Risk assessment method and device, electronic equipment and storage medium

Abstract

The invention discloses a risk assessment method and device, electronic equipment and a storage medium, and relates to the technical field of security operation and maintenance and risk assessment. The method comprises the following steps: respectively acquiring a plurality of position images, a sound array and a temperature array of target production equipment in a target plant area, and analyzing the plurality of position images, the sound array and the temperature array to obtain an equipment risk assessment result; obtaining video data in the target factory area, and determining operation behaviors and material storage information of workers in the target factory area; determining a worker risk assessment result based on the operation behavior of the worker, and determining a material placement risk assessment result based on the material storage information; and determining a risk assessment result of the target factory based on the equipment risk assessment result, the worker risk assessment result and the material placement risk assessment result. According to the scheme, risk assessment can be rapidly and accurately carried out on the chemical factory, potential risks are identified, and help is provided for safety management of the chemical factory.

Description

Technical Field

[0001] The present invention relates to the technical field of safe operation and maintenance and risk assessment, and in particular to a risk assessment method, device, electronic equipment and storage medium. Background Art

[0002] Chemical plants, also known as chemical plants or chemical plants, are industrial facilities that specialize in the production, processing, and handling of chemicals. These plants transform raw materials into a variety of useful chemicals, including basic chemicals (such as sulfuric acid, ammonia), fine chemicals (such as pharmaceuticals, fragrances), and specialty chemicals (such as coatings, adhesives) through a series of complex chemical reactions and physical operations.

[0003] Risk assessment in chemical plants is a systematic process that aims to identify, analyze and control potential risks to ensure personnel safety, environmental protection and stable operation of production facilities. Risk assessment not only helps prevent accidents, but also improves corporate compliance and optimizes resource allocation for more efficient safety management.

[0004] How to quickly and accurately conduct risk assessments on chemical plants, identify potential risks, and provide assistance for the safety management of chemical plants is a key research issue in the industry. Summary of the invention

[0005] The present invention provides a risk assessment method, device, electronic equipment and storage medium to quickly and accurately conduct risk assessment on a chemical plant, identify potential risks, and provide assistance for the safety management of the chemical plant.

[0006] According to one aspect of the present invention, there is provided a risk assessment method, the method comprising:

[0007] Performing image acquisition, sound monitoring, and temperature monitoring on at least two target positions of target production equipment in a target factory area to obtain multiple position images, sound arrays, and temperature arrays;

[0008] Performing abnormality analysis on the sound array and the temperature array respectively to obtain a sound abnormality coefficient array and a temperature abnormality coefficient array, and obtaining a first abnormality coefficient array based on the sound abnormality coefficient array and the temperature abnormality coefficient array;

[0009] Performing damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determining a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determining an equipment risk assessment result of the target production equipment based on the risk coefficient;

[0010] An uncontrollable coefficient array of a target plant area is determined based on the risk assessment results of each device, and a risk assessment result of the target plant area is determined according to the uncontrollable coefficient array of the target plant area.

[0011] According to another aspect of the present invention, there is provided a risk assessment device, the device comprising:

[0012] A data acquisition module, used to respectively perform image acquisition, sound monitoring and temperature monitoring of at least two target positions of target production equipment in a target factory area, and obtain multiple position images, sound arrays and temperature arrays;

[0013] A first abnormal coefficient array determination module is used to perform abnormal analysis on the sound array and the temperature array respectively to obtain a sound abnormal coefficient array and a temperature abnormal coefficient array, and obtain a first abnormal coefficient array based on the sound abnormal coefficient array and the temperature abnormal coefficient array;

[0014] an equipment risk assessment result determination module, configured to perform damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determine a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determine an equipment risk assessment result of the target production equipment based on the risk coefficient;

[0015] The plant area risk assessment result determination module is used to determine the uncontrollable coefficient array of the target plant area based on the risk assessment results of each device, and determine the risk assessment result of the target plant area according to the uncontrollable coefficient array of the target plant area.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] at least one processor; and

[0018] a memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the risk assessment method described in any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the risk assessment method described in any embodiment of the present invention when executed.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the risk assessment method according to any embodiment of the present invention is implemented.

[0022] The technical solution of the embodiment of the present invention obtains multiple position images, sound arrays and temperature arrays by performing image acquisition, sound monitoring and temperature monitoring on at least two target positions of the target production equipment in the target plant area respectively; performs abnormal analysis on the sound array and the temperature array respectively to obtain a sound abnormality coefficient array and a temperature abnormality coefficient array, and obtains a first abnormality coefficient array based on the sound abnormality coefficient array and the temperature abnormality coefficient array; performs damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determines a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determines an equipment risk assessment result of the target production equipment based on the risk coefficient; determines an uncontrollable coefficient array of the target plant area based on each equipment risk assessment result, and determines the risk assessment result of the target plant area based on the uncontrollable coefficient array of the target plant area, which can quickly and accurately conduct risk assessment on chemical plants, identify potential risks, and provide assistance for safety management of chemical plants.

[0023] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 is a flow chart of a risk assessment method provided according to Embodiment 1 of the present invention;

[0026] Figure 2 is a flow chart of a risk assessment method provided according to Embodiment 2 of the present invention;

[0027] Figure 3 is a flow chart of a risk assessment method provided according to Embodiment 3 of the present invention;

[0028] Figure 4 is a flow chart of a risk assessment method provided according to Embodiment 4 of the present invention;

[0029] Figure 5 is a schematic diagram of the structure of a risk assessment device provided according to Embodiment 5 of the present invention;

[0030] Figure 6 It is a schematic diagram of the structure of an electronic device for implementing the risk assessment method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0033] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0034] Embodiment 1

[0035] Figure 1This is a flow chart of a risk assessment method provided according to the first embodiment of the present invention. This embodiment is applicable to the situation of conducting safety risk assessment on production equipment in the chemical industry. The method can be executed by a risk assessment device. The risk assessment device can be implemented in the form of hardware and / or software. The risk assessment device can be configured in electronic devices such as computers, servers or tablet computers. Figure 1 As shown, the method includes:

[0036] Step 110: perform image acquisition, sound monitoring, and temperature monitoring on at least two target locations of target production equipment in the target factory area to obtain multiple location images, sound arrays, and temperature arrays.

[0037] The target plant area may be a production plant area, a storage plant area, or a use plant area of ​​chemical products, which is not limited in this embodiment.

[0038] Among them, the target production equipment can be production equipment, storage equipment or transportation equipment for hazardous chemicals, etc., which is not limited in this embodiment; in this embodiment, the number of target production equipment can be multiple, for example, all storage equipment in the target plant area, which is not limited in this embodiment.

[0039] It is understandable that in the risk assessment of hazardous chemical production and storage equipment, valves and welds are key parts of the equipment. Aging and damage in these areas may cause leakage in the equipment, which in turn may lead to safety accidents. Valves and welds are the areas on the tank that are most prone to aging and damage. Valves are in a state of pressure change and switching operation for a long time, while welds are the welding parts of the tank, which are prone to cracks and corrosion due to stress concentration. At the same time, when these areas are damaged and leaked, they usually manifest as temperature changes and abnormal sounds. Therefore, monitoring the position of valves and welds is very critical.

[0040] Accordingly, the target position may be a valve position or a weld position in the target production equipment, or may be other key positions, which are not limited in this embodiment.

[0041] In this embodiment, in order to ensure comprehensive monitoring of the status of these target locations (valves and welds), it is necessary to install multiple sensors and monitoring equipment near the valves and welds to collect relevant data in real time; exemplary data may include image data, sound data and temperature data, for example, using a high-definition camera or infrared imager to regularly capture images of the valve and weld areas, especially at the joints of the valves and welds, where tiny cracks or corrosion spots may appear, which are usually difficult to detect with the naked eye, but temperature anomalies can be captured through infrared imaging; installing acoustic sensors at the valves and welds to collect sound data of these parts in real time. When the valves and welds are damaged, there may be tiny leakage sounds or metal friction sounds, which can reflect the working status of the equipment. These sound data will help analyze the abnormal conditions of the equipment and identify whether there are leaks or other potential faults; installing temperature sensors around the valves and welds to monitor temperature changes at multiple locations in real time. When the equipment is damaged or aged, it is usually accompanied by abnormal increase or decrease in local temperature, especially at the connection between the valves and welds. Temperature anomalies may be a precursor to leakage or corrosion.

[0042] Optionally, in this embodiment, image acquisition, sound monitoring and temperature monitoring are performed on at least two target positions of the target production equipment in the target factory area to obtain multiple position images, sound arrays and temperature arrays, which may include: acquiring the target positions of the target production equipment, and respectively acquiring the position images, sound data and temperature data of each of the target positions; wherein the target positions include: valves and welds; obtaining the sound array based on the position information of each of the target positions and the sound data; obtaining the temperature array based on the position information of each of the target positions and the temperature data.

[0043] In an optional implementation of this embodiment, the target position of key components such as valves or welds can be determined based on the design drawings of the target production equipment. Furthermore, image data of each target position can be collected by an image sensor; sound data of each target position can be collected by a microphone array; and temperature data of each target position can be collected by a temperature sensor.

[0044] Furthermore, a sound array may be obtained based on the position information of each target position, for example, the position information of each target position and each sound data; and temperature data may be obtained based on the position information of each target position and each temperature data.

[0045] In a specific example of this embodiment, the sound data collected by each sound sensor can be formed into a sound array according to the position coordinates of each target position; the temperature data of multiple positions can be formed into a temperature array according to the position coordinates of each target position.

[0046] The solution of this embodiment can provide comprehensive data support for real-time status monitoring of the equipment by collecting image, sound and temperature data of multiple key positions (valves and welds) of the target production equipment. Through the integration and analysis of these multi-dimensional data, the dynamic changes of the equipment can be reflected in real time, significantly improving the risk assessment capability of the tank equipment.

[0047] Step 120: perform abnormality analysis on the sound array and the temperature array respectively to obtain a sound abnormality coefficient array and a temperature abnormality coefficient array, and obtain a first abnormality coefficient array based on the sound abnormality coefficient array and the temperature abnormality coefficient array.

[0048] Optionally, in this embodiment, after determining the sound array and temperature array of each target position, further, anomaly analysis can be performed based on the sound array and temperature array, respectively, to obtain a sound anomaly coefficient array and a temperature anomaly coefficient array; further, a first anomaly coefficient array can be determined based on the sound anomaly coefficient array and the temperature anomaly coefficient array.

[0049] In an optional implementation of this embodiment, the sound array at the target location may be compared with the standard sound at the target location to obtain an abnormality analysis result of the sound array and a sound abnormality coefficient array.

[0050] In another optional implementation of this embodiment, the temperature array of the target position may be compared with the standard temperature of the target position to obtain an abnormal analysis result of the temperature array and a temperature abnormality coefficient array.

[0051] Furthermore, the sound anomaly coefficient array and the temperature anomaly coefficient array may be combined to obtain a first anomaly coefficient array.

[0052] Step 130: perform damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determine a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determine an equipment risk assessment result of the target production equipment based on the risk coefficient.

[0053] Optionally, in this embodiment, after acquiring multiple position images of the target position, damage identification can be further performed on each position image. For example, damage path identification, damage degree identification, or damage category identification can be performed on each position image. Furthermore, an array of equipment damage coefficients can be obtained based on the identification results.

[0054] Furthermore, the damage risk coefficient of the target location can be determined based on the equipment damage coefficient array and the first abnormal coefficient array.

[0055] In an optional implementation of this embodiment, the acquired position images and the coordinate information of the target position corresponding to each position image may be input into a first large model obtained by pre-fine-tuning, so as to output an array of equipment damage coefficients.

[0056] Furthermore, the determined equipment damage coefficient array and the first abnormal coefficient array can be input into the second largest model obtained by pre-fine-tuning to output a risk coefficient; wherein the risk coefficient can be a probability value, for example, the probability of generating a risk. In this embodiment, the risk coefficient can be a probability value of a dangerous event occurring at a target location matching each target location, or it can be a probability value of a dangerous event occurring at the target production equipment as a whole, which is not limited in this embodiment.

[0057] Optionally, in this embodiment, after the risk coefficient is determined, the risk level corresponding to the risk coefficient may be further determined, and further, the risk level may be determined as the equipment risk assessment result.

[0058] Among them, the risk level can be low, medium, or high; it can also be level one to level ten, and the higher the level, the more dangerous it is.

[0059] In an optional implementation of this embodiment, if it is determined that the risk level of the equipment risk assessment result is greater than a preset level, that is, the probability of a dangerous incident occurring in the target production equipment is high, an alarm can be issued to prompt relevant personnel to perform maintenance or evacuate in time to prevent the occurrence of safety accidents.

[0060] Step 140: determine an uncontrollable coefficient array of a target plant area based on the risk assessment results of each device, and determine a risk assessment result of the target plant area according to the uncontrollable coefficient array of the target plant area.

[0061] Optionally, in this embodiment, after obtaining the equipment risk assessment results of each production equipment, the uncontrollable coefficient array of the target plant area can be further determined based on the risk assessment results of each equipment, and further, the risk assessment results of the target plant area can be determined based on the uncontrollable coefficient array of the target plant area.

[0062] Optionally, in this embodiment, an uncontrollable coefficient array of a target plant area is determined based on the risk assessment results of each device, and the risk assessment result of the target plant area is determined based on the uncontrollable coefficient array of the target plant area, which may include: obtaining the equipment risk assessment results of all target equipment in the target plant area, and determining the uncontrollable coefficient array based on the uncontrollable coefficients in each risk assessment result and the location information of each target equipment; obtaining the target location information for storing emergency resources in the target plant area; determining the out-of-control coefficient of the target plant area based on the uncontrollable coefficient array and the target location information, and determining the out-of-control coefficient as the risk assessment result of the target plant area.

[0063] In this embodiment, after conducting a risk assessment of the production equipment, there are generally multiple identical devices configured in the factory. Based on the steps in the aforementioned content, risk assessments are conducted on multiple similar devices, and uncontrollable coefficients are obtained respectively. An uncontrollable coefficient array of multiple devices is constructed, and further risk assessment of the factory is conducted to obtain the risk assessment results of the factory.

[0064] In this embodiment, multiple similar equipments as the target equipments are generally arranged in the factory area, such as multiple storage tanks, for production support. The steps in the above content are adopted to conduct monitoring risk assessment on multiple similar equipments to obtain the uncontrollable coefficients of multiple similar equipments of the target equipments in the factory area.

[0065] Furthermore, the device locations of multiple similar devices are obtained. For example, the longitude and latitude coordinates of each similar device in the factory can be obtained as the device location, and then the uncontrollable coefficients of multiple similar devices are marked according to the location to construct an uncontrollable coefficient array.

[0066] Furthermore, the location of emergency resources in the factory, such as the location of fire-fighting and first aid equipment, is obtained. If multiple emergency resources are deployed in the factory, the location coordinates of multiple emergency resources are obtained.

[0067] Then, according to the uncontrollable coefficient array and the emergency resource location, the distance between the device location of each similar device and the nearest emergency resource location is calculated to obtain multiple emergency distances. The emergency distance can be calculated based on the device location coordinates of the similar device and the coordinates of the emergency resource location.

[0068] Furthermore, according to the sizes of multiple emergency distances, weights are configured, for example, the ratio of each emergency distance to multiple emergency distances is calculated to obtain multiple weights, and then multiple weights are used to perform weighted calculations on multiple uncontrollable coefficients in the uncontrollable coefficient array, and the uncontrollable coefficients of multiple similar equipment in the entire plant area based on the weighted calculation of the emergency distance are obtained as the plant area out-of-control coefficient and the plant area risk assessment result. Among them, the larger the emergency distance, the greater the danger of the corresponding equipment when an uncontrollable risk occurs, and therefore, the greater its proportion in the plant area out-of-control coefficient.

[0069] It is understandable that the greater the plant's out-of-control coefficient, the greater the probability of uncontrollable risks such as leakage incidents occurring in the plant as a whole. Based on this, a decision-making reference for plant equipment maintenance is provided to equipment maintenance personnel, such as refurbishing and maintaining equipment in the entire plant.

[0070] The technical solution of this embodiment obtains multiple position images, sound arrays and temperature arrays by performing image acquisition, sound monitoring and temperature monitoring on at least two target positions of the target production equipment in the target plant area respectively; performs abnormal analysis on the sound array and the temperature array respectively to obtain a sound abnormality coefficient array and a temperature abnormality coefficient array, and obtains a first abnormality coefficient array based on the sound abnormality coefficient array and the temperature abnormality coefficient array; performs damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determines a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determines an equipment risk assessment result of the target production equipment based on the risk coefficient; determines an uncontrollable coefficient array of the target plant area based on each equipment risk assessment result, and determines the risk assessment result of the target plant area based on the uncontrollable coefficient array of the target plant area, which can quickly and accurately conduct risk assessment on chemical plants, identify potential risks, and provide assistance for safety management of chemical plants.

[0071] Embodiment 2

[0072] Figure 2 1 is a flow chart of a risk assessment method provided according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution. The technical solution in this embodiment can be combined with each optional solution in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0073] Step 210: perform image acquisition, sound monitoring, and temperature monitoring on at least two target locations of target production equipment in the target factory area to obtain multiple location images, sound arrays, and temperature arrays.

[0074] Step 220, obtaining standard sound data and standard temperature data; determining a first amplitude by which each sound data in the sound array deviates from the standard sound data, and determining a sound anomaly coefficient array based on each of the first amplitudes; and determining a second amplitude by which each temperature data in the temperature array deviates from the standard temperature data, and determining a temperature anomaly coefficient array based on each of the second amplitudes.

[0075] In this embodiment, when performing risk assessment on target production equipment, standard sound data and standard temperature data are reference values ​​used to determine whether there is any abnormality in the target production equipment, which can be obtained through long-term monitoring and data collection.

[0076] In an optional implementation of the present embodiment, a method for acquiring standard sound data may be as follows: installing multiple acoustic sensors at multiple target locations (such as valves and welds) of target production equipment to collect sound data when the equipment is running without any failure or damage; then, performing a statistical analysis on the sound data at multiple target locations to obtain standard sound data for each location. The standard sound may be established by calculating the sound pressure level (dB), frequency, etc., and usually the average of multiple data points is taken, and the acoustic characteristics of the equipment under different loads and temperatures are taken into consideration.

[0077] In another optional implementation of this embodiment, the method for obtaining standard temperature data can be as follows: when the target production equipment is operating normally, the temperature data of each target position of the equipment is collected through a temperature sensor, especially in vulnerable areas such as valves and welds, and the standard temperature data is collected; then the temperature data of different positions are counted to obtain the standard temperature of each position. The standard temperature is usually obtained by averaging the temperature of the equipment that has been operating stably for a period of time to ensure that it can reflect the typical temperature of the equipment under normal working conditions.

[0078] Furthermore, the first amplitude of the deviation of each sound data in the sound array from the standard sound data can be determined, and the sound abnormality coefficient array can be determined based on each of the first amplitudes; in a specific implementation, the sound difference between each sound data in the sound array and the standard sound data can be determined respectively, and the first ratio of each sound difference to the standard sound can be calculated respectively, and each ratio is stored in a set order to obtain the sound abnormality coefficient array.

[0079] Furthermore, the second amplitude of the deviation of each temperature data in the temperature array from the standard temperature data can be determined, and the temperature anomaly coefficient array can be determined based on each second amplitude. In a specific implementation, the temperature difference between each temperature data in the temperature array and the standard temperature data can be determined respectively, and the second ratio of each temperature difference to the standard temperature can be calculated respectively, and each ratio is stored in a set order to obtain the temperature anomaly coefficient array.

[0080] Optionally, the sound difference (such as the sound pressure level difference) between each sound in the sound array and the standard sound is calculated to obtain multiple sound difference values; then, the ratio of the absolute value of the multiple sound difference values ​​to the standard sound is calculated respectively, and the ratio is set as the sound abnormality coefficient, as shown in the following formula: ;in, is the sound abnormality coefficient, is the sound in the sound array, It is the standard sound.

[0081] In this embodiment, the sound difference (such as the sound pressure level difference) between each sound in the sound array and the standard sound data can be calculated to obtain multiple sound difference values; then, the ratio of the absolute value of the multiple sound difference values ​​to the standard sound is calculated respectively, and the ratio is set as the sound abnormality coefficient. The sound abnormality coefficient reflects the degree of deviation between the collected sound and the standard sound, and a sound abnormality coefficient array is generated, wherein the sound abnormality coefficient table can be shown in Table 1 below:

[0082] Table 1: Sound Abnormality Coefficient Table

[0083]

[0084] On the other hand, the temperature difference between each temperature in the temperature array and the standard temperature data is calculated to obtain a plurality of temperature differences, and the ratios of the absolute values ​​of the plurality of temperature differences to the standard temperature are respectively calculated, and the ratios are set as the temperature anomaly coefficient, as shown in the following formula: ;in, is the temperature anomaly coefficient, is the temperature in the temperature array, is the standard temperature.

[0085] It can be understood that the temperature anomaly coefficient reflects the degree of deviation between the collected temperature and the standard temperature. The larger the temperature difference, the larger the temperature anomaly coefficient, indicating that there is a more obvious temperature anomaly at the location, indicating that equipment damage or failure may occur. A temperature anomaly coefficient array is constructed, where the temperature anomaly coefficient table can be shown in Table 2 below:

[0086] Table 2: Temperature anomaly coefficient table

[0087]

[0088] By calculating the ratio between the sound and temperature difference and the standard value, the sound and temperature anomaly coefficients at each location are obtained, which can effectively reflect the abnormal status of the equipment at each location. The larger the value of the anomaly coefficient, the greater the deviation, and the higher the risk of equipment failure or leakage. By continuously monitoring these sound / temperature anomaly coefficients, potential equipment problems can be discovered in a timely manner.

[0089] Step 230: merge the sound anomaly coefficient array and the temperature anomaly coefficient array to obtain the first anomaly coefficient array.

[0090] Optionally, in this embodiment, after the sound anomaly coefficient array and the temperature anomaly coefficient array are determined based on the above steps, the sound anomaly coefficient array and the temperature anomaly coefficient array can be further merged to obtain the first anomaly coefficient array involved in this embodiment.

[0091] In an optional implementation of this embodiment, according to the sound anomaly coefficient array and the temperature anomaly coefficient array, the sound anomaly coefficient and the temperature anomaly coefficient of each position can be averaged, and the average calculation result is used as the basic anomaly coefficient of the position to construct a first anomaly coefficient array, wherein the basic anomaly coefficient table is shown in Table 3 below:

[0092] Table 3: Basic anomaly coefficient table

[0093]

[0094] It should be noted that, in this embodiment, the first abnormal coefficient array is calculated by comprehensively considering the average values ​​of the sound abnormal coefficient and the temperature abnormal coefficient. This array can provide a comprehensive abnormal evaluation result for further risk analysis. Locations in the equipment with large basic abnormal coefficients may have more significant faults or potential problems.

[0095] Step 240: Perform production equipment damage identification on each of the position images to obtain an array of equipment damage coefficients.

[0096] Step 250: perform damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, and determine a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array.

[0097] Step 260: Determine an equipment risk assessment result of the target production equipment based on the risk coefficient.

[0098] Step 270: determine an uncontrollable coefficient array of the target plant area based on the risk assessment results of each device, and determine a risk assessment result of the target plant area according to the uncontrollable coefficient array of the target plant area.

[0099] The solution of this embodiment is to obtain standard sound data and standard temperature data; determine the first amplitude of each sound data in the sound array deviating from the standard sound data, and determine the sound anomaly coefficient array based on each first amplitude; and determine the second amplitude of each temperature data in the temperature array deviating from the standard temperature data, and determine the temperature anomaly coefficient array based on each second amplitude; merge the sound anomaly coefficient array and the temperature anomaly coefficient array to obtain the first anomaly coefficient array, so as to accurately perform an anomaly analysis on the sound array and the temperature array, and integrate the analysis results to obtain the first anomaly coefficient array, which provides a basis for risk assessment of subsequent production equipment.

[0100] Embodiment 3

[0101] Figure 3 1 is a flow chart of a risk assessment method provided according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution. The technical solution in this embodiment can be combined with each optional solution in one or more of the above embodiments. Figure 3 As shown, the method includes:

[0102] Step 310: perform image acquisition, sound monitoring, and temperature monitoring on at least two target locations of target production equipment in the target factory area to obtain multiple location images, sound arrays, and temperature arrays.

[0103] Step 320: perform abnormality analysis on the sound array and the temperature array respectively to obtain a sound abnormality coefficient array and a temperature abnormality coefficient array, and obtain a first abnormality coefficient array based on the sound abnormality coefficient array and the temperature abnormality coefficient array.

[0104] Step 330: Input each of the position images into a damage path identification model for identification to obtain the equipment damage coefficient array.

[0105] Optionally, in this embodiment, after the position images of each target position are acquired, each acquired position image may be further input into a damage path identification model for identification, thereby obtaining an array of equipment damage coefficients.

[0106] Optionally, in this embodiment, the damage path identification model can be trained through the following steps: obtaining a set of sample position images of different positions of different production equipment, and marking the damage coefficient of each sample position image to obtain a set of sample damage coefficients; wherein the damage coefficient is the ratio of the damage size to the maximum damage size of the corresponding position; constructing damage identification paths corresponding to different positions respectively; performing iterative training based on the sample position image set, the sample damage coefficient set and each of the damage identification paths to obtain the damage path identification model.

[0107] In a specific implementation, based on the aging damage monitoring data of similar equipment of the target production equipment, a set of sample position images of different positions of different production equipment can be collected. That is, for multiple key positions of similar equipment (such as valves, welds, etc.), image samples of these positions are obtained through image acquisition technology. These images will show whether there is damage (such as cracks, corrosion, dents, etc.) to the equipment, and provide data support for subsequent damage analysis; further, the damage coefficient in each sample position image is annotated, and the damage coefficient is the ratio of the damage size (the actual size of the damage area obtained by image analysis) to the maximum damage size of the corresponding position (the maximum damage size allowed at the position, usually determined according to factors such as equipment design standards and material strength), and a set of sample damage coefficients is obtained. For example, the sample damage coefficients can be shown in Table 4:

[0108] Table 4: Sample damage coefficient table

[0109]

[0110] It can be understood that the damage coefficient reflects the degree of damage to the equipment at the target location, can quantify the relative size of the damage, and is used for risk assessment and equipment health management; by acquiring images of the target location of the equipment and performing damage analysis, the damage coefficient of each location can be calculated, providing a basis for subsequent safety assessments.

[0111] Furthermore, a damage identification path for each equipment position (such as valves, welds, etc.) can be constructed based on a convolutional neural network. The convolutional neural network can automatically identify damage patterns in images through a feature extraction layer, such as cracks, corrosion, dents and other features. Each path corresponds to an equipment position, and the input image data will be specially processed and identified according to the features of the position; the damage identification path includes an input layer, a convolutional layer (used for convolutional feature extraction, such as cracks, corrosion, etc., each convolution kernel extracts features from the input image through a convolution operation), a pooling layer (used for downsampling, reducing the size of the feature map, while retaining important spatial feature information and reducing the amount of calculation), a fully connected layer (integrating the features extracted by convolution and pooling, and outputting a high-dimensional feature vector) and an output layer, wherein the input data of the input layer is the equipment position image, and the output data is the damage coefficient. Further, the plurality of sample position image sets and the plurality of sample damage coefficient sets are respectively used to perform supervised training on the plurality of damage identification paths. For example, the first sample position image set and the first sample damage coefficient set corresponding to the first position are used to perform supervised training on the damage identification path. First, the input image is passed into the network for forward propagation, and features are extracted through the convolution layer and the pooling layer, and then features are integrated through the fully connected layer, and finally the predicted damage coefficient is calculated through the output layer; further, the error between the predicted damage coefficient and the true label (true damage coefficient) is calculated through the mean square error loss function; further, the gradient of the loss function with respect to the network parameters (convolution kernel, weight, etc.) is calculated through the back propagation algorithm, the network parameters are updated, and the prediction error is gradually reduced; in each round of training, the weights and biases of the convolutional neural network are updated through the gradient descent or optimization algorithm to minimize the loss function, so that the predicted value of the damage coefficient is closer to the true value; the above-mentioned process of forward propagation, loss calculation, back propagation, and parameter update is repeated until the training accuracy of the network reaches the predetermined standard and the loss function converges, and the first damage identification path after training is obtained.

[0112] In this embodiment, the loss function of each damage identification path is as follows:

[0113] ;

[0114] in, is the loss, N is the amount of data in the sample position image set and the sample damage coefficient set, is the damage coefficient of the damage identification path after the i-th group of sample position images are input, is the damage coefficient of the i-th group of samples.

[0115] Furthermore, the multiple position images are respectively input into the corresponding multiple damage identification paths for identification, and multiple damage coefficients are output to construct an equipment damage coefficient array. The solution of this embodiment can improve the intelligence and automation of damage coefficient analysis by identifying damage features through constructing damage identification paths based on convolutional neural networks, thereby effectively improving the efficiency and accuracy of damage calculation and analysis, and providing accurate data support for equipment risk assessment.

[0116] Step 340: determine a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array.

[0117] Optionally, in this embodiment, determining the risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array may include: when the first dimension of the equipment damage coefficient array is the same as the second dimension of the first abnormality coefficient array, determining the product of each first coefficient of the equipment damage coefficient array and each second coefficient of the first abnormality coefficient array to obtain a risk coefficient array; determining the array mean of the risk coefficient array, and determining the array mean as the risk coefficient; or, when the first dimension of the equipment damage coefficient array is different from the second dimension of the first abnormality coefficient array, completing the equipment damage coefficient array or the first abnormality coefficient array based on a preset rule to make the first dimension of the equipment damage coefficient array the same as the second dimension of the first abnormality coefficient array.

[0118] In another optional implementation of the present embodiment, determining the risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array may also include: obtaining historical basic abnormality coefficient data of each equipment under different damage states from historical operation and maintenance and monitoring data of similar equipment based on risk monitoring data of similar equipment; then, for each equipment damage coefficient, obtaining all corresponding basic abnormality coefficients in the historical monitoring data, and calculating their average value to obtain the historical average basic abnormality coefficient; further obtaining the historical average basic abnormality coefficient when each equipment damage coefficient in the equipment damage coefficient array occurs, and constructing a historical basic abnormality coefficient array.

[0119] Furthermore, based on the basic anomaly coefficient array and the historical basic anomaly coefficient array, similarity analysis can be performed on the basic anomaly coefficient and the historical basic anomaly coefficient at each same position. The purpose of similarity calculation is to measure the similarity between the current basic anomaly coefficient and the historical basic anomaly coefficient, such as calculating the ratio of the absolute value of the difference between the basic anomaly coefficient and the historical basic anomaly coefficient to the historical basic anomaly coefficient, and subtracting the ratio from 1 to set the difference between the two as the similarity; as shown in the following formula: ;

[0120] in, is the risk verification coefficient, is the basic anomaly coefficient, is the historical basis anomaly coefficient.

[0121] For example, assuming that the basic anomaly coefficient is 0.06 and the historical basic anomaly coefficient is 0.08, the similarity between the two is 1 minus 0.02 / 0.08, which is equal to 0.75, that is, the similarity is 75%. The smaller the difference between the basic anomaly coefficient and the historical basic anomaly coefficient, the smaller the ratio, and the greater the similarity. The similarity is used as the risk verification coefficient to construct a risk verification coefficient array.

[0122] Furthermore, according to the basic abnormality coefficient array and the risk verification coefficient array, the product of each basic abnormality coefficient and the risk verification coefficient at the same position is calculated, and the product of the two is used as the risk coefficient of the position, and multiple risk coefficients at multiple positions are obtained to construct a risk coefficient array; finally, the multiple risk coefficients at multiple positions are averaged, and the average calculation result is used as the risk coefficient, and the risk coefficient represents the overall risk level of the equipment.

[0123] Step 350: Determine an equipment risk assessment result of the target production equipment based on the risk coefficient.

[0124] Optionally, in this embodiment, determining the equipment risk assessment result of the target production equipment based on the risk coefficient may include: acquiring historical operation and maintenance data of the target production equipment, determining the maintenance information of each target position within the historical time based on the historical operation and maintenance data, and obtaining a historical operation and maintenance data array; determining the historical evaluation equipment damage coefficient of each historical operation and maintenance data pair in the historical operation and maintenance data array, and obtaining a historical equipment damage coefficient array; determining the similarity between each historical equipment damage coefficient and the equipment damage coefficient based on the historical equipment damage coefficient array and the equipment damage coefficient array, and obtaining a damage verification coefficient array; determining the mean of the damage verification coefficient array, and determining the mean as a controllable coefficient; determining a controllable risk coefficient based on the product of the controllable coefficient and the risk coefficient, or determining an uncontrollable coefficient based on the product of the uncontrollable coefficient and the risk coefficient; wherein the sum of the controllable coefficient and the uncontrollable coefficient is 1; determining the controllable risk coefficient or the uncontrollable coefficient as the equipment risk assessment result.

[0125] The solution of this embodiment obtains the operation and maintenance times of the multiple locations in the historical time (such as the last three months) based on the historical operation and maintenance data of the target device. The operation and maintenance times reflect the usage and maintenance frequency of different locations, and can provide relevant information on equipment aging and damage. The more maintenance times, the more serious the aging of the location, and the greater the magnitude of equipment damage, thereby constructing a historical operation and maintenance data array. On the other hand, based on the risk monitoring data of similar equipment, the historical average equipment damage coefficient when each historical operation and maintenance data in the historical operation and maintenance data array appears is obtained. The historical average equipment damage coefficient is calculated based on the historical operation data of similar equipment, reflecting the degree of equipment damage at each location, and obtaining a historical equipment damage coefficient array.

[0126] Furthermore, the similarity of each historical equipment damage coefficient and the equipment damage coefficient at the same position can be calculated based on the historical equipment damage coefficient array and the equipment damage coefficient array. First, the absolute value of the difference between the historical equipment damage coefficient and the equipment damage coefficient is calculated to the historical equipment damage coefficient. The ratio is subtracted from 1, and the difference between the two is set as the damage coefficient similarity. Multiple damage coefficient similarities at multiple positions are obtained, and the damage coefficient similarity is set as the damage verification coefficient to construct a damage verification coefficient array. Then, the multiple damage verification coefficients in the damage verification coefficient array are averaged, and the average calculation result is set as the controllable coefficient; then, the controllable coefficient is subtracted from 1, and the difference between the two is used as the uncontrollable coefficient. Finally, the controllable coefficient is multiplied by the risk coefficient, and the product of the two is used as the controllable risk coefficient. The uncontrollable coefficient is multiplied by the risk coefficient, and the product of the two is used as the uncontrollable coefficient. The controllable risk coefficient and the uncontrollable coefficient are used as the equipment risk assessment results.

[0127] It is understandable that the larger the uncontrollable coefficient, the greater the abnormal amplitude caused by other damages besides the damage caused by normal operation and maintenance of the target equipment, and the larger the controllable risk coefficient, the greater the abnormal amplitude caused by the damage caused by normal operation and maintenance of the target equipment. It can be used as a reference for equipment maintenance personnel to perform equipment maintenance or scrapping, and transfer chemicals in time to avoid risk events. For example, the uncontrollable coefficient and controllable risk coefficient of similar equipment with leakage and other accidents can be used to distinguish and carry out further maintenance and processing.

[0128] The solution of the embodiment of the present invention can effectively distinguish between controllable and uncontrollable risks by combining the similarity analysis of historical equipment damage coefficients with the current equipment damage coefficients, and perform dynamic assessment based on the actual condition and aging of the equipment; this method significantly improves the accuracy of risk assessment, and can help the maintenance team give priority to high-risk areas, or give priority to areas with larger uncontrollable coefficients, thereby improving the scientificity and efficiency of equipment management.

[0129] Step 360: determine an uncontrollable coefficient array of the target plant area based on the risk assessment results of each device, and determine a risk assessment result of the target plant area according to the uncontrollable coefficient array of the target plant area.

[0130] The solution of the embodiment of the present invention obtains multiple position images, sound arrays and temperature arrays by performing image acquisition, sound monitoring and temperature monitoring on multiple positions of the target equipment; then, the sound array and temperature array are analyzed for abnormality according to standard sound and standard temperature to obtain sound abnormality coefficient arrays and temperature abnormality coefficient arrays, and a basic abnormality coefficient array is calculated to obtain; further, equipment damage identification is performed on the multiple position images to obtain an equipment damage coefficient array, and damage verification is performed with the basic abnormality coefficient array to obtain a risk coefficient array, and a risk coefficient is calculated to obtain; then, the historical operation and maintenance data arrays of the multiple positions are collected and compared with the basic abnormality coefficient array. The equipment damage coefficient array performs controllable damage verification, and calculates the controllable coefficient and the uncontrollable coefficient; finally, the controllable coefficient and the uncontrollable coefficient are multiplied by the risk coefficient to obtain the controllable risk coefficient and the uncontrollable coefficient as the equipment risk assessment result; that is to say, by combining multi-dimensional data and intelligent algorithms, a dynamic assessment of equipment safety risks can be achieved. At the same time, by generating controllable equipment risk assessment results and uncontrollable equipment risk assessment results, the changes in equipment can be more accurately reflected, and the scientificity, timeliness and accuracy of the equipment risk assessment results can be significantly improved, thereby effectively reducing misjudgments and missed judgments, and improving the safety and reliability of equipment storage.

[0131] Embodiment 4

[0132] Figure 4 1 is a flow chart of a risk assessment method provided according to Embodiment 4 of the present invention. This embodiment is a further refinement of the above technical solution. The technical solution in this embodiment can be combined with each optional solution in one or more of the above embodiments. Figure 4 As shown, the method includes:

[0133] Step 410: perform image acquisition, sound monitoring, and temperature monitoring on at least two target locations of target production equipment in the target factory area to obtain multiple location images, sound arrays, and temperature arrays.

[0134] Step 420: perform abnormality analysis on the sound array and the temperature array respectively to obtain a sound abnormality coefficient array and a temperature abnormality coefficient array, and obtain a first abnormality coefficient array based on the sound abnormality coefficient array and the temperature abnormality coefficient array.

[0135] Step 430: perform damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determine a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determine an equipment risk assessment result of the target production equipment based on the risk coefficient.

[0136] Step 440: Acquire video data within the target plant area, analyze the video data, and obtain the operating behavior of the staff in the target plant area and the material storage information in the target plant area; determine the staff risk assessment result based on the operating behavior of the staff in the target plant area, and determine the material placement risk assessment result based on the material storage information in the target plant area; determine the risk assessment result of the target plant area based on the equipment risk assessment result, the staff risk assessment result and the material placement risk assessment result.

[0137] The operating behavior of the staff may include the working behavior of the staff, and may also include safety behaviors such as whether the staff wears a safety helmet, which is not limited in this embodiment.

[0138] Optionally, in this embodiment, after obtaining the equipment risk assessment results of the target production equipment, the video data in the target plant area can be further obtained through various cameras deployed in the target plant area, and the obtained video data can be further analyzed. For example, the obtained video data can be input into a pre-fine-tuned video data analysis model to obtain the operating behavior of the staff in the target plant area and the material storage information in the target plant area.

[0139] Furthermore, the risk assessment results of the staff can be determined based on the operating behavior of the staff in the target factory area, and the risk assessment results of material placement can be determined based on the material information in the target factory area; illustratively, in this embodiment, the risk assessment results of the staff and the risk assessment results of material placement can be determined based on the pre-fine-tuned operating behavior risk assessment model and the material placement risk assessment model, respectively.

[0140] Furthermore, the risk assessment results of the target plant area can be determined based on the equipment risk assessment results, the staff risk assessment results and the material placement risk assessment results; illustratively, different weights can be set for the equipment risk assessment results, the staff risk assessment results and the material placement risk assessment results based on actual conditions; further, the products of each weight value and each risk assessment result can be summed respectively to obtain the final risk assessment result.

[0141] The solution of this embodiment can obtain video data within the target factory area, analyze the video data, and obtain the operating behavior of the staff in the target factory area and the material storage information in the target factory area; determine the staff risk assessment results based on the operating behavior of the staff in the target factory area, and determine the material placement risk assessment results based on the material storage information in the target factory area; determine the risk assessment results of the target factory area based on the equipment risk assessment results, the staff risk assessment results and the material placement risk assessment results. The risk assessment results of the target factory area can be determined based on different dimensions, providing a basis for accurately determining the risk assessment results of the target factory area.

[0142] Embodiment 5

[0143] Figure 5 Schematic diagram of the structure of a risk assessment device provided according to Embodiment 5 of the present invention. Figure 5 As shown, the device includes: a data acquisition module 510, a first abnormal coefficient array determination module 520, an equipment risk assessment result determination module 530 and a plant area risk assessment result determination module 540.

[0144] The data acquisition module 510 is used to respectively perform image acquisition, sound monitoring and temperature monitoring of at least two target positions of the target production equipment in the target factory area, and obtain multiple position images, sound arrays and temperature arrays;

[0145] A first abnormal coefficient array determination module 520 is used to perform abnormal analysis on the sound array and the temperature array respectively to obtain a sound abnormal coefficient array and a temperature abnormal coefficient array, and obtain a first abnormal coefficient array based on the sound abnormal coefficient array and the temperature abnormal coefficient array;

[0146] an equipment risk assessment result determination module 530, configured to perform damage identification on the target production equipment based on each of the position images, obtain an equipment damage coefficient array, determine a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determine an equipment risk assessment result of the target production equipment based on the risk coefficient;

[0147] The plant area risk assessment result determination module 540 is used to determine the uncontrollable coefficient array of the target plant area based on the risk assessment results of each device, and determine the risk assessment result of the target plant area according to the uncontrollable coefficient array of the target plant area.

[0148] The scheme of this embodiment is to perform image acquisition, sound monitoring and temperature monitoring of at least two target positions of the target production equipment in the target plant area through the data acquisition module to obtain multiple position images, sound arrays and temperature arrays; to perform abnormal analysis on the sound array and the temperature array through the first abnormal coefficient array determination module to obtain a sound abnormal coefficient array and a temperature abnormal coefficient array, and to obtain a first abnormal coefficient array based on the sound abnormal coefficient array and the temperature abnormal coefficient array; to identify damage to the target production equipment based on each of the position images through the equipment risk assessment result determination module to obtain an equipment damage coefficient array, to determine a risk coefficient based on the equipment damage coefficient array and the first abnormal coefficient array, and to determine an equipment risk assessment result of the target production equipment based on the risk coefficient; to determine an uncontrollable coefficient array of the target plant area based on each equipment risk assessment result through the plant area risk assessment result determination module, and to determine the risk assessment result of the target plant area based on the uncontrollable coefficient array of the target plant area, so as to quickly and accurately conduct risk assessment on chemical plants, identify potential risks, and provide assistance for safety management of chemical plants.

[0149] In an optional implementation of this embodiment, the data acquisition module 510 is specifically used to obtain the target position of the target production equipment, and respectively collect the position image, sound data and temperature data of each target position; wherein the target position includes: valves and welds;

[0150] Obtaining the sound array based on the position information of each target position and each sound data;

[0151] The temperature array is obtained based on the position information of each target position and the temperature data.

[0152] In an optional implementation of this embodiment, the first abnormal coefficient array determination module 520 is specifically used to obtain standard sound data and standard temperature data;

[0153] Determine a first amplitude by which each sound data in the sound array deviates from the standard sound data, and determine a sound abnormality coefficient array based on each of the first amplitudes; and,

[0154] Determine a second amplitude by which each temperature data in the temperature array deviates from the standard temperature data, and determine a temperature anomaly coefficient array based on each second amplitude;

[0155] The sound anomaly coefficient array and the temperature anomaly coefficient array are combined to obtain the first anomaly coefficient array.

[0156] In an optional implementation of the present embodiment, the first abnormal coefficient array determination module 520 is further specifically used to respectively determine the sound difference between each sound data in the sound array and the standard sound data, and respectively calculate the first ratio of each sound difference to the standard sound, store each ratio in a set order, and obtain the sound abnormal coefficient array; and,

[0157] The temperature difference between each temperature data in the temperature array and the standard temperature data is determined respectively, and the second ratio of each temperature difference to the standard temperature is calculated respectively, and each ratio is stored in a set order to obtain the temperature anomaly coefficient array.

[0158] In an optional implementation of this embodiment, the equipment risk assessment result determination module 530 is specifically used to input each of the position images into the damage path recognition model for recognition, and obtain the equipment damage coefficient array;

[0159] The damage path identification model is obtained through the following steps:

[0160] A set of sample position images of different positions of different production equipment is obtained, and the damage coefficient of each sample position image is annotated to obtain a set of sample damage coefficients; wherein the damage coefficient is the ratio of the damage size to the maximum damage size of the corresponding position;

[0161] construct damage identification paths corresponding to different positions respectively;

[0162] Perform iterative training based on the sample position image set, the sample damage coefficient set and each of the damage identification paths to obtain the damage path identification model;

[0163] In a case where the first dimension of the equipment damage coefficient array is the same as the second dimension of the first abnormal coefficient array, determining the product of each first coefficient of the equipment damage coefficient array and each second coefficient of the first abnormal coefficient array to obtain a risk coefficient array;

[0164] Determining an array mean of the risk coefficient array, and determining the array mean as the risk coefficient;

[0165] or,

[0166] In a case where the first dimension of the device damage coefficient array is different from the second dimension of the first abnormal coefficient array, a completion operation is performed on the device damage coefficient array or the first abnormal coefficient array based on a preset rule so that the first dimension of the device damage coefficient array is the same as the second dimension of the first abnormal coefficient array;

[0167] Acquire historical operation and maintenance data of the target production equipment, determine maintenance information of each target location within a historical period based on the historical operation and maintenance data, and obtain an array of historical operation and maintenance data;

[0168] Determine the historical evaluation equipment damage coefficient of each historical operation and maintenance data pair in the historical operation and maintenance data array to obtain a historical equipment damage coefficient array;

[0169] According to the historical equipment damage coefficient array and the equipment damage coefficient array, determining the similarity between each historical equipment damage coefficient and the equipment damage coefficient, and obtaining a damage verification coefficient array;

[0170] Determining a mean value of the damage verification coefficient array, and determining the mean value as a controllable coefficient;

[0171] Determine a controllable risk coefficient based on the product of the controllable coefficient and the risk coefficient, or determine an uncontrollable coefficient based on the product of the uncontrollable coefficient and the risk coefficient; wherein the sum of the controllable coefficient and the uncontrollable coefficient is 1;

[0172] The controllable risk coefficient or the uncontrollable coefficient is determined as the equipment risk assessment result.

[0173] In an optional implementation of this embodiment, the plant area risk assessment result determination module 540 is specifically used to obtain the equipment risk assessment results of all target equipment in the target plant area, and determine the uncontrollable coefficient array based on the uncontrollable coefficient in each of the risk assessment results and the location information of each of the target equipment;

[0174] Obtaining target location information of emergency resources stored in the target plant area;

[0175] The out-of-control coefficient of the target plant area is determined according to the uncontrollable coefficient array and the target position information, and the out-of-control coefficient is determined as a risk assessment result of the target plant area.

[0176] In an optional implementation of this embodiment, the risk assessment device further includes: a comprehensive risk assessment module, which is used to obtain video data in the target plant area, analyze the video data, and obtain the operating behavior of the staff in the target plant area and the material storage information in the target plant area;

[0177] Determine a staff risk assessment result based on the operating behavior of the staff in the target plant area, and determine a material placement risk assessment result based on the material storage information in the target plant area;

[0178] The risk assessment result of the target plant area is determined based on the equipment risk assessment result, the staff risk assessment result and the material placement risk assessment result.

[0179] The risk assessment device provided in the embodiment of the present invention can execute the risk assessment method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0180] In the technical solution of the embodiment of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of data (such as image data, sound data and temperature data, etc.) of the production equipment involved shall comply with the provisions of relevant laws and regulations and shall not violate public order and good morals.

[0181] Embodiment 6

[0182] Figure 6 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0183] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The input / output (I / O) interface 15 is also connected to the bus 14.

[0184] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0185] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a risk assessment method, which includes: performing image acquisition, sound monitoring, and temperature monitoring on at least two target positions of the target production equipment in the target plant area, respectively, to obtain multiple position images, sound arrays, and temperature arrays; performing abnormal analysis on the sound array and the temperature array, respectively, to obtain a sound abnormality coefficient array and a temperature abnormality coefficient array, and obtaining a first abnormality coefficient array based on the sound abnormality coefficient array and the temperature abnormality coefficient array; performing damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determining a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determining an equipment risk assessment result of the target production equipment based on the risk coefficient; determining an uncontrollable coefficient array of the target plant area based on each equipment risk assessment result, and determining the risk assessment result of the target plant area based on the uncontrollable coefficient array of the target plant area.

[0186] In some embodiments, the risk assessment method may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the risk assessment method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the risk assessment method in any other appropriate manner (e.g., by means of firmware).

[0187] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0188] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0189] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0190] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0191] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0192] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0193] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0194] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

[0195] An embodiment of the present invention further provides a computer program product, including a computer program, which, when executed by a processor, implements the database detection method provided in any embodiment of the present application.

[0196] In the process of implementation, the computer program product can be written in one or more programming languages ​​or a combination thereof to perform the computer program code of the present invention, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0197] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0198] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A risk assessment method, characterized in that: include: Perform image acquisition, sound monitoring, and temperature monitoring on at least two target locations of target production equipment in a target factory area to obtain multiple location images, sound arrays, and temperature arrays; Performing abnormality analysis on the sound array and the temperature array respectively to obtain a sound abnormality coefficient array and a temperature abnormality coefficient array, and obtaining a first abnormality coefficient array based on the sound abnormality coefficient array and the temperature abnormality coefficient array; Performing damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determining a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determining an equipment risk assessment result of the target production equipment based on the risk coefficient; An uncontrollable coefficient array of a target plant area is determined based on the risk assessment results of each device, and a risk assessment result of the target plant area is determined according to the uncontrollable coefficient array of the target plant area.

2. The risk assessment method according to claim 1, characterized in that: The method of performing image acquisition, sound monitoring and temperature monitoring on at least two target positions of target production equipment in the target factory area to obtain multiple position images, sound arrays and temperature arrays includes: Acquire the target position of the target production equipment, and respectively collect the position image, sound data and temperature data of each target position; wherein the target position includes: valves and welds; Obtaining the sound array based on the position information of each target position and each sound data; The temperature array is obtained based on the position information of each target position and the temperature data.

3. The risk assessment method according to claim 1, characterized in that: The method of performing abnormal analysis on the sound array and the temperature array respectively to obtain a sound abnormal coefficient array and a temperature abnormal coefficient array, and obtaining a first abnormal coefficient array based on the sound abnormal coefficient array and the temperature abnormal coefficient array, comprises: Obtain standard sound data and standard temperature data; Determine a first amplitude by which each sound data in the sound array deviates from the standard sound data, and determine a sound abnormality coefficient array based on each of the first amplitudes; and, Determine a second amplitude by which each temperature data in the temperature array deviates from the standard temperature data, and determine a temperature anomaly coefficient array based on each second amplitude; The sound anomaly coefficient array and the temperature anomaly coefficient array are combined to obtain the first anomaly coefficient array.

4. The risk assessment method according to claim 3, characterized in that: Determining a first amplitude by which each sound data in the sound array deviates from the standard sound data, and determining a sound abnormality coefficient array based on each of the first amplitudes; And, determining a second amplitude of each temperature data in the temperature array deviating from the standard temperature data, and determining a temperature anomaly coefficient array based on each second amplitude, comprising: respectively determining the sound difference between each sound data in the sound array and the standard sound data, respectively calculating the first ratio of each sound difference to the standard sound, storing each ratio in a set order, and obtaining the sound abnormality coefficient array; as well as, The temperature difference between each temperature data in the temperature array and the standard temperature data is determined respectively, and the second ratio of each temperature difference to the standard temperature is calculated respectively, and each ratio is stored in a set order to obtain the temperature anomaly coefficient array.

5. The risk assessment method according to claim 1, characterized in that: The step of performing damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determining a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determining an equipment risk assessment result of the target production equipment based on the risk coefficient includes: Inputting each of the position images into a damage path identification model for identification to obtain the equipment damage coefficient array; The damage path identification model is obtained through the following steps: A set of sample position images of different positions of different production equipment is obtained, and the damage coefficient of each sample position image is annotated to obtain a set of sample damage coefficients; wherein the damage coefficient is the ratio of the damage size to the maximum damage size of the corresponding position; construct damage identification paths corresponding to different positions respectively; Perform iterative training based on the sample position image set, the sample damage coefficient set and each of the damage identification paths to obtain the damage path identification model; In a case where the first dimension of the equipment damage coefficient array is the same as the second dimension of the first abnormal coefficient array, determining the product of each first coefficient of the equipment damage coefficient array and each second coefficient of the first abnormal coefficient array to obtain a risk coefficient array; Determining an array mean of the risk coefficient array, and determining the array mean as the risk coefficient; or, In a case where the first dimension of the device damage coefficient array is different from the second dimension of the first abnormal coefficient array, a completion operation is performed on the device damage coefficient array or the first abnormal coefficient array based on a preset rule so that the first dimension of the device damage coefficient array is the same as the second dimension of the first abnormal coefficient array; Acquire historical operation and maintenance data of the target production equipment, determine maintenance information of each target location within a historical period based on the historical operation and maintenance data, and obtain an array of historical operation and maintenance data; Determine the historical evaluation equipment damage coefficient of each historical operation and maintenance data pair in the historical operation and maintenance data array to obtain a historical equipment damage coefficient array; According to the historical equipment damage coefficient array and the equipment damage coefficient array, determining the similarity between each historical equipment damage coefficient and the equipment damage coefficient, and obtaining a damage verification coefficient array; Determining a mean value of the damage verification coefficient array, and determining the mean value as a controllable coefficient; Determine a controllable risk coefficient based on the product of the controllable coefficient and the risk coefficient, or determine an uncontrollable coefficient based on the product of the uncontrollable coefficient and the risk coefficient; wherein the sum of the controllable coefficient and the uncontrollable coefficient is 1; The controllable risk coefficient or the uncontrollable coefficient is determined as the equipment risk assessment result.

6. The risk assessment method according to claim 1, characterized in that: The step of determining the uncontrollable coefficient array of the target plant area based on the risk assessment results of each device, and determining the risk assessment result of the target plant area according to the uncontrollable coefficient array of the target plant area includes: Obtaining equipment risk assessment results of all target equipment in the target plant area, and determining an uncontrollable coefficient array based on the uncontrollable coefficients in each of the risk assessment results and the location information of each of the target equipment; Obtaining target location information of emergency resources stored in the target plant area; The out-of-control coefficient of the target plant area is determined according to the uncontrollable coefficient array and the target position information, and the out-of-control coefficient is determined as a risk assessment result of the target plant area.

7. The risk assessment method according to claim 1, characterized in that: The method further comprises: Acquire video data in the target factory area, analyze the video data, and obtain operating behaviors of staff in the target factory area and material storage information in the target factory area; Determine a staff risk assessment result based on the operating behavior of the staff in the target plant area, and determine a material placement risk assessment result based on the material storage information in the target plant area; The risk assessment result of the target plant area is determined based on the equipment risk assessment result, the staff risk assessment result and the material placement risk assessment result.

8. A risk assessment device, characterized in that: include: A data acquisition module, used to respectively perform image acquisition, sound monitoring and temperature monitoring of at least two target positions of target production equipment in a target factory area, and obtain multiple position images, sound arrays and temperature arrays; A first abnormal coefficient array determination module is used to perform abnormal analysis on the sound array and the temperature array respectively to obtain a sound abnormal coefficient array and a temperature abnormal coefficient array, and obtain a first abnormal coefficient array based on the sound abnormal coefficient array and the temperature abnormal coefficient array; an equipment risk assessment result determination module, configured to perform damage identification on the target production equipment based on each of the position images to obtain an equipment damage coefficient array, determine a risk coefficient based on the equipment damage coefficient array and the first abnormality coefficient array, and determine an equipment risk assessment result of the target production equipment based on the risk coefficient; The plant area risk assessment result determination module is used to determine the uncontrollable coefficient array of the target plant area based on the risk assessment results of each device, and determine the risk assessment result of the target plant area according to the uncontrollable coefficient array of the target plant area.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the risk assessment method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the risk assessment method according to any one of claims 1 to 7 when executed.

Images