second feature extraction unit is used to extract continuous features based on the continuous variables;

[0011] The third feature extraction unit is used to extract the mutation feature according to the mutation amount;

[0012] The output layer is used to obtain the alarm results of various types of risk sources based on the invariant characteristics, continuity characteristics and mutation characteristics. S3: Obtain the current risk source data to be evaluated in the industrial park, input the trained neural network model, and evaluate the alarm results of each risk source.

[0013] Furthermore, the feature extraction unit includes: a first convolutional layer, a first pooling layer, a hidden layer, a second convolutional layer, a random dropout layer, a third convolutional layer, and a second pooling layer connected in sequence;

[0014] The first convolutional layer and the first pooling layer, as the first feature extraction unit, effectively extract invariant features;

[0015] The hidden layer, the second convolutional layer, and the random dropout layer serve as the second feature extraction unit to effectively extract continuous features;

[0016] The third convolutional layer and the second pooling layer serve as the third feature extraction unit to effectively extract mutation features.

[0017] Furthermore, the convolution kernel of the first convolution layer is 3, the stride is 3, the padding is 2, and the activation function is rule; the convolution kernel of the second convolution layer is 3, the stride is 7, the padding is 3, and the activation function is rule; the convolution kernel of the third convolution layer is 5, the stride is 3, the padding is 2, and the activation function is rule.

[0018] Furthermore, step S3 further includes: obtaining historical risk source data to be assessed for the industrial park;

[0019] The overall evaluation model includes: an overall input layer, two neural network models, a fully connected layer, and an overall output layer;

[0020] The total input layer is used to obtain the current risk source data and historical risk source data to be assessed in the industrial park;

[0021] The first neural network model is connected to the total input layer and is used to output current features based on current risk source data;

[0022] The second neural network model is connected to the total input layer and is used to output historical features based on historical risk source data;

[0023] The fully connected layer is connected to the output of the first and second neural network models, and is used to determine the total alarm result based on current features and historical features, and output it through the total output layer.

[0024] Furthermore, the total alarm result is determined based on the current features and the historical features, specifically by weighting the current features and the historical features to obtain the total alarm result.

[0025] Furthermore, the training of the neural network model includes:

[0026] Obtain previous data, including key factors corresponding to each risk source, the likelihood and severity of each risk source;

[0027] Based on key factors, the likelihood and severity of each risk source, expert experience is used to classify each risk source, obtain the alarm results for each risk source, and construct a training data set based on previous data and alarm results;

[0028] The neural network model is trained according to the training data set to obtain a trained neural network model.

[0029] Furthermore, based on the risk source data, variables with only two possible answers, yes or no, are classified as unpredictable variables; variables with consistent and predictable change directions are classified as continuous variables; and variables with inconsistent and uncontrollable change directions are classified as mutation variables.

[0030] Furthermore, risk sources include: mechanical risk sources, electrical risk sources, and operational risk sources;

[0031] Mechanical risk sources include: risk sources related to transmission devices, risk sources related to drive devices, risk sources related to bearings, and risk sources related to fixing devices;

[0032] Electrical risk sources include: risk sources related to power supply, risk sources related to control system, risk sources related to electrical equipment and risk sources related to ground wire;

[0033] Operational risk sources include: risk sources related to operation control, risk sources related to safety warnings, and risk sources related to cleaning and maintenance.

[0034] On the other hand, the present invention also provides an industrial park risk source level assessment device, comprising a data acquisition unit, a model processing unit and a risk assessment unit connected in sequence; and used to respectively execute any of the above-mentioned industrial park risk source level assessment methods.

[0035] On the other hand, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein when the processor executes the program, any of the above-mentioned industrial park risk source level assessment methods is implemented.

[0036] The beneficial effects of the present invention are: as can be seen from the above, the industrial park risk source level assessment method, device and electronic equipment provided by the embodiment of the present invention can effectively identify, evaluate and control potential risks, ensure the safety of production operations, so as to formulate effective risk management strategies to achieve sustainable development. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 Schematic diagram of the process of the industrial park risk source level assessment method in an embodiment of the present invention;

[0039] Figure 2 Schematic diagram of the structure of a neural network model in an embodiment of the present invention;

[0040] Figure 3 Schematic diagram of a long short-term memory network in an embodiment of the present invention;

[0041] Figure 4 Schematic diagram of the structure of the industrial park risk source level assessment device in an embodiment of the present invention;

[0042] Figure 5 A schematic diagram of an electronic device in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0043] In order to make the objectives, technical solutions and advantages of the present disclosure more clearly understood, the present disclosure is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0044] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present invention should have the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0045] The embodiment of the present invention provides a method for assessing the risk source level of an industrial park. The embodiment of the present invention provides a method for assessing the risk source level of an industrial park. Figure 1 As shown in the figure, the risk source level assessment method for industrial parks includes:

[0046] Step S1: Obtain the risk source data to be assessed in the industrial park and classify them into invariable variables, continuous variables, and sudden changes;

[0047] In an embodiment of the present invention, all possible risk sources in the park are comprehensively sorted out, including but not limited to the use, storage, transportation, waste disposal and other links of chemicals; flow charts, technical specifications, record books, etc. are optionally used to record in detail the operation process and potential risk factors of each link.

[0048] More specifically, risk sources include mechanical risk sources, electrical risk sources, and operational risk sources. Mechanical risk sources include, but are not limited to, risks associated with transmission devices, drive devices, bearings, and fixtures. Electrical risk sources include, but are not limited to, risks associated with power supplies, control systems, electrical equipment, and grounding. Operational risk sources include, but are not limited to, risks associated with operational control, safety warnings, and cleaning and maintenance.

[0049] Let's take the coal mine belt conveyor assessment as an example. To ensure safety and stability during coal mine belt conveyor transportation, it is necessary to identify and control risk sources. The following is the identification of risk sources in the coal mine belt conveyor profession:

[0050] Mechanical risk sources include:

[0051] 1. Risk sources involved in the belt itself: wear, stretching, aging, rupture, blistering, insufficient or excessive tension, breakage, etc.

[0052] 2. Risk sources involved in the drive device: drive motor failure, electronic control failure, heat increase caused by dust accumulation in the drive transmission chain, etc.

[0053] 3. Risk sources involved in bearings: poor lubrication, wear, overload, vibration, looseness, etc.

[0054] 4. Risk sources involved in the roller: thermal expansion and contraction, peeling, diameter change, rust, foreign objects such as stones and wood stuck in the roller, etc.

[0055] 5. Risk sources involved in belt brackets: insufficient firmness, deformation, rust, cracking, etc.; risk sources involved: belt overlap, excessive tension, deviation, etc.

[0056] Sources of electrical risks include:

[0057] 1. Risk sources involved in power supply: power short circuit, poor cable quality, overload, etc.

[0058] 2. Risk sources involved in the control system: control line short circuit, poor wiring, electrical protection relay failure, etc.

[0059] 3. Risk sources involved in electrical equipment: motor failure, loose terminal blocks, excessive temperature, etc.

[0060] 4. Risk sources involved in ground wire: ground wire breakage, poor grounding, etc.

[0061] Sources of operational risk include:

[0062] 1. Risk sources involved in operation control: unbalanced load, too fast or too slow operation speed.

[0063] 2. Risk sources involved in the discharge pedal: residues, dropped objects, unbalanced discharge flow, etc.

[0064] 3. Risk sources involved in safety warnings: warning signals, emergency stops, start buttons, etc.

[0065] 4. Risk sources involved in cleaning and maintenance: incorrect shutdown procedures, improper personnel during cleaning and maintenance, and incorrect operations in maintenance and simulation.

[0066] The evaluation is based on 12 key factors at the production nodes in coal mine belt transportation.

[0067] After collecting the above risk source data, it can be optionally converted into digital data for recording and storage. For example, serious variables such as short circuit, warning, emergency stop, etc., which have only two conditions of yes or no, are regarded as non-variable variables, that is, if the variable changes, it is directly identified as a high risk level, and if it does not change, it is identified as a low risk; it is converted into 0 / 1, 0 means no occurrence, 1 means occurrence, and irreversible quantities such as belt wear, aging, cracking, etc. that continue to grow over time are regarded as continuous variables. This type of continuous variable not only has a consistent change direction, but also has controllable and predictable changes. For example, wear and aging, etc., can roughly predict the risks and high risks. ; Specifically expressed by its specific wear and aging amount, etc., when it is within a certain range, it is tolerable and identified as low risk, exceeding a certain range is identified as medium risk, and exceeding a higher range is identified as high risk; unstable quantities such as temperature and load fluctuations are regarded as mutations, and the direction of change of the mutation variable is inconsistent. The key is that its change amount is uncontrollable, and it is impossible to predict the time when it may occur and the specific amount of change. When the amplitude of the change is within a certain range, that is, the change amount is less than the set threshold, it is tolerable and identified as low risk, exceeding a certain range is identified as medium risk, and exceeding a higher range is identified as high risk.

[0068] Step S2: Construct and train a neural network model that takes risk source data as input and outputs warning results for various types of risk sources; including:

[0069] Input layer, used to input risk source data;

[0070] Feature extraction unit, including:

[0071] A first feature extraction unit is used to extract an invariant feature based on an invariant variable;

[0072] The second feature extraction unit is used to extract continuous features based on the continuous variables;

[0073] The third feature extraction unit is used to extract the mutation feature according to the mutation amount;

[0074] The output layer is used to obtain the warning results of various types of risk sources based on invariant features, continuity features and mutation features.

[0075] In this embodiment, invariant features, continuity features and mutation features are extracted in sequence according to the type of risk source data, which can process each risk source data according to its type and extract the unique features of each type of data more effectively and accurately; more importantly, invariant features, continuity features and mutation features are extracted in sequence according to the different features, which can bring out the importance of each feature in order and further improve the accuracy of subsequent evaluation.

[0076] More preferably, the feature extraction unit includes, in sequence: a first convolutional layer, a first pooling layer, a hidden layer, a second convolutional layer, a random dropout layer, a third convolutional layer, and a second pooling layer; the first convolutional layer and the first pooling layer, as the first feature extraction unit, effectively extract invariant features; the hidden layer, the second convolutional layer, and the random dropout layer, as the second feature extraction unit, are good at extracting continuous values ​​and effectively extracting continuity features; the third convolutional layer and the second pooling layer, as the third feature extraction unit, are placed at the end of the model to effectively extract mutation features. Specifically, the structure of the feature extraction unit is provided in the present invention as a preferred embodiment, which takes advantage of the advantages of each structure, first extracts invariant features, then extracts continuous variable features, and finally extracts mutation features, and finally fuses and outputs the alarm results of each type of risk source to determine the overall risk level.

[0077] More preferably, the convolution kernel of the first convolution layer is 3, the stride is 3, the padding is 2, and the activation function is rule. The convolution kernel of the second convolution layer is 3, the stride is 7, the padding is 3, and the activation function is rule. The convolution kernel of the third convolution layer is 5, the stride is 3, the padding is 2, and the activation function is rule.

[0078] More preferably, the hidden layer adopts Figure 3 The Long Short-Term Memory (LSTM) network model shown in Figure 1 is used to further extract continuous features, where the forget gate f tActing on the previous temporal hidden state C t-1 Determines the effect of the previous hidden unit state on the current hidden unit: f t *C t-1 .

[0079] Input gate i t The input to the current hidden unit (The input is the output h of the previous time series hidden unit t-1 and the input of the current hidden unit). Determines the effect of the current input on the current hidden unit:

[0080]

[0081] The forget gate and input gate determine the current hidden layer state output C t , output gate o t Acting on the current output, it produces the current hidden unit output:

[0082]

[0083] h t =tanh(o t *C t )

[0084] Among them, f t 、i t 、o t They are all linear combinations of the inputs of the current hidden units, and then normalized by the sigmoid function:

[0085] f t =σ(W f X t +U f h t-1 +b f )

[0086] i t =σ(W i X t +U i h t-1 +b i )

[0087] o t =σ(W o X t +U o h t-1 +b o )

[0088] More preferably, after constructing the neural network model, prior to step S3, previous data on risk sources is obtained. This previous data includes the key factors corresponding to each risk source, the likelihood of occurrence, and the severity of each risk source. The three types of risk source data—invariant, continuous, and sudden—all include at least one key factor. Based on the key factors, the likelihood of occurrence, and the severity of each risk source, expert experience is applied to classify each risk source, obtain an alarm result for each risk source, and construct a training dataset based on the previous data and the alarm results. The neural network model is then trained based on the training dataset to obtain a trained neural network model.

[0089] S3: Obtain the current risk source data to be evaluated in the industrial park, input it into the trained neural network model, and evaluate the alarm results of each risk source.

[0090] Specifically, the current risk source data for the industrial park to be assessed is obtained and input into the neural network model trained in step S2 to evaluate and obtain the warning results for each risk source. Specifically, the trained neural network model is used to conduct an in-depth analysis of each key factor and evaluate the warning results for each risk source.

[0091] More preferably, step S3 further includes: obtaining historical risk source data to be assessed in the industrial park;

[0092] like Figure 2 As shown, the overall evaluation model includes: the overall input layer, the two aforementioned neural network models, the fully connected layer, and the overall output layer;

[0093] The total input layer is used to obtain the current risk source data and historical risk source data to be assessed in the industrial park;

[0094] The first neural network model is connected to the total input layer and is used to output current features based on current risk source data;

[0095] The second neural network model is connected to the total input layer and is used to output historical features based on historical risk source data;

[0096] The fully connected layer is connected to the output of the first and second neural network models to add the weights of current features and historical features, and output the total alarm results of each risk source through the total output layer.

[0097] For example, the key factors of the current day are input into the first neural network model for processing, and the key factors of several days before the current day are input into the second neural network model for processing; the first neural network model processes and outputs the feature vector of the current day, and the second neural network model processes and outputs the feature vectors of several days before the current day; the feature vector of the current day and the feature vectors of several days before the current day are weighted and added, and after processing through the fully connected layer, the alarm results of each risk source are output.

[0098] For example, the first and second neural network models output corresponding eigenvectors, which are the sum of the eigenvectors of w(1*n): w1*0.65 + w2*0.35. Risks are graded based on the likelihood of occurrence and the severity of the consequences, including low risk, medium risk, and high risk. That is, the neural network model outputs warning results, including low risk, medium risk, and high risk.

[0099] Preferably, the industrial park risk source level assessment method of the present invention further includes: Step S4: comprehensively assessing the overall risk level based on the alarm results of each risk source combined with the mutual relationship and combination effect between the risk sources.

[0100] In the embodiment of the present invention, risks are quantitatively or qualitatively described using previous data and industry standards, and the overall risk level is comprehensively assessed by considering the interrelationships and combined effects between risks.

[0101] In an embodiment of the present invention, the risk source level assessment of an industrial park further includes: formulating corresponding prevention and control measures for different risk levels; such as improving processes, strengthening monitoring, and setting up protective facilities.

[0102] Develop specific operating procedures and emergency plans based on the risk level of each risk source to ensure a rapid response when risks occur.

[0103] In an embodiment of the present invention, the industrial park risk source level assessment method further includes: establishing a monitoring system to regularly check the status of risk sources and the implementation of safety measures; regularly reviewing the results of risk assessments and promptly updating the risk database to ensure the timeliness and accuracy of the assessments.

[0104] Promptly communicate risk assessment results to management, employees, and relevant stakeholders to ensure information transparency. Provide safety risk education and training to employees to enhance their safety awareness and emergency response capabilities.

[0105] The embodiments of the present invention classify and extract features from risk sources based on their characteristics, and then identify and evaluate them, so as to discover potential safety hazards in advance and take preventive measures to prevent accidents. Through quantitative risk management, decision makers can make more scientific safety management decisions based on data and analysis results. Clear risk levels help to formulate targeted emergency plans, improve the ability to respond to emergencies, and facilitate strengthening cooperation with external professional organizations to ensure the effectiveness and practicality of risk assessment work.

[0106] The embodiments of the present invention introduce new safety management technologies and best practices to continuously improve the level of risk management. Through this series of steps, the industrial park can effectively identify, evaluate and control potential risks, ensure the safety of production operations, and comply with national and local laws and regulations to promote the sustainable development of the park. Safety risk assessment can timely detect potential dangerous factors, such as belt deviation, slippage, overload, etc. By taking corresponding safety measures, it can effectively prevent the occurrence of accidents and protect the life safety and physical health of employees. In addition, good safety management can also enhance the corporate image and enhance employees' trust and satisfaction with the company. Coal mine belt transportation risk assessment is of great significance to ensuring safe production, improving efficiency and protecting the environment. Enterprises should fully recognize its importance and formulate effective risk management strategies to achieve sustainable development. It can effectively identify, evaluate and control potential risks and ensure the safety of production operations so as to formulate effective risk management strategies to achieve sustainable development.

[0107] The foregoing description is of specific embodiments of the present invention. In some cases, the actions or steps described in the embodiments of the present invention may be performed in an order different from that shown in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0108] Based on the same concept, an embodiment of the present invention also provides an industrial park risk source level assessment device. Applied to a processor. Figure 4 As shown, the industrial park risk source level assessment device includes: a data acquisition unit, a model processing unit and a risk assessment unit, which respectively perform the above steps S1-S3.

[0109] For the convenience of description, the above devices are described as being divided into various modules according to their functions. Of course, when implementing the embodiments of the present invention, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0110] The apparatus of the above embodiment is applied to the corresponding method of the above embodiment and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0111] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the method of any of the above embodiments is implemented.

[0112] An embodiment of the present invention provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the method in any of the above embodiments.

[0113] Figure 5 A more specific hardware structure diagram of an electronic device provided in this embodiment is shown. The device may include: a processor 501, a memory 502, an input / output interface 503, a communication interface 504, and a bus 505. The processor 501, the memory 502, the input / output interface 503, and the communication interface 504 are communicatively connected to each other within the device via the bus 505.

[0114] The processor 501 can be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided by the method embodiments of the present invention.

[0115] The memory 502 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 502 can store an operating system and other application programs. When the technical solutions provided by the method embodiments of the present invention are implemented through software or firmware, the relevant program codes are stored in the memory 502 and called and executed by the processor 501.

[0116] The input / output interface 503 is used to connect to the input / output module to realize information input and output. The input / output module can be configured as a component in the device (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0117] The communication interface 504 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (such as USB, network cable, etc.) or a wireless method (such as mobile network, WIFI, Bluetooth, etc.).

[0118] The bus 505 comprises a pathway for transmitting information between the various components of the device (eg, the processor 501 , the memory 502 , the input / output interface 503 , and the communication interface 504 ).

[0119] It should be noted that although the above device only shows the processor 501, memory 502, input / output interface 503, communication interface 504, and bus 505, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will understand that the above device may only include the components necessary to implement the embodiments of the present invention, and does not necessarily include all the components shown in the figure.

[0120] It should be appreciated that the method steps in the embodiments of the present invention can be implemented or executed by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method can use standard programming techniques. Each program can be implemented in a high-level procedural or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. In addition, for this purpose, the program can be run on a programmed application-specific integrated circuit.

[0121] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by the context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. A computer program includes a plurality of instructions that can be executed by one or more processors.

[0122] Further, the method can be implemented in and operably connected to any type of suitable computing platform, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Various aspects of the present invention can be implemented as machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, RAM, ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or portions thereof, can be transmitted over a wired or wireless network. When such media includes instructions or programs that implement the above steps in conjunction with a microprocessor or other data processor, the invention herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques of the present invention, the present invention can also include the computer itself.

[0123] The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data that is stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on the display.

[0124] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application is limited to these examples. Within the scope of the present application, the technical features of the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the above aspects of the present application, which are not provided in detail for the sake of simplicity.

[0125] This application is intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the embodiments of the present invention. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the scope of protection of this application.

Claims

1. A method for assessing the risk level of an industrial park, characterized in that: Methods include: S1: Obtain risk source data to be assessed in the industrial park and classify them into invariable variables, continuous variables, and sudden changes; An invariant is a variable that has only two conditions: yes or no. A continuous variable is an irreversible variable that changes in a consistent direction and continues to change over time. A sudden change is an unstable variable that changes in an inconsistent direction and fluctuates up and down. S2: Build and train a neural network model that takes risk source data as input and outputs warning results for various risk sources; this includes: Input layer, used to input risk source data; The feature extraction unit includes: a first convolutional layer, a first pooling layer, a hidden layer, a second convolutional layer, a random dropout layer, a third convolutional layer, and a second pooling layer connected in sequence; extracting invariant features, continuity features, and mutation features in sequence; the first convolutional layer and the first pooling layer serve as a first feature extraction unit for effectively extracting invariant features based on invariants; The hidden layer, the second convolutional layer and the random dropout layer serve as the second feature extraction unit, and are used to effectively extract continuous features based on continuous variables; The third convolutional layer and the second pooling layer serve as the third feature extraction unit, which is used to effectively extract mutation features according to the mutation amount; The output layer is used to obtain the warning results of various types of risk sources based on invariant features, continuity features, and mutation features; S3: Obtain the current risk source data to be assessed in the industrial park, input it into the trained neural network model, evaluate the alarm results of various types of risk sources; then fuse and output the alarm results of various types of risk sources; and then determine the overall risk level.

2. The industrial park risk source level assessment method according to claim 1 is characterized in that: The convolution kernel of the first convolution layer is 3, the stride is 3, the padding is 2, and the activation function is ReLU; the convolution kernel of the second convolution layer is 3, the stride is 7, the padding is 3, and the activation function is ReLU; the convolution kernel of the third convolution layer is 5, the stride is 3, the padding is 2, and the activation function is ReLU.

3. The industrial park risk source level assessment method according to claim 1 is characterized in that: Training of neural network models, including: Obtain previous data, including key factors corresponding to each risk source, the likelihood and severity of each risk source; Based on key factors, the likelihood and severity of each risk source, expert experience is used to classify each risk source, obtain the alarm results for each risk source, and construct a training data set based on previous data and alarm results; The neural network model is trained according to the training data set to obtain a trained neural network model.

4. The industrial park risk source level assessment method according to any one of claims 1 to 3, characterized in that: Risk sources include: mechanical risk sources, electrical risk sources and operational risk sources; Mechanical risk sources include: risk sources related to transmission devices, risk sources related to drive devices, risk sources related to bearings, and risk sources related to fixing devices; Electrical risk sources include: risk sources related to power supply, risk sources related to control system, risk sources related to electrical equipment and risk sources related to ground wire; Operational risk sources include: risk sources related to operation control, risk sources related to safety warnings, and risk sources related to cleaning and maintenance.

5. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for assessing the risk source level of an industrial park as described in any one of claims 1 to 4 is implemented.

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