Hazardous chemical substance storage state detection method and device

Abstract

The embodiment of the invention provides a hazardous chemical substance storage state detection method and device.The hazardous chemical substance storage state detection method comprises the steps that area detection indexes of a hazardous chemical substance storage area are obtained, the hazardous chemical substance storage area comprises a plurality of sub-areas, and the area detection indexes are used for reflecting detection priorities of the sub-areas; the region detection index is obtained by processing historical detection data of the hazardous chemical substance storage region through the index generation model; based on the area detection indexes, hazardous chemical substance storage state detection is conducted on the multiple sub-areas, and hazardous chemical substance storage state detection results are obtained. According to the scheme, the historical detection data is analyzed through the index generation model to determine the risk level of each sub-region, and the region detection index reflecting the detection priority of each sub-region is generated based on the risk level of each sub-region, so that differential detection is realized, and the detection efficiency and accuracy of the abnormal state of the hazardous chemical substance are improved.

Description

TECHNICAL FIELD

[0001] Embodiments of the present specification relate to the technical field of anomaly detection, and in particular to a dangerous chemical storage state detection method and device. BACKGROUND

[0002] Dangerous chemicals usually have characteristics such as flammability, explosiveness, corrosiveness, toxicity, or reactivity. If the abnormality of the storage state of dangerous chemicals (such as leakage, deterioration, mixing of incompatible substances, and out-of-control environmental parameters) cannot be discovered in time, it is easy to cause major safety accidents such as fire, explosion, and poisoning. Therefore, efficient and accurate anomaly detection of the storage state of dangerous chemicals is of great significance to improve the safety protection capability in specific scenarios.

[0003] Currently, the detection method of the storage state of dangerous chemicals in the industry is mainly manual regular inspection. This method requires the inspection personnel to check each dangerous chemical storage cabinet one by one according to the plan, and checks the storage state of the dangerous chemicals in the cabinet by visual observation and manual recording. However, this manual inspection mode is highly dependent on personal experience, and it is difficult for personnel with insufficient experience or limited energy to discover dangerous chemicals with abnormalities in time and accurately, which makes the detection accuracy and efficiency of the storage state of dangerous chemicals low. Therefore, there is an urgent need for a detection method that can improve the detection accuracy and efficiency of the storage state of dangerous chemicals. SUMMARY

[0004] Therefore, the embodiments of the present specification provide a dangerous chemical storage state detection method. One or more embodiments of the present specification also relate to a dangerous chemical storage state detection device, a computing device, a computer-readable storage medium, and a computer program product to solve the technical defects in the prior art.

[0005] According to a first aspect of the embodiments of the present specification, a dangerous chemical storage state detection method is provided, comprising:

[0006] Obtaining a region detection index of a dangerous chemical storage region, wherein the dangerous chemical storage region includes a plurality of sub-regions, the region detection index is used to reflect the detection priority of the plurality of sub-regions, and the region detection index is obtained by processing historical detection data of the dangerous chemical storage region by an index generation model;

[0007] Based on the region detection index, the dangerous chemical storage state detection of the plurality of sub-regions is performed respectively to obtain a dangerous chemical storage state detection result of the dangerous chemical storage region.

[0008] According to a second aspect of the embodiments of the present specification, a dangerous chemical storage state detection device is provided, comprising:

[0009] The acquisition module is configured to acquire a region detection index of the hazardous chemical storage region, wherein the hazardous chemical storage region comprises a plurality of sub-regions, the region detection index is used to reflect detection priorities of the plurality of sub-regions, and the region detection index is obtained by processing historical detection data of the hazardous chemical storage region by an index generation model.

[0010] The detection module is configured to perform hazardous chemical storage state detection on the plurality of sub-regions respectively based on the region detection index, and obtain a hazardous chemical storage state detection result of the hazardous chemical storage region.

[0011] According to a third aspect of an embodiment of the present specification, a computing device is provided, comprising:

[0012] a memory and a processor;

[0013] The memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions, so as to implement the steps of the hazardous chemical storage state detection method.

[0014] According to a fourth aspect of an embodiment of the present specification, a computer readable storage medium is provided, which stores computer executable instructions, and the instructions are executed by a processor to implement the steps of the hazardous chemical storage state detection method.

[0015] According to a fifth aspect of an embodiment of the present specification, a computer program product is provided, comprising computer programs / instructions, and the computer programs / instructions are executed by a processor to implement the steps of the hazardous chemical storage state detection method.

[0016] One embodiment of the present specification implements a hazardous chemical storage state detection method, comprising: acquiring a region detection index of a hazardous chemical storage region, wherein the hazardous chemical storage region comprises a plurality of sub-regions, the region detection index is used to reflect detection priorities of the plurality of sub-regions, and the region detection index is obtained by processing historical detection data of the hazardous chemical storage region by an index generation model; and performing hazardous chemical storage state detection on the plurality of sub-regions respectively based on the region detection index, and obtaining a hazardous chemical storage state detection result of the hazardous chemical storage region. The risk levels of the sub-regions are determined by analyzing the historical detection data by the index generation model, the region detection index reflecting the detection priorities of the sub-regions is generated based on the risk levels of the sub-regions, and thus differential detection is realized, and the detection efficiency and accuracy of the abnormal state of the hazardous chemicals are improved. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is a flowchart of a hazardous chemical storage state detection method provided by one embodiment of the present specification;

[0018] Figure 2is a processing procedure flow diagram of a hazardous chemical storage state detection method provided by an embodiment of the present specification;

[0019] Figure 3 is a structural schematic diagram of a hazardous chemical storage state detection device provided by an embodiment of the present specification;

[0020] Figure 4 is a structural block diagram of a computing device provided by an embodiment of the present specification. DETAILED DESCRIPTION

[0021] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present specification. However, the present specification can be practiced without the specific details, other than in the examples described herein. In other instances, well-known methods have not been described in detail in order not to unnecessarily obscure the present specification.

[0022] The terminology used in one or more embodiments of the present specification is for the purpose of describing particular embodiments only and is not intended to be limiting of one or more embodiments of the present specification. As used in one or more embodiments of the present specification and the accompanying claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in one or more embodiments of the present specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0023] It will be understood that, although the terms first, second, etc. can be used herein to describe various information, these terms are not intended to denote a temporal or chronological order. Rather, these terms are used solely to distinguish one from another only. For example, without departing from the scope of one or more embodiments of the present specification, first can be termed second, and similarly, second can be termed first. The word "if' can be interpreted to mean "when" or "upon" or "in response to determining" depending on the context.

[0024] In addition, it should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in one or more embodiments of the present specification are all information and data authorized by the user or authorized by all parties, and the collection, use and processing of related data need to comply with relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation portal for user to choose authorization or refusal.

[0025] In the present specification, a hazardous chemical storage state detection method is provided, and the present specification also relates to a hazardous chemical storage state detection device, a computing device, a computer readable storage medium, and a computer program product, which are described in detail in the following embodiments.

[0026] Referring to Figure 1 , Figure 1 A flowchart of a hazardous chemical storage state detection method according to an embodiment of the present specification is shown, which specifically includes the following steps 102-104.

[0027] Step 102: Obtain a region detection index of a hazardous chemical storage region, wherein the hazardous chemical storage region includes a plurality of sub-regions, the region detection index is used to reflect the detection priority of the plurality of sub-regions, and the region detection index is obtained by processing historical detection data of the hazardous chemical storage region by an index generation model.

[0028] The hazardous chemical storage region refers to a region that needs to be detected for the storage state of hazardous chemicals, such as a warehouse, a data machine room, a storage room for storing hazardous chemicals and medicines, etc. In order to facilitate management or anomaly detection, the hazardous chemical storage region is divided into a plurality of sub-regions.

[0029] The sub-region can refer to an independent hazardous chemical storage cabinet, or a hazardous chemical storage cabinet group composed of a plurality of hazardous chemical storage cabinets.

[0030] The historical detection data is the relevant data recorded in the past when the hazardous chemical storage state detection is performed in the hazardous chemical storage region. It includes but is not limited to: the anomaly record of each sub-region at each detection time in the past (time, goods, and the type of anomaly corresponding to the goods, etc.), environmental data (temperature, humidity, etc.).

[0031] The index generation model "learns" the hidden rules in the historical detection data, thereby predicting which region is more likely to have an anomaly in the future.

[0032] It should be understood that if a region has a higher frequency of abnormality in history, it is likely that the region is continuously affected by certain stable and non-eliminated negative factors. For example, a dangerous chemical storage cabinet is prone to fluctuations in environmental conditions such as temperature and humidity due to its proximity to the door, which makes the dangerous chemicals stored therein prone to deterioration. Therefore, as long as the dangerous chemical storage cabinet is still close to the door, the environmental conditions of the cabinet are still prone to fluctuations, and the probability of abnormality is relatively high. For another example, a dangerous chemical storage cabinet is closest to the work area and is used most frequently, and high-frequency use itself brings a higher probability of misplacement and damage. Therefore, as long as the dangerous chemical storage cabinet is still close to the work area, the probability of abnormality of the cabinet is still relatively high. Therefore, historical detection data can reflect the inherent and persistent systematic risk factors in a region to some extent. In this way, based on the analysis of the historical detection data, the risk levels of the plurality of sub-regions at the current time can be determined, and the detection priorities of the plurality of sub-regions are determined based on the risk levels of the plurality of sub-regions.

[0033] In actual applications, there are various ways to obtain the region detection index of the dangerous chemical storage region, which are specifically selected according to actual conditions, and the embodiments of the present specification do not make any limitation thereto.

[0034] In an optional implementation of the present embodiment, the region detection index generated in advance for the dangerous chemical storage region can be read from a database or other data acquisition device.

[0035] In another optional implementation of the present embodiment, the region detection index of the dangerous chemical storage region can be generated based on the historical detection data of the dangerous chemical storage region.

[0036] Specifically, obtaining the region detection index of the dangerous chemical storage region comprises:

[0037] obtaining historical detection data;

[0038] parsing the historical detection data to determine generation prompt information of the dangerous chemical storage region, wherein the generation prompt information comprises at least one of frequency prompt information and abnormality prompt information, the frequency prompt information is used to guide an index generation model to generate a region detection index based on the detection frequency of the plurality of sub-regions, and the abnormality prompt information is used to guide the index generation model to generate a region detection index based on the abnormal state of the plurality of sub-regions;

[0039] inputting the generation prompt information and the historical detection data into the index generation model to obtain the region detection index of the dangerous chemical storage region.

[0040] Specifically, the system first preprocesses the historical detection data and integrates it into a structured data table. This data table takes each sub-area inspection record as the basic unit, and the fields cover timestamps, sub-area IDs (Identification, identity), detection results (normal / abnormal), abnormal types, and various environmental data (such as temperature, humidity, and harmful gas content).

[0041] On this basis, the system analyzes the historical detection data of multiple sub-areas, extracts key indicators, including the detection frequency and abnormal frequency of each sub-area. Subsequently, the system automatically generates corresponding prompt information based on pre-set rules and strategies.

[0042] For example, the abnormal prompt information generation rule: if the number of sub-areas with abnormal frequency exceeding a certain threshold reaches or exceeds the first set threshold within a pre-set statistical period, it is determined that the overall abnormal risk of the system is high. At this time, based on the historical abnormal frequency of each sub-area, abnormal prompt information is generated to guide the model to preferentially detect high-risk areas.

[0043] The frequency prompt information generation rule: if the number of sub-areas with detection frequency below a certain standard reaches or exceeds the second set threshold, it is determined that the system has a risk of insufficient detection coverage. At this time, based on the historical detection frequency of each sub-area, frequency prompt information is generated to guide the model to preferentially cover long-unchecked areas (high-risk areas).

[0044] It needs to be clear that whether it is frequency prompt information or abnormal prompt information, its essence is to quantitatively evaluate the current risk level of each sub-area from different dimensions. Among them, the abnormal prompt information starts from the perspective of the abnormal frequency of each sub-area: the higher the frequency of abnormal occurrence in the history of a sub-area, the greater the severity, and the higher the potential risk, so the risk level is also rated higher. The frequency prompt information starts from the perspective of detection frequency, for example, the longer the interval between detections of a sub-area, the greater the possibility of potential problems inside being undetected, and the higher the uncertainty, so its risk level is also rated as high.

[0045] The two types of prompt information are used to guide the index generation model to determine the real-time risk level of each sub-area from different dimensions, so as to determine the area detection index reflecting the detection priority of each sub-area according to the risk level of each sub-area. This index ensures that detection resources can always be preferentially invested in the sub-area with the highest risk and most attention, so as to realize the preferential detection of high-risk areas, early warning and intervention.

[0046] Among them, generating prompt information is a guide or instruction to explicitly tell the "index generation model" to focus on analyzing historical data from which perspective or follow what strategy.

[0047] The frequency prompt information is a specific "generation prompt information", which is used to guide the model to generate detection priorities according to the historical detection frequencies of the sub-regions.

[0048] For example, the frequency prompt information can be "find the sub-regions with the least inspection frequency in the last 30 days and give them higher detection priorities".

[0049] In this way, the model can be guided to pay more attention to the "low-frequency" regions that have not been detected for a long time, preventing unknown risks from being incubated due to long-term neglect.

[0050] For another example, the frequency prompt information can be "find the sub-regions with the most inspection frequency in the last 30 days and give them higher detection priorities".

[0051] In this way, the model can be guided to pay more attention to the regions that have been identified as critical or high-risk and thus need to be intensively monitored.

[0052] The abnormal prompt information is another specific "generation prompt information", which is used to guide the model to generate detection priorities according to the historical abnormal states of the sub-regions. The purpose is to guide the model to pay more attention to the "high-risk" regions with a high incidence of abnormalities in history.

[0053] For example, the abnormal prompt information is "find the sub-systems with the most abnormal records in history or the most recent abnormal occurrences and give them higher detection priorities".

[0054] In this scheme, first, historical detection data is collected, and the manager or the system itself decides the strategy of this inspection according to the current management target, such as whether to fill in the inspection blind spot (use frequency prompt, the lower the historical detection frequency, the higher the current detection priority) or focus on preventing risk points (use abnormal prompt), or a combination of the two. This decision is embodied as "generation prompt information"; then, "historical detection data" and "generation prompt information" representing the analysis target are input into the index generation model, and the model analyzes the data under the guidance of the "prompt information": if receiving frequency prompt information, the model will calculate the historical detection frequency of each sub-region and determine the detection priorities of multiple sub-regions according to the historical detection frequencies of multiple sub-regions; if receiving abnormal prompt information, the model will analyze the abnormal history of each sub-region, identify the regions with a high incidence of abnormalities in history or recent problems as high-risk points, and set them as high detection priorities for intensive prevention.

[0055] For example, the above scheme is illustrated by taking the detection of the dangerous chemical storage state of the "dangerous chemical storage area of a chemical plant" as an example.

[0056] Dangerous chemical storage area: dangerous chemical storage warehouse;

[0057] Sub-region: 100 dangerous chemical storage cabinets (C-001 to C-100);

[0058] Historical detection data: Daily inspection records of the past 3 months, including: which cabinet was inspected, when, and the inspection result (normal / abnormal, and the type of abnormality).

[0059] Scenario One: The factory has experienced a long holiday, and during the holiday, the inspection was carried out on a sampling basis. The manager hopes that after the comprehensive resumption, the storage cabinets that were neglected during the holiday can be checked first.

[0060] Frequency prompt information is used, and the frequency prompt information is: "Please prioritize the detection of storage cabinets with the lowest recent inspection frequency."

[0061] The model receives historical detection data and frequency prompt information, calculates the number of times each storage cabinet has been detected in the last 14 days, and detects that cabinets C-055, C-089, C-016, etc. have only been checked 0-1 times during the holiday, while other cabinets have been checked 3-5 times. The model outputs the regional detection index:

[0062] C-055, C-089, and C-016 are high priority, and other storage cabinets are low priority.

[0063] Therefore, the dangerous chemical storage state detection will be prioritized for cabinets C-055, C-089, C-016, etc.

[0064] Scenario Two: The factory wants to allocate limited inspection resources to the key area with an unusually high number of abnormalities.

[0065] Abnormal prompt information is used. The abnormal prompt information is: "Please prioritize the detection of storage cabinets with the highest total number of abnormalities since the system was launched."

[0066] The model receives historical detection data and abnormal prompt information, and the model directly counts the total number of all abnormal events recorded in the history of each storage cabinet without distinguishing between abnormal types. It is found that cabinet C-012 has a cumulative abnormality of 15 times, C-033 has a cumulative abnormality of 8 times, which is much higher than other cabinets (average 2-3 times). The output of the regional detection index is:

[0067] C-012 (priority: highest)

[0068] C-078 (priority: second highest)

[0069] Other storage cabinets (priority: low).

[0070] According to the regional detection index, dangerous chemical storage state detection will be prioritized for C-012, so as to accurately allocate resources to the link that can most improve the overall safety level.

[0071] The embodiments of the present specification determine the "generated prompt information" by analyzing historical data. This scheme makes the inspection strategy no longer fixed. The manager can dynamically adjust the logic of model generation priority indicators by configuring different prompt information (such as frequency prompt or abnormal prompt) according to the needs of different periods. This makes a set of systems adapt to diversified business scenarios and management needs, overcoming the limitations of traditional single and rigid inspection strategies. At the same time, the generated prompt information and historical detection data are jointly input into the indicator generation model. Under the guidance of the "generated prompt information", the model can accurately locate high-risk points, avoiding the missed judgment of key areas due to insufficient experience or negligence in manual inspection, and significantly improving the accuracy of risk identification.

[0072] Step 104: Based on the regional detection indicators, the storage state of the dangerous goods in the storage area is detected, and the detection result of the dangerous goods storage state in the storage area is obtained.

[0073] After determining the detection priority of each sub-region, the system executes specific abnormal detection on each sub-region according to the order. This detection process can be realized in two ways according to the characteristics of the data to be detected and the complexity of the business needs: one is rule matching based on explicit threshold, suitable for scenarios with clear logic and quantifiable judgment criteria; the other is intelligent analysis and identification based on fine-tuned large models, suitable for advanced scenarios that need to handle complex patterns (such as images, text) or discover unknown abnormalities. In actual systems, the two can be used in combination to complement each other's strengths.

[0074] For example, a typical rule base may contain the following clauses:

[0075] Rule ID:001|Name:Temperature Exceeds|Condition:IF Temperature>[Item Maximum Tolerance Temperature] THEN Abnormal;

[0076] Rule ID:002|Name:Humidity Too High|Condition:IF Humidity>[Item Maximum Tolerance Humidity] THEN Abnormal;

[0077] Target sub-region: Cabinet-12 (storage cabinet-12, storing acetone, the specified storage temperature needs to be lower than 30℃).

[0078] The obtained regional data of the target sub-region: {"temperature": 35, "humidity": 50, "volatile gas concentration": 80, "cabinet door status": "closed", "real-time weight": "15kg"}

[0079] Rule matching process:

[0080] The system matches the data temperature: 35 with rule 001 (temperature > 30): the condition is met, triggering the abnormality: "temperature exceeds".

[0081] The system determines the dangerous chemical storage state detection result of the sub-region: ["temperature exceeds the standard"].

[0082] In an optional implementation of the embodiment, the detection priority includes a data priority.

[0083] Based on the region detection index, the dangerous chemical storage state of the plurality of sub-regions is detected respectively to obtain the dangerous chemical storage state detection result of the dangerous chemical storage region, including:

[0084] The region data of the plurality of sub-regions is obtained.

[0085] Based on the region detection index, the data priority of the region data is determined.

[0086] According to the data priority, the region data is detected in the dangerous chemical storage state to obtain the dangerous chemical storage state detection result of the dangerous chemical storage region.

[0087] In the scheme, the system first obtains the region data of all sub-regions, but because there are too many data to be processed, it is necessary to determine which region data to analyze first. At this time, the region detection index is used as the basis, and the data of the sub-region with high priority is analyzed first.

[0088] The region data refers to a multi-dimensional data set collected from a sub-region and reflecting the state of the sub-region. These are the direct analysis objects of the anomaly detection.

[0089] For example, for a dangerous chemical storage cabinet, the "region data" may include: dangerous chemical images obtained by shooting the dangerous chemicals in the storage cabinet, environmental state data (temperature data, humidity data) collected by sensors, and spectrum images scanned by a spectrometer.

[0090] The data priority refers to the order of analyzing the region data of each sub-region. It is directly determined by the "region detection index" (i.e., the detection priority) of the sub-region. The data of the sub-region with high priority will be analyzed first.

[0091] In the embodiments of the present specification, the "data priority" is introduced to ensure that the system can perform serial calculation according to the risk level after parallel data acquisition, so that the anomaly of the high-risk region can be identified and alarmed as soon as possible, effectively reducing the risk of delay of critical alarms due to competition or overload of computing resources.

[0092] In an optional implementation of the embodiment, according to the data priority, the region data is detected in the dangerous chemical storage state to obtain the dangerous chemical storage state detection result of the dangerous chemical storage region, including:

[0093] According to the data priority, target region data is determined from the region data, wherein the data priority of the target region data is greater than the data priority of the candidate region data, and the candidate region data is region data in the region data that is not subjected to the dangerous chemical storage state detection in addition to the target region data;

[0094] The abnormality detection model is called to perform the dangerous chemical storage state detection based on the target region data, and a dangerous chemical storage state detection result of a target sub-region is obtained, wherein the target sub-region is a sub-region corresponding to the target region data;

[0095] The step of determining the dangerous chemical storage region data from the region data according to the data priority is returned to be executed until the region data is iterated through, and a dangerous chemical storage state detection result of the dangerous chemical storage region is obtained.

[0096] Example:

[0097] Initial state:

[0098] Region data: The data packets of all cabinets such as Cabinet-12, Cabinet-33, Cabinet-47,... have been acquired.

[0099] Data priority: Cabinet-12 > Cabinet-33 > Cabinet-47 >...

[0100] Loop process:

[0101] First round of loop:

[0102] Determination of target: From all candidate data, the data packet of Cabinet-12 is determined as the target region data.

[0103] Detection: The abnormality detection model is called to analyze the data of Cabinet-12, and it is found that the gas concentration is abnormal, and an alarm is immediately given.

[0104] Update: The data of Cabinet-12 is removed from the candidate list.

[0105] Second round of loop:

[0106] Determination of new target: From the remaining candidate data (Cabinet-33, Cabinet-47,...), the data packet of Cabinet-33 is determined as the new target region data.

[0107] Detection: The model is called to analyze the data of Cabinet-33, and it is judged that the humidity is normal.

[0108] Update: Cabinet-33 is removed from the candidate list.

[0109] Subsequent cycle: Repeat the process until the data of Cabinet-47 and all other cabinets are processed.

[0110] In the embodiments of the present specification, by always selecting the "target region data" with the highest current data priority for analysis in each detection cycle, the scheme ensures that limited computing resources are preferentially allocated to the highest-risk sub-region. In this way, the system can discover the most urgent abnormality (such as a dangerous chemical cabinet gas leak) with the shortest delay, thereby gaining valuable time for risk disposal and greatly improving the real-time response capability of the system.

[0111] In addition to the above-mentioned one-by-one serial processing mode, the embodiments of the present specification also provide an abnormality detection implementation mode based on parallel processing and dynamic priority scheduling. This method can significantly improve system throughput and resource utilization while ensuring that high-priority tasks are processed in a timely manner.

[0112] In another optional implementation of the present embodiment, according to the data priority, the region data is subjected to dangerous chemical storage state detection to obtain the dangerous chemical storage state detection result of the dangerous chemical storage region, including the following steps:

[0113] Construct a priority task queue: place all region data according to its corresponding data priority in a priority blocking queue. The scheduling strategy of this queue is: always prioritize the region data with the highest data priority in the current queue.

[0114] Parallel processing and resource allocation: initialize a fixed-size thread pool (or worker pool) containing multiple concurrent abnormality detection workers. Each worker runs independently and continuously competes for region data to be processed from the above-mentioned priority blocking queue.

[0115] Perform abnormality detection and result collection: each worker immediately calls the abnormality detection model to perform dangerous chemical storage state detection on the region data (i.e. target region data) after successfully obtaining it from the queue.

[0116] After detection is completed, the dangerous chemical storage state detection result of the target sub-region is generated and stored in a unified result set.

[0117] The worker then returns to the ready state and continues to request the next task from the priority queue.

[0118] Completion determination and result return: a master process monitors the task state. When the following two conditions are met:

[0119] All region data has been dequeued and processed (i.e. the queue is empty);

[0120] All workers are in idle state, and all detection tasks are completed:

[0121] The master process determines that the detection is completed, and then integrates all sub-region results in the result set to generate and output the detection result of the dangerous chemical storage state of the dangerous chemical storage area.

[0122] In an optional embodiment of the present embodiment, the dangerous chemical storage state detection includes dangerous chemical anomaly detection, and the target region data includes target dangerous chemical data of the dangerous chemical stored on the target sub-region.

[0123] The anomaly detection model is called to perform dangerous chemical storage state detection based on the target region data to obtain a dangerous chemical storage state detection result of the target sub-region, including:

[0124] The target dangerous chemical data is input into the anomaly detection model to perform dangerous chemical anomaly detection to obtain the dangerous chemical storage state detection result of the target sub-region, wherein the dangerous chemical storage state detection result is used to reflect the abnormal situation of the stored dangerous chemical.

[0125] The dangerous chemical anomaly detection is used to judge whether the dangerous chemical stored in the sub-region itself is abnormal (such as leakage, corrosion, damage, deterioration, quantity anomaly).

[0126] Target dangerous chemical data: In the "target region data" (i.e. the complete data of a certain sub-region currently being processed), the part of data specially used to describe the state of the dangerous chemical.

[0127] Examples: real-time image of the dangerous chemical, weight of the dangerous chemical, identification information of the dangerous chemical, etc.

[0128] In the present scheme, the system selects the target region data (for example, the data packet of Cabinet-12) to be processed according to the data priority. From the comprehensive data packet of Cabinet-12, the "target dangerous chemical data" (such as the appearance picture of the dangerous chemical taken by the camera inside the cabinet, the reading of the weighing sensor) directly related to the dangerous chemical is specially extracted. The "target dangerous chemical data" (such as the picture of the dangerous chemical) is input into the special "anomaly detection model". The model analyzes and outputs the result, for example: "the dangerous chemical packaging exists serious corrosion" or "the quantity of the dangerous chemical does not match the record". This result directly reflects the abnormal situation of the stored dangerous chemical.

[0129] For example, the target dangerous chemical data includes spectral data obtained after spectral scanning of the target dangerous chemical, and label information of the target dangerous chemical obtained by performing text recognition on an image of the target dangerous chemical obtained after photographing the target dangerous chemical; the spectral data is input into a pre-trained spectral recognition model, and the type information of the target dangerous chemical can be determined; by using the abnormality detection model, the type information of the target dangerous chemical is compared with the label information, and whether the label of the target dangerous chemical is correct can be determined.

[0130] For example, the target dangerous chemical data can also include an image of the target dangerous chemical, and the image of the target dangerous chemical is preprocessed (such as enhancing contrast, etc.), and the preprocessed image of the target dangerous chemical is input into the fine-tuned large model (abnormality detection model), and the large model can analyze whether the target dangerous chemical is abnormal in form according to the image of the target dangerous chemical, for example, whether the target dangerous chemical leaks, whether the target dangerous chemical has precipitates and crystals, whether the color of the target dangerous chemical is normal, whether the liquid level of the target dangerous chemical is normal, etc.

[0131] In the embodiments of the present specification, the target dangerous chemical data is analyzed by the model, and the defects of the dangerous chemical itself such as leakage, corrosion, damage, deterioration, and label error can be accurately identified, and the missed judgment caused by the limitation of naked eye observation or insufficient experience can be effectively avoided.

[0132] In an optional implementation of the present embodiment, the dangerous chemical storage state detection includes storage abnormality detection, and the target region data includes reference dangerous chemical data of the target sub-region and target dangerous chemical data of the dangerous chemical stored on the target sub-region.

[0133] The abnormality detection model is called, and the dangerous chemical storage state detection is performed based on the target region data to obtain a dangerous chemical storage state detection result of the target sub-region, including:

[0134] The reference dangerous chemical data and the target dangerous chemical data are input into the abnormality detection model to perform storage abnormality detection, and a dangerous chemical storage state detection result of the target sub-region is obtained, wherein the dangerous chemical storage state detection result is used to reflect the storage abnormality of the target sub-region.

[0135] The storage abnormality detection is a detection type specially used to judge whether the storage condition and storage position of the dangerous chemical are correct and safe.

[0136] The reference dangerous chemical data refers to standard data or expected data of the correct dangerous chemical that should be stored in the sub-region. It is a "benchmark" or "reference" for judging whether the current storage state is abnormal.

[0137] For example, the cabinet is preconfigured with a list of allowed chemicals, an ideal storage temperature / humidity range for each chemical, etc.

[0138] Specifically, the system selects target area data (for example, the data packet of Cabinet-33); at the same time, reference dangerous chemical data is obtained: Cabinet-33 is preset to store "metallic sodium", and the storage requirement is that "humidity must be lower than 5%". Target dangerous chemical data: the current real-time data in Cabinet-33, such as the current humidity of 65%, and the package identification of the goods recognized by the camera; the "reference dangerous chemical data" and the "target dangerous chemical data" are input into the "abnormal detection model" together; the model outputs the result after comparison and logical judgment, for example: "the current environmental humidity is far beyond the safety standard" or "there are unregistered chemicals in the cabinet". This result directly reflects the storage abnormality of the target sub-area.

[0139] In the embodiments of the present specification, by automatically comparing the real-time recognized dangerous chemical information (target dangerous chemical data) with the system preset storage specification (reference dangerous chemical data), it can accurately find the abnormality of mixed storage of forbidden materials, storage of dangerous chemicals in non-specified positions, abnormal storage environment, etc.

[0140] In an optional embodiment of the present embodiment, the detection priority includes area priority;

[0141] Based on the area detection index, the storage state of the dangerous chemicals in the multiple sub-areas is detected respectively to obtain the detection result of the storage state of the dangerous chemicals in the storage area, including:

[0142] Based on the area detection index, the area priority of the multiple sub-areas is determined;

[0143] According to the area priority, the area data of the multiple sub-areas is obtained in sequence, and the storage state of the dangerous chemicals is detected based on the area data to obtain the detection result of the storage state of the dangerous chemicals in the storage area.

[0144] The area priority refers to the order of the sequence of the multiple sub-areas in the order of access and detection. It is directly transformed from the "area detection index", and the higher the risk of the sub-area, the higher the area priority.

[0145] In the present scheme, the system does not obtain all the data at one time, but strictly follows the order of the area priority list to sequentially execute the process of "obtaining area data->detecting the storage state of dangerous chemicals".

[0146] For example, the system calculates the area priority sequence as: Cabinet-12 (acetone)> Cabinet-33 (metallic sodium)> Cabinet-47 (concentrated nitric acid)>.

[0147] The inspection device first moves to Cabinet-12, reads the sensor data (area data) and analyzes on site, finds that the gas concentration is abnormal, and immediately reports.

[0148] Then, the inspection device moves to Cabinet-33, and performs the same data acquisition and analysis.

[0149] This cycle continues until all cabinets in the list are sequentially checked.

[0150] It should be noted that the detection of the storage state of the hazardous chemicals in the region in the present scheme also includes the abnormality detection of the articles and the storage abnormality detection, and the specific manner can refer to the above-embodied examples.

[0151] In the embodiments of the present specification, by arranging the sub-regions with the highest priority at the front of the detection sequence, it is ensured that the detection resources are first used in the most critical region, and the major risks such as leakage and temperature rise that are developing can be captured in the first time. Once an abnormality is identified in the region with the highest priority, the system can immediately trigger the highest level of alarm and start the emergency plan, thereby gaining valuable time for personnel evacuation, emergency disposal and other measures, and effectively avoiding the further expansion and deterioration of the risks.

[0152] In an optional embodiment of the present embodiment, the region data of the plurality of sub-regions is sequentially acquired according to the region priority, including:

[0153] The region priority is sent to the data acquisition device.

[0154] The region data of the plurality of sub-regions sequentially fed back by the data acquisition device is received, wherein the region data is collected by the data acquisition device based on the region priority.

[0155] The data acquisition device is used to collect the region data of the sub-region. According to the deployment mode, the data acquisition device can be divided into two types: fixed type and mobile type.

[0156] Fixed device: refers to a sensing unit fixedly installed in each sub-region, such as a sensor network, a monitoring camera, a spectrometer, etc.

[0157] Mobile device: refers to a collection platform that can move in the hazardous chemical storage region, such as a patrol robot or an autonomous mobile trolley, which also carries data acquisition modules such as sensors, cameras or spectrometers.

[0158] In this solution, the system (decision layer) sends the calculated region priority list to the data collection device. Based on the received list, the data collection device sequentially visits or activates the specified sub-regions and collects their region data. If it is a mobile robot or a movable inspection trolley: it will plan the optimal movement path according to the list order and visit each cabinet in sequence for data collection. If it is a fixed sensor network: the system will activate the sensors of different cabinets in sequence to take readings, avoiding data congestion caused by all sensors working at the same time. The data collection device will return the data packet to the central system after completing the data collection of a sub-region. The central system receives a data packet and immediately performs a hazardous chemical storage state detection, and waits for the arrival of the next data packet.

[0159] For example:

[0160] The central system sends instructions to the inspection device: region priority sequence: [Cabinet-12, Cabinet-33, Cabinet-47,...].

[0161] The inspection device moves to Cabinet-12, scans its label, takes internal images, reads gas concentration, and then packages the region data of Cabinet-12 and sends it back to the central system. The central system calls the model to analyze the data of Cabinet-12. At the same time, the inspection device has started to move to Cabinet-33 according to the instructions. The robot arrives at Cabinet-33, collects and returns the data, and the system continues to analyze. This cycle forms an efficient "collection - transmission - analysis" pipeline.

[0162] In the embodiments of the present specification, the complex risk assessment logic (completed in the central system) is separated from the simple data collection action (executed by the collection device). This architecture allows the system to easily access different types of data collection devices (such as different brands of robots or sensors), as long as they can understand the priority instructions, greatly enhancing the compatibility and expansion ability of the system. At the same time, through the serial flow processing of "collecting one, transmitting one, and processing one", instead of the processing mode of "massive data at a time", the data flood at a specific time (such as when all sensors report at the same time) is effectively avoided, making the system run more smoothly and requiring less network and computing resources.

[0163] In addition to the above serial solution where the data collection device "sequentially" performs collection based on priority, the embodiments of the present specification also provide a hybrid data acquisition solution of "central scheduling, parallel control". This solution aims to optimize the overall data collection efficiency, especially in scenarios where the collection device itself does not have complex path planning capabilities, or part of the data comes from fixed sensors.

[0164] In an optional implementation of the embodiment, the system acquires the region data of the plurality of sub-regions according to the region priorities in sequence, including the following steps:

[0165] The system presets the region priority list as the current acquisition task list, and sends a first moving and acquisition instruction to the data acquisition device, the instruction pointing to the sub-region with the highest priority in the task list.

[0166] Instead of passively waiting for the data acquisition device to report "task completed", the system actively monitors the state of the data acquisition device and the received data.

[0167] The system defines a state triggering event, for example, receiving region data from the target sub-region or the sensor feedback of the data acquisition device reaching the target position. The triggering of the event is a symbol of starting the next acquisition task.

[0168] When the state triggering event occurs, the system automatically removes the completed item from the acquisition task list and determines whether the list is empty.

[0169] If the list is not empty, the system immediately sends a next moving and acquisition instruction to the data acquisition device, the instruction pointing to the new highest priority sub-region.

[0170] This process is repeated in a loop to form a dynamic instruction stream driven by events until the acquisition task list is empty.

[0171] In an optional implementation of the embodiment, based on the region detection index, the storage state of the hazardous chemicals in the plurality of sub-regions is detected respectively, and after obtaining the hazardous chemical storage state detection result of the hazardous chemical storage region, the method further includes:

[0172] In the case that the hazardous chemical storage state detection result represents an abnormal hazardous chemical storage state, an abnormal alarm is performed.

[0173] The abnormal alarm refers to the system issuing a warning to relevant personnel through sound, light, electricity, message notification and other means, prompting an abnormal situation.

[0174] In addition, in addition to the abnormal alarm, the state of the abnormal hazardous chemicals can also be recorded. For example, the environmental state information of the abnormal hazardous chemicals, the identification of the abnormal hazardous chemicals, the abnormal type, the abnormal level, the abnormal occurrence time and other information.

[0175] For example, the alarm mechanism sends alarm information to relevant personnel through various channels (such as monitoring center large screen, short message, email, mobile phone APP push, etc.) according to the preset alarm strategy. The alarm information usually includes the identification of the abnormal hazardous chemicals, the abnormal type, the abnormal level, the occurrence time, etc., so as to quickly respond.

[0176] In the embodiments of the present specification, through the alarm, it is helpful to quickly respond and handle, avoid abnormal expansion, and reduce loss.

[0177] It should be noted that the system can automatically perform a complete detection process once according to a preset period (for example, every 4 hours, every day, or every week). In this mode, the system will determine the risk level of each sub-region in the hazardous chemical storage area according to the historical detection data up to the end of the last period at the beginning of each period, and then generate a corresponding detection priority sequence, and complete the storage state detection of this round according to the sequence. This mode is suitable for regular monitoring scenarios where the risk changes relatively slowly or there is a need for energy saving of computing and inspection resources.

[0178] In addition, the system can also be configured as an uninterrupted real-time detection mode. In this mode, whenever a round of detection is completed, the area data and hazardous chemical storage state detection results generated thereby are incrementally updated to the historical database in real time. This update action will immediately trigger the index generation model to start a new round of calculation. Based on the historical data set fused with the latest data, the model analyzes and outputs the latest risk level of each sub-region in real time. The system then determines a new detection priority according to the updated risk level and starts the next round of detection task without interruption. In this way, the detection priority is always determined based on the latest historical detection data, ensuring that limited inspection resources (such as computing power) are always scheduled to the area with the highest current risk, maximizing resource utilization efficiency. Since the detection cycle is uninterrupted and the data analysis is real-time, the system can detect risks that accumulate slowly (such as slow temperature rise and gradual humidity exceeding) early. This enables the system to issue real-time warnings before the potential risk reaches the destructive threshold, achieving a qualitative change from "alarm" to "warning" and securing a valuable time window for human intervention.

[0179] It should be understood that the above two modes can be flexibly selected or combined according to actual safety management needs to jointly ensure the continuous safety of hazardous chemical storage.

[0180] In order to implement the above method, the embodiments of the present specification also provide a hazardous chemical storage state detection system. The hazardous chemical storage state detection system comprises a hazardous chemical storage area and an anomaly detection device, the hazardous chemical storage area comprises a plurality of sub-regions;

[0181] The anomaly detection device is configured to obtain area detection indexes of the hazardous chemical storage area, wherein the area detection indexes are used to reflect the detection priorities of the plurality of sub-regions, and the area detection indexes are obtained by processing historical detection data of the hazardous chemical storage area by an index generation model; based on the area detection indexes, the hazardous chemical storage state detection is performed on the plurality of sub-regions respectively, and a hazardous chemical storage state detection result of the hazardous chemical storage area is obtained.

[0182] The acquisition manner of the region detection index refers to the related description of step 102 in the above embodiment. The specific implementation manner of detecting the dangerous chemical storage state of each sub-region based on the region detection index refers to the related description of step 104 in the above embodiment.

[0183] The following describes an embodiment of the present specification in combination with accompanying drawings. Figure 2 The dangerous chemical storage state detection method provided in the present specification is further described by taking the application of the dangerous chemical storage state detection method in dangerous chemical abnormality detection as an example. Wherein, Figure 2 FIG. 1 shows a flowchart of a processing process of a dangerous chemical storage state detection method provided in an embodiment of the present specification, which specifically includes the following steps.

[0184] Step 202: Collecting historical detection data of a dangerous chemical storage region, wherein the dangerous chemical storage region includes a plurality of sub-regions.

[0185] The historical detection data includes region data collected when the dangerous chemical storage state detection is performed on each sub-region at each time point (the historical detection data of each sub-region is used to reflect the environment state of the sub-region and the state of the stored dangerous chemicals (including image, spectrum data, etc.) at each detection time point) and the dangerous chemical storage state detection result (whether abnormal, abnormal type, abnormal reason, etc.) obtained after the region data is detected.

[0186] Step 204: Analyzing the historical detection data to determine the generation prompt information of the dangerous chemical storage region.

[0187] If the generation prompt information includes frequency prompt information, step 206 is performed; if the generation prompt information includes abnormal prompt information, step 208 is performed.

[0188] Step 206: Inputting the frequency prompt information and the historical detection data into an index generation model to obtain a region detection index of the dangerous chemical storage region, wherein the region detection index is used to reflect the detection priority of the plurality of sub-regions.

[0189] The specific logic of generating the region detection index based on the frequency prompt information and the historical detection data by the index generation model refers to the related description of step 102 in the above embodiment.

[0190] Step 208: Inputting the abnormal prompt information and the historical detection data into an index generation model to obtain a region detection index of the dangerous chemical storage region.

[0191] The specific logic of generating the region detection index based on the abnormal prompt information and the historical detection data by the index generation model refers to the related description of step 102 in the above embodiment.

[0192] If the detection priority comprises the data priority, steps 210-220 are performed; if the detection priority comprises the area priority, steps 222-224 are performed.

[0193] Step 210: Obtain area data of a plurality of sub-areas.

[0194] Step 212: Determine the data priority of the area data based on the area detection index.

[0195] Step 214: Determine target area data from the area data according to the data priority, wherein the data priority of the target area data is greater than the data priority of candidate area data, and the candidate area data is area data other than the target area data in the area data that has not been detected for the dangerous chemical storage state.

[0196] Step 216: Call the abnormality detection model to detect the dangerous chemical storage state based on the target area data to obtain a dangerous chemical storage state detection result of a target sub-area, wherein the target sub-area is a sub-area corresponding to the target area data.

[0197] Step 218: Determine whether the traversal is completed.

[0198] If the traversal is not completed, return to the step of determining the target area data from the area data according to the data priority; if the traversal is completed, perform step 220.

[0199] Step 220: Obtain the dangerous chemical storage state detection result of the dangerous chemical storage area based on the dangerous chemical storage state detection results of the plurality of sub-areas.

[0200] Step 222: Determine the area priority of the plurality of sub-areas based on the area detection index.

[0201] Step 224: Obtain the area data of the plurality of sub-areas in sequence according to the area priority, and detect the dangerous chemical storage state of the area data to obtain the dangerous chemical storage state detection result of the dangerous chemical storage area.

[0202] The specific manner and process of the dangerous chemical storage state detection are described in the above embodiment of step 104.

[0203] Corresponding to the above method embodiment, the present specification also provides a dangerous chemical storage state detection device embodiment, Figure 3 A structural schematic diagram of a dangerous chemical storage state detection device provided by an embodiment of the present specification is shown. As shown in the figure, Figure 3 The device comprises:

[0204] The acquisition module 302 is configured to acquire a region detection index of a hazardous chemical substance storage area, wherein the hazardous chemical substance storage area includes a plurality of sub-regions, the region detection index is used to reflect detection priorities of the plurality of sub-regions, and the region detection index is obtained by processing historical detection data of the hazardous chemical substance storage area by an index generation model;

[0205] The detection module 304 is configured to perform hazardous chemical substance storage state detection on the plurality of sub-regions respectively based on the region detection index, and obtain a hazardous chemical substance storage state detection result of the hazardous chemical substance storage area.

[0206] Optionally, the acquisition module 302 is further configured to:

[0207] acquire historical detection data;

[0208] analyze the historical detection data to determine generation prompt information of the hazardous chemical substance storage area, wherein the generation prompt information includes at least one of frequency prompt information and abnormal prompt information, the frequency prompt information is used to guide the index generation model to generate the region detection index based on detection frequencies of the plurality of sub-regions, and the abnormal prompt information is used to guide the index generation model to generate the region detection index based on abnormal states of the plurality of sub-regions;

[0209] input the generation prompt information and the historical detection data into the index generation model to obtain the region detection index of the hazardous chemical substance storage area.

[0210] Optionally, the detection priority includes a data priority; and the detection module 304 is further configured to:

[0211] acquire region data of the plurality of sub-regions;

[0212] determine a data priority of the region data based on the region detection index;

[0213] perform hazardous chemical substance storage state detection on the region data according to the data priority to obtain the hazardous chemical substance storage state detection result of the hazardous chemical substance storage area.

[0214] Optionally, the detection module 304 is further configured to:

[0215] determine target region data from the region data according to the data priority, wherein a data priority of the target region data is greater than a data priority of candidate region data, and the candidate region data is region data in the region data that is not the target region data and has not been subjected to hazardous chemical substance storage state detection;

[0216] invoke an abnormality detection model to perform hazardous chemical substance storage state detection based on the target region data to obtain a hazardous chemical substance storage state detection result of a target sub-region, wherein the target sub-region is a sub-region corresponding to the target region data;

[0217] Return to execute the step of determining the target area data from the area data according to the data priority, until the area data traversal is completed, and the dangerous chemical storage state detection result of the dangerous chemical storage area is obtained.

[0218] Optionally, the dangerous chemical storage state detection includes dangerous chemical abnormality detection, and the target area data includes target dangerous chemical data of the dangerous chemical stored on the target sub-area; the detection module 304 is further configured to:

[0219] input the target dangerous chemical data into the abnormality detection model for dangerous chemical abnormality detection, and obtain the dangerous chemical storage state detection result of the target sub-area, wherein the dangerous chemical storage state detection result is used to reflect the abnormality of the stored goods.

[0220] Optionally, the dangerous chemical storage state detection includes storage abnormality detection, and the target area data includes reference dangerous chemical data of the target sub-area and target goods data of the dangerous chemical stored on the target sub-area; the detection module 304 is further configured to:

[0221] input the reference dangerous chemical data and the target dangerous chemical data into the abnormality detection model for storage abnormality detection, and obtain the dangerous chemical storage state detection result of the target sub-area, wherein the dangerous chemical storage state detection result is used to reflect the storage abnormality of the target sub-area.

[0222] Optionally, the detection priority includes area priority; the detection module 304 is further configured to:

[0223] determine the area priority of the plurality of sub-areas based on the area detection index;

[0224] obtain the area data of the plurality of sub-areas in sequence according to the area priority, and perform dangerous chemical storage state detection on the area data to obtain the dangerous chemical storage state detection result of the dangerous chemical storage area.

[0225] Optionally, the detection module 304 is further configured to:

[0226] send the area priority to the data collection device;

[0227] receive the area data of the plurality of sub-areas fed back by the data collection device in sequence, wherein the area data is collected by the data collection device based on the area priority in sequence.

[0228] Optionally, the dangerous chemical storage state detection device further includes an alarm module configured to:

[0229] perform abnormality alarm in the case that the dangerous chemical storage state detection result represents the abnormality of the dangerous chemical storage state.

[0230] The above is a schematic scheme of the dangerous chemical storage state detection device of the embodiment. It should be noted that the technical scheme of the dangerous chemical storage state detection device is the same as the technical scheme of the dangerous chemical storage state detection method described above, and the technical scheme of the abnormality detection device is not described in detail. The details can be seen from the description of the technical scheme of the dangerous chemical storage state detection method.

[0231] Figure 4 A structural block diagram of a computing device 400 according to one embodiment of the present specification is shown. The components of the computing device 400 include, but are not limited to, a memory 410 and a processor 420. The processor 420 is connected to the memory 410 through a bus 430, and a database 450 is used to save data.

[0232] The computing device 400 also includes an access device 440, which enables the computing device 400 to communicate via one or more networks 460. Examples of these networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet. The access device 440 can include one or more of any type of network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 wireless local area network (WLAN) wireless interface, a Worldwide Interoperability for Microwave Access (Wi-MAX) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, and a near field communication (NFC).

[0233] In one embodiment of the present specification, the above-mentioned components of the computing device 400 and other components not shown in the Figure 4 may be connected to each other, for example, through a bus. It should be understood that Figure 4 The structural block diagram of the computing device shown is only for the purpose of example, and is not a limitation on the scope of the present specification. Those skilled in the art can add or replace other components as needed.

[0234] The computing device 400 can be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (e.g., a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (e.g., a smartphone), a wearable computing device (e.g., a smart watch, smart glasses, etc.), or other type of mobile device, or a stationary computing device such as a desktop computer or a personal computer (PC). The computing device 400 can also be a mobile or stationary server.

[0235] The processor 420 is configured to execute computer-executable instructions to implement the steps of the hazardous chemical storage state detection method or the hazardous chemical storage cabinet anomaly detection method.

[0236] The above is a schematic solution of the computing device of the embodiment. It should be noted that the technical solution of the computing device belongs to the same concept as the technical solution of the hazardous chemical storage state detection method, and the details of the technical solution of the computing device that are not described in detail can be referred to the description of the technical solution of the hazardous chemical storage state detection method or the hazardous chemical storage cabinet anomaly detection method.

[0237] An embodiment of the present specification also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the hazardous chemical storage state detection method or the hazardous chemical storage cabinet anomaly detection method.

[0238] The above is a schematic solution of the computer-readable storage medium of the embodiment. It should be noted that the technical solution of the storage medium belongs to the same concept as the technical solution of the hazardous chemical storage state detection method or the hazardous chemical storage cabinet anomaly detection method, and the details of the technical solution of the storage medium that are not described in detail can be referred to the description of the technical solution of the hazardous chemical storage state detection method.

[0239] An embodiment of the present specification also provides a computer program, which, when executed in a computer, causes the computer to perform the steps of the hazardous chemical storage state detection method or the hazardous chemical storage cabinet anomaly detection method.

[0240] The above is a schematic solution of the computer program of the embodiment. It should be noted that the technical solution of the computer program belongs to the same concept as the technical solution of the hazardous chemical storage state detection method or the hazardous chemical storage cabinet anomaly detection method, and the details of the technical solution of the computer program that are not described in detail can be referred to the description of the technical solution of the hazardous chemical storage state detection method or the hazardous chemical storage cabinet anomaly detection method.

[0241] The above-described embodiments of the application have several aspects, no single one of which is solely responsible for the application's desirable attributes. Without limiting the scope of the application as expressed by the claims which follow, some further embodiments make these aspects even more useful. Other embodiments can result in less desirable attributes.

[0242] The computer readable medium can include any entity or apparatus capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, software distribution medium, etc. It should be noted that the computer readable medium can include appropriate contents according to the requirements of patent practice, for example, according to the patent practice in some regions, the computer readable medium does not include electrical carrier signals and telecommunication signals.

[0243] It should be noted that for the foregoing method embodiments, the descriptions are expressed as a series of action combinations for the sake of simplicity and brevity, but those skilled in the art should know that the present application is not limited by the order of the actions, because according to the present application, certain steps can be performed in other orders or simultaneously. In addition, those skilled in the art should know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily all the necessary actions and modules of the present application.

[0244] In the above embodiments, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the relevant description of other embodiments.

[0245] The preferred embodiments of the present specification disclosed above are only used to help explain the present specification. The alternative embodiments do not describe all the details and limit the application to the specific embodiments described. Obviously, according to the content of the embodiments of the present specification, many modifications and changes can be made. The present specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the embodiments of the present specification, so that those skilled in the art can well understand and use the present specification. The present specification is limited by the claims and their entire scope and equivalents.

Claims

1. A hazardous chemical substance storage state detection method characterized by comprising: The method comprises: obtaining a region detection index of a hazardous chemical storage region, wherein the hazardous chemical storage region comprises a plurality of sub-regions, the region detection index is used to reflect detection priorities of the plurality of sub-regions, and the region detection index is obtained by processing historical detection data of the hazardous chemical storage region by a index generation model; based on the region detection index, performing hazardous chemical storage state detection on the plurality of sub-regions respectively to obtain a hazardous chemical storage state detection result of the hazardous chemical storage region.

2. The method of claim 1, wherein, The method comprises: obtaining the historical detection data; parsing the historical detection data to determine generation prompt information of the hazardous chemical storage region, wherein the generation prompt information comprises at least one of frequency prompt information and abnormal prompt information, the frequency prompt information is used to guide the index generation model to generate the region detection index based on detection frequencies of the plurality of sub-regions, and the abnormal prompt information is used to guide the index generation model to generate the region detection index based on abnormal states of the plurality of sub-regions; inputting the generation prompt information and the historical detection data into the index generation model to obtain the region detection index of the hazardous chemical storage region.

3. The method of claim 1, wherein, The detection priorities comprise data priorities; The method comprises: obtaining region data of the plurality of sub-regions; determining the data priorities of the region data based on the region detection index; performing hazardous chemical storage state detection on the region data according to the data priorities to obtain the hazardous chemical storage state detection result of the hazardous chemical storage region.

4. The method of claim 3, wherein, The method comprises: determining target region data from the region data according to the data priorities, wherein a data priority of the target region data is greater than a data priority of candidate region data, and the candidate region data is region data in the region data that has not been subjected to hazardous chemical storage state detection; calling an abnormality detection model to perform hazardous chemical storage state detection based on the target region data to obtain a hazardous chemical storage state detection result of a target sub-region, wherein the target sub-region corresponds to the target region data; returning to the step of determining the target region data from the region data according to the data priorities until the region data is traversed to obtain the hazardous chemical storage state detection result of the hazardous chemical storage region.

5. The method of claim 4, wherein, The hazardous chemical storage state detection comprises hazardous chemical abnormality detection, and the target region data comprises target hazardous chemical data stored in the target sub-region; The method comprises: The target hazardous chemical data is input into the anomaly detection model to perform hazardous chemical anomaly detection, and the hazardous chemical storage status detection result of the target sub-region is obtained, wherein the hazardous chemical storage status detection result is used to reflect the abnormal situation of the stored hazardous chemicals.

6. The method of claim 4, wherein, The hazardous chemical storage status detection includes storage anomaly detection, and the target area data includes reference hazardous chemical data of the target sub-region and target hazardous chemical data of hazardous chemicals stored in the target sub-region. The method of calling the anomaly detection model to detect the storage status of hazardous chemicals based on the target area data, and obtaining the detection results of the storage status of hazardous chemicals in the target sub-region, includes: The reference hazardous chemical data and the target hazardous chemical data are input into the anomaly detection model to perform the storage anomaly detection, thereby obtaining the hazardous chemical storage status detection result of the target sub-region, wherein the hazardous chemical storage status detection result is used to reflect the storage anomaly situation of the target sub-region.

7. The method of claim 1, wherein, The detection priority includes regional priority; The step of detecting the hazardous chemical storage status of the multiple sub-regions based on the regional detection indicators to obtain the hazardous chemical storage status detection results of the hazardous chemical storage areas includes: Based on the region detection indicators, the region priority of the plurality of sub-regions is determined; Based on the area priority, the area data of the multiple sub-areas are obtained sequentially, and the area data is used to detect the storage status of hazardous chemicals to obtain the detection result of the storage status of hazardous chemicals in the hazardous chemicals storage area.

8. The method of claim 7, wherein, The step of sequentially acquiring the region data of the plurality of sub-regions according to the region priority includes: The region priority is sent to the data acquisition device; The system receives regional data from the plurality of sub-regions sequentially fed back by the data acquisition device, wherein the regional data is obtained by the data acquisition device sequentially collecting data from the plurality of sub-regions based on the regional priority.

9. The method according to any one of claims 1 to 8, characterized in that, After obtaining the hazardous chemical storage status detection results for the hazardous chemical storage areas by detecting the hazardous chemical storage status of the multiple sub-areas based on the regional detection indicators, the method further includes: If the detection results of the hazardous chemical storage status indicate that the storage status of the hazardous chemical is abnormal, an abnormal alarm will be issued.

10. A hazardous chemical substance storage state detection device characterized by comprising: include: The acquisition module is configured to acquire regional detection indicators of a hazardous chemical storage area, wherein the hazardous chemical storage area includes multiple sub-areas, the regional detection indicators are used to reflect the detection priority of the multiple sub-areas, and the regional detection indicators are obtained by an indicator generation model processing historical detection data of the hazardous chemical storage area; The detection module is configured to perform hazardous chemical storage status detection on the multiple sub-regions based on the regional detection indicators, and obtain the hazardous chemical storage status detection results of the hazardous chemical storage area.

11. A computing device, comprising: include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the hazardous chemical storage status detection method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that, The storage has computer executable instructions, which, when executed by the processor, implement the steps of the dangerous chemical storage state detection method of any one of claims 1 to 9.

13. A computer program product, characterised in that, The computer program / instructions are executed by the processor to implement the steps of the dangerous chemical storage state detection method of any one of claims 1 to 9.

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