Equipment risk evaluation method and device, electronic equipment and readable storage medium

Abstract

Embodiments of the invention provide an equipment risk evaluation method and apparatus, an electronic device and a readable storage medium. The method comprises the steps of obtaining target equipment data collected for a preset equipment group; for any target risk dimension in the plurality of risk dimensions, obtaining a first score obtained by evaluating the preset device group based on the actual operation condition corresponding to each device in the preset device group; based on the initial evaluation rule and the equipment data, evaluating the preset equipment group for the target risk dimension to obtain a second score; and based on the first score and the second score, determining a target score corresponding to the preset device group in the target risk dimension. Thus, the first score and the second score are combined, the first score and the second score are mutually supplemented and revised, a more comprehensive, objective and accurate gas equipment scoring system can be formed, the gas equipment is evaluated from multiple dimensions, comprehensiveness and accuracy of scoring are ensured, and practicability and effectiveness of target scoring are improved.

Description

Technical Field

[0001] The present invention relates to the field of gas safety technology, and in particular to an equipment risk assessment method, device, electronic equipment and readable storage medium. Background Art

[0002] As the requirements for safety and reliability in the gas field are increasing, FMEA (Failure Mode and Effects Analysis) has been widely used in this field as an important risk management tool. FMEA conducts forward-looking analysis of possible failure modes in the gas system, evaluates their possible consequences, and formulates corresponding preventive measures to reduce potential risks and losses. However, in actual business, the complexity and variability of the gas system poses a challenge to the evaluation method of FMEA.

[0003] Traditional FMEA evaluation methods are often based on qualitative analysis, that is, evaluating failure modes and their impacts based on experience and expert opinions. However, this rigid evaluation method may not fully meet the needs when facing the complex and changing actual situation in the gas field. Therefore, a more practical and effective risk assessment method for gas equipment is urgently needed. Summary of the invention

[0004] In order to overcome the problems existing in the related art, the present invention provides an equipment risk assessment method, device, electronic device and readable storage medium.

[0005] In a first aspect, the present invention provides a method for equipment risk assessment, the method comprising:

[0006] Acquire target device data collected for a preset device group; the target device data includes device data corresponding to multiple risk dimensions;

[0007] For any target risk dimension among the multiple risk dimensions, obtaining a first score obtained by evaluating the preset device group based on the actual operation conditions corresponding to each device in the preset device group;

[0008] Based on the initial evaluation rule and the device data, the preset device group is evaluated according to the target risk dimension to obtain a second score;

[0009] Based on the first score and the second score, a target score corresponding to the preset device group in the target risk dimension is determined; the target score is used to characterize the risk level of the preset device group in the target risk dimension.

[0010] Optionally, determining a target score corresponding to the preset device group in the target risk dimension based on the first score and the second score includes:

[0011] A target score corresponding to the preset device group in the target risk dimension is calculated based on the first score, the score weight corresponding to the first score, the second score, and the score weight corresponding to the second score.

[0012] Optionally, determining a target score corresponding to the preset device group in the target risk dimension based on the first score and the second score includes:

[0013] When a preset scoring condition is met, a target score corresponding to the preset device group in the target risk dimension is determined based on the first score and the second score.

[0014] Optionally, the method further comprises:

[0015] Data cleaning is performed on the multiple first scores to obtain first target data.

[0016] Optionally, the method further comprises:

[0017] When the difference between the target score and the second score is greater than or equal to a preset threshold, the initial evaluation rule is updated.

[0018] In a second aspect, the present invention provides an equipment risk assessment device, the device comprising:

[0019] A first acquisition module is used to acquire target device data collected for a preset device group; the target device data includes device data corresponding to multiple risk dimensions;

[0020] A second acquisition module is used to obtain, for any target risk dimension among the multiple risk dimensions, a first score obtained by evaluating the preset device group based on the actual operation conditions corresponding to each device in the preset device group;

[0021] A first evaluation module, configured to evaluate the preset device group according to the target risk dimension based on the initial evaluation rules and the device data, to obtain a second score;

[0022] A first determination module is configured to determine a target score corresponding to the preset device group in the target risk dimension based on the first score and the second score; the target score is configured to characterize a risk level of the preset device group in the target risk dimension.

[0023] Optionally, the first determining module includes:

[0024] The first determination submodule is used to calculate the target score corresponding to the preset device group in the target risk dimension based on the first score, the score weight corresponding to the first score, the second score, and the score weight corresponding to the second score.

[0025] Optionally, the first determining module includes:

[0026] The second determination submodule is used to determine a target score corresponding to the preset device group in the target risk dimension based on the first score and the second score when a preset score condition is met.

[0027] Optionally, the device further comprises:

[0028] The third acquisition module is used to perform data cleaning on the multiple first scores to obtain first target data.

[0029] Optionally, the device further comprises:

[0030] The first updating module is used to update the initial evaluation rule when the difference between the target score and the second score is greater than or equal to a preset threshold.

[0031] In a third aspect, the present invention provides an electronic device comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the device risk assessment method described in any one of the first aspects is implemented.

[0032] In a fourth aspect, the present invention provides a readable storage medium, which, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to execute the steps in the device risk assessment method in any one of the embodiments of the first aspect above.

[0033] In an embodiment of the present invention, by acquiring target device data collected for a preset device group; the target device data includes device data corresponding to multiple risk dimensions; for any target risk dimension in the multiple risk dimensions, obtaining a first score obtained by evaluating the preset device group based on the actual operating conditions corresponding to each device in the preset device group; based on the initial evaluation rules and the device data, evaluating the preset device group for the target risk dimension to obtain a second score; based on the first score and the second score, determining the target score corresponding to the preset device group in the target risk dimension; the target score is used to characterize the risk level of the preset device group in the target risk dimension. In this way, the second score obtained based on the initial evaluation rules and the device data can provide a unified standard for device evaluation, ensuring the objectivity and consistency of the score. The first score obtained by evaluating based on the actual operating conditions of the device can directly reflect the operating status and performance of the device, improving the intuitiveness of the score. Furthermore, based on the first score and the second score, the target score corresponding to the preset equipment group in the target risk dimension is determined. By combining the first score and the second score, and supplementing and revising each other, a more comprehensive, objective and accurate gas equipment scoring system can be formed, avoiding the impact of the solidified initial scoring rules on complex businesses. The risk assessment based on the first score obtained in combination with the actual operating conditions on the basis of the second score has more flexible application value that is more combined with actual scenarios, and then the gas equipment is evaluated from multiple dimensions to ensure the comprehensiveness and accuracy of the score, thereby improving the practicality and effectiveness of the target score. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 is a flowchart of the steps of a device risk assessment method provided by an embodiment of the present invention;

[0036] Figure 2 is a structural diagram of an equipment risk assessment device provided by an embodiment of the present invention;

[0037] Figure 3 It is a structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 are within the scope of protection of the present invention.

[0039] Figure 1 is a flow chart of the steps of a device risk assessment method provided by an embodiment of the present invention, such as Figure 1 As shown, the method may include:

[0040] Step 101: Acquire target device data collected for a preset device group; the target device data includes device data corresponding to multiple risk dimensions.

[0041] In an embodiment of the present invention, preset equipment groups are divided according to actual business needs, and the preset equipment groups include multiple gas equipment, for example, gas supply and regulation equipment (such as stations, pressure regulating stations, storage and distribution stations, etc.), gas transmission and distribution equipment (such as pipelines, pressure regulators, etc.), gas using equipment (such as gas stoves, gas water heaters, etc.) and other gas equipment.

[0042] A data collection device is set for each device included in the preset device group, and the target device data collected by the data collection device is obtained. Among them, the target device data may include device data corresponding to multiple risk dimensions, such as basic device parameter data, device appearance feature data, device operation IoT data, and device maintenance data. The device data corresponding to multiple risk dimensions can be divided based on the nature and source of the data and its different application requirements in device management. Correspondingly, the risk dimensions corresponding to the device data can be divided based on the device data corresponding to the risk dimension as the risk factor causing the risk. For example, the basic device parameter data corresponds to the risk dimension of the risk caused by abnormalities in the basic parameters of the device, the device appearance feature data corresponds to the risk dimension of the risk caused by abnormalities in the appearance of the device, the device operation IoT data corresponds to the risk dimension of the risk caused by abnormalities in the device operation process, and the device maintenance data corresponds to the risk dimension of the risk caused by abnormalities in the equipment maintenance process.

[0043] Exemplarily, the basic parameter data of the equipment may include the manufacturer data, technical parameters (such as specifications, models, performance indicators, etc.), basic physical data (such as size, weight, etc.), and organizational structure data (such as cost center, planning personnel group, main work center, etc.). The appearance feature data of the equipment may include scratches, dents, discoloration and other defects on the surface of the equipment, as well as the overall appearance and integrity of the equipment. These data are usually obtained through visual inspection or image recognition technology. The equipment operation IoT data may include the equipment's on / off status, alarm data, operating pressure, speed, temperature, gas concentration and other real-time data, as well as the time data corresponding to these equipment status data. These data are usually collected and transmitted in real time through IoT technologies (such as sensors, RFID, data acquisition equipment, etc.). Equipment maintenance data may include regular inspection records, daily maintenance records, fault diagnosis reports, spare parts replacement records, etc. of the equipment. Equipment maintenance data may mainly include structured data (such as equipment operation time, number of repairs, etc.) and unstructured data (such as fault descriptions, pictures and videos, etc.).

[0044] Step 102: For any target risk dimension among the multiple risk dimensions, obtain a first score obtained by evaluating the preset device group based on the actual operation conditions corresponding to each device in the preset device group.

[0045] In an embodiment of the present invention, multiple target objects evaluate the preset equipment group for any target risk dimension among multiple risk dimensions based on the actual operating conditions corresponding to each device in the preset equipment group to obtain a first score. Obtain the first score. Among them, the target object can be a safety manager, and the first score can be an on-site risk score corresponding to the preset equipment group in the target risk dimension, which is used to characterize the on-site risk degree corresponding to the preset equipment group in the target risk dimension. The first score can be obtained by the safety manager through manual scoring based on the actual operating conditions and status of the equipment. Exemplarily, as an equipment on-site personnel, the safety manager can score the target risk dimension for each device in the preset equipment group based on experience and professional knowledge, combined with the actual operating conditions, and collect the scoring results corresponding to each device, and take the average value as the scoring result of the safety manager for the preset equipment group in the target risk dimension as the first score.

[0046] Step 103: Based on the initial evaluation rules and the device data, the preset device group is evaluated for the target risk dimension to obtain a second score.

[0047] In an embodiment of the present invention, an initial evaluation rule is established according to a business scenario and actual business situation. The initial evaluation rule may include evaluation rules established for severity, occurrence, and detection, respectively. Based on the initial scoring rule and the device data, the preset device group is evaluated for the target risk dimension to obtain a second score. Among them, the second score may be a theoretical risk score corresponding to the preset device group in the target risk dimension, which is used to characterize the theoretical risk degree corresponding to the preset device group in the target risk dimension. The second score may be obtained by rule conversion based on the initial scoring rule and the device data. Based on the initial scoring rule and the device data, a severity score (S), an occurrence score (O), and a detection score (D) are obtained. The second score is calculated based on the severity score (S), the occurrence score (O), and the detection score (D), and the specific calculation formula may be: second score = severity score (S) × occurrence score (O) × detection score (D). The second score may reflect the risk degree of the fault mode, wherein the score range of each factor is usually 1 to 10, and the higher the score, the greater the risk, and the higher the priority of the corresponding risk treatment.

[0048] Exemplarily, the evaluation rules established for severity, occurrence, and detection may include:

[0049] ① Severity assessment rules:

[0050] - Used to assess the impact of the risk situation corresponding to the target risk dimension on the system, equipment or process if it occurs.

[0051] - Can be rated using a scale of 1 to 10, where 1 means very little impact on the system and 10 means very high impact on the system.

[0052] -Evaluate the failure mode based on its impact on safety, environment, customer satisfaction, regulatory compliance, etc.

[0053] - Faults rated 1 to 3 are low level and have little impact on the system; faults rated 4 to 6 are medium level; faults rated 7 to 10 are high level and have a greater impact on the system.

[0054] ② Occurrence evaluation rules:

[0055] -Used to assess the likelihood or frequency of occurrence of risk situations corresponding to the target risk dimension.

[0056] - Rating is done using a scale of 1 to 10, where 1 means the probability of a failure occurring is very low and 10 means the probability of a failure occurring is very high.

[0057] -Evaluation is conducted based on historical data of failure modes, opinions of evaluators, technical indicators, etc.

[0058] - Failure levels rated 1 to 3 have a low probability of occurrence; failure levels rated 4 to 6 have a medium probability of occurrence; and failure levels rated 7 to 10 have a high probability of occurrence.

[0059] ③Detection evaluation rules:

[0060] -Assess the ability of risk situations corresponding to the target risk dimension to be detected after they occur.

[0061] - Rating is done using a scale of 1 to 10, where 1 means the fault is easily detected and 10 means it is very difficult to detect the fault.

[0062] -Evaluation is based on factors such as the effectiveness, reliability and timeliness of the testing method.

[0063] Step 104: Determine a target score corresponding to the preset device group in the target risk dimension based on the first score and the second score; the target score is used to characterize the risk level of the preset device group in the target risk dimension.

[0064] In an embodiment of the present invention, a target score corresponding to a preset device group in a target risk dimension is determined based on the first score and the second score. The target score is used to characterize the actual risk level of the preset device group in the target risk dimension. Exemplarily, the first score and the second score are calculated according to a pre-designed scoring rule to obtain a target score.

[0065] In a possible implementation, the first score can be obtained by evaluating the preset equipment group based on the actual operation conditions corresponding to each device in the preset equipment group, and the obtained severity score, occurrence score and detection score are calculated. That is to say, the safety manager can score the preset equipment group for severity, occurrence and detection according to the actual operation conditions corresponding to each device in the preset equipment group, and obtain the severity score, occurrence score and detection score. Then, based on the severity score, occurrence score and detection score, the first score is determined. The second score can be calculated based on the severity score, occurrence score and detection score obtained based on the initial evaluation rules and equipment data. In this way, the first score and the second score are the evaluation results obtained by evaluating the severity, occurrence and detection according to different evaluation criteria. By using the same evaluation form, different evaluation criteria are distinguished, and diversified evaluation results are obtained. On the basis of FMEA (failure mode and effect analysis), the influence of solidified rules on complex business can be avoided, so that the target score obtained based on the first score and the second score can more realistically evaluate the severity, occurrence and detection of potential failure modes, and the authenticity of the target score is improved to a certain extent.

[0066] Optionally, step 104 may include the following steps:

[0067] Step 201: Calculate a target score corresponding to the preset device group in the target risk dimension based on the first score, the score weight corresponding to the first score, the second score, and the score weight corresponding to the second score.

[0068] In an embodiment of the present invention, a weighting method can be used to set scoring weights for the first score and the second score in advance, respectively, and based on the first score, the scoring weight corresponding to the first score, the second score, and the scoring weight corresponding to the second score, the target score corresponding to the preset device group in the target risk dimension is calculated.

[0069] In summary, in an embodiment of the present invention, by acquiring target device data collected for a preset device group; the target device data includes device data corresponding to multiple risk dimensions; for any target risk dimension in the multiple risk dimensions, a first score is obtained by evaluating the preset device group based on the actual operating conditions corresponding to each device in the preset device group; based on the initial evaluation rules and the device data, the preset device group is evaluated for the target risk dimension to obtain a second score; based on the first score and the second score, the target score corresponding to the preset device group in the target risk dimension is determined; the target score is used to characterize the risk level of the preset device group in the target risk dimension. In this way, the second score obtained based on the initial evaluation rules and the device data can provide a unified standard for device evaluation, ensuring the objectivity and consistency of the score. The first score obtained by evaluating based on the actual operating conditions of the device can directly reflect the operating status and performance of the device, improving the intuitiveness of the score. Furthermore, based on the first score and the second score, the target score corresponding to the preset equipment group in the target risk dimension is determined. By combining the first score and the second score, and supplementing and revising each other, a more comprehensive, objective and accurate gas equipment scoring system can be formed, avoiding the impact of the solidified initial scoring rules on complex businesses. The risk assessment based on the first score obtained in combination with the actual operating conditions on the basis of the second score has more flexible application value that is more combined with actual scenarios, and then the gas equipment is evaluated from multiple dimensions to ensure the comprehensiveness and accuracy of the score, thereby improving the practicality and effectiveness of the target score.

[0070] Furthermore, compared with the overall risk assessment of the preset device group, the embodiment of the present invention can refine the risk assessment content and more carefully capture the potential risks of the preset device group by performing risk assessment on the preset device group according to different dimensions, thereby being able to more comprehensively identify the various risks that the device may face, thereby improving the risk identification capability.

[0071] Optionally, step 104 may include the following steps:

[0072] Step 301: When a preset scoring condition is met, a target score corresponding to the preset device group in the target risk dimension is determined based on the first score and the second score.

[0073] In an embodiment of the present invention, preset scoring conditions are set in advance, wherein the preset scoring conditions may include condition settings for the evaluation period, condition settings for the number of scores, etc., which are not limited in the embodiment of the present invention. When the preset scoring conditions are met, the target score corresponding to the preset device group in the target risk dimension is determined based on the first score and the second score. Exemplarily, when the time interval with the last risk assessment meets the preset evaluation period, the target score corresponding to the preset device group in the target risk dimension is determined based on the first score and the second score; when a preset number of first scores are obtained, the target score corresponding to the preset device group in the target risk dimension is determined based on the first score and the second score.

[0074] In an embodiment of the present invention, by setting a preset scoring condition, when the preset scoring condition is met, the target score corresponding to the preset device group in the target risk dimension is determined based on the first score and the second score, and by periodically calculating the target score, computing resources are saved to a certain extent.

[0075] Optionally, the embodiment of the present invention may further include the following steps:

[0076] Step 401: perform data cleaning on the multiple first scores to obtain first target data.

[0077] In the embodiment of the present invention, since the first score is obtained by the target object evaluating the preset equipment group based on the actual operation situation, the professional background, experience and knowledge level of different target objects may be different, so the target object's understanding and evaluation of the same problem may also be different. In order to improve the accuracy and reliability of the score, data cleaning can be performed on multiple first scores to obtain the first target data. Among them, data cleaning can include weighting, variance, regression and other methods. It can be understood that the specific method of data cleaning is not limited to the embodiment of the present invention, and can be determined according to actual needs.

[0078] Exemplarily, the advantages of performing data cleaning on multiple first scores are:

[0079] a. Improve the accuracy and reliability of scoring: Through weighting, regression and other methods, we can eliminate the extreme opinions or biases of individual target objects, making the scoring more objective and fair. These methods can comprehensively consider the opinions of all target objects, reduce the influence of subjective factors, and thus improve the accuracy and reliability of scoring.

[0080] b. Balance the influence of different target objects: Through weighting, regression and other methods, different weights can be assigned to the target objects according to their professional background, experience and other factors, thereby balancing the influence of different target objects and making the scoring results more reasonable.

[0081] c. Handling outliers in data: During the evaluation of the target object, some outliers or outliers may appear, which may have an adverse effect on the final scoring results. Through weighting, regression and other methods, these outliers can be properly handled, such as reducing their weight or eliminating their influence, so as to ensure the stability and accuracy of the scoring results.

[0082] d. Improve the discrimination of scores: In the target object evaluation, if all target objects are given similar scores, then these scores will hardly reflect the advantages and disadvantages of different solutions. Through weighting, regression and other methods, the differences between scores can be increased and the discrimination of scores can be improved.

[0083] e. Facilitate subsequent analysis and decision-making: After pre-processing such as weighting and regression, the scoring data usually has better statistical characteristics and regularity, which is helpful for subsequent data analysis and decision-making. For example, you can understand the overall distribution of the score by calculating statistics such as weighted mean and variance; or use regression analysis to explore the relationship between the score and certain factors.

[0084] In the embodiment of the present invention, by performing data cleaning on a plurality of first scores to obtain first target data, the first target data after data cleaning can more accurately characterize the risk score of the preset device group in the target risk dimension.

[0085] Optionally, the embodiment of the present invention may further include the following steps:

[0086] Step 501: When the difference between the target score and the second score is greater than or equal to a preset threshold, update the initial evaluation rule.

[0087] In an embodiment of the present invention, since the target score is obtained based on the first score and the second score, that is, on the basis of the second score, the first score is used as a supplementary revision to obtain a target score that can more truly reflect the risk level of the preset device group in the target dimension. Therefore, when the difference between the target score and the second score is greater than or equal to the preset threshold, the initial scoring rule corresponding to the second score is characterized by a deviation, resulting in a large difference between the second score and the target score, and the initial evaluation rule can be updated. Among them, the preset threshold value can be ±a%. It can be set according to actual needs, and the embodiment of the present invention does not limit this. Exemplarily, when the difference between the target score and the second score is greater than or equal to the preset threshold, a special assessment can be performed based on the actual operating conditions, business characteristics, regions, enterprise characteristics and industry standards of each device in the preset device group to dynamically update the scoring rules.

[0088] For example, assume that according to the initial evaluation rules and equipment data, the preset equipment group is evaluated for the target risk dimension to obtain a second score, namely the SOD score. However, after practice in multiple locations, multiple enterprises and multiple equipment, the target score corresponding to the target risk dimension, that is, the actual risk level of the risk (such as the frequency of occurrence or the impact caused) is inconsistent with the second score (for example, the difference between the two is greater than or equal to the preset threshold), so the initial evaluation rules will be revised. Specifically, if the difference between the second score and the target score is less than the preset threshold, there is no need to make corrections. From the perspective of data analysis, it is determined whether the score change is caused by occasional deviations; if the difference between the second score and the target score is greater than or equal to the preset threshold, it means that there is an abnormality in the initial scoring rule, and the data needs to be re-analyzed and the initial scoring rule needs to be revised.

[0089] In the embodiment of the present invention, according to the relationship between the difference between the target score and the second score and the preset threshold, when the authenticity of the second score is low, the initial scoring rule can be updated and adjusted to ensure the authenticity and accuracy of the second score.

[0090] Figure 2 is a schematic diagram of the structure of an equipment risk assessment device provided by an embodiment of the present invention, such as Figure 2 As shown, the device may specifically include:

[0091] The first acquisition module 601 is used to acquire target device data collected for a preset device group; the target device data includes device data corresponding to multiple risk dimensions;

[0092] A second acquisition module 602 is used to acquire, for any target risk dimension among the multiple risk dimensions, a first score obtained by evaluating the preset device group based on the actual operation conditions corresponding to each device in the preset device group;

[0093] A first evaluation module 603 is used to evaluate the preset device group according to the target risk dimension based on the initial evaluation rule and the device data to obtain a second score;

[0094] The first determination module 604 is used to determine a target score corresponding to the preset device group in the target risk dimension based on the first score and the second score; the target score is used to characterize the risk level of the preset device group in the target risk dimension.

[0095] The embodiment of the present invention provides an equipment risk evaluation device, which obtains target equipment data collected for a preset equipment group; the target equipment data includes equipment data corresponding to multiple risk dimensions; for any target risk dimension in the multiple risk dimensions, obtains a first score obtained by evaluating the preset equipment group based on the actual operating conditions corresponding to each device in the preset equipment group; based on the initial evaluation rules and equipment data, evaluates the preset equipment group for the target risk dimension to obtain a second score; based on the first score and the second score, determines the target score corresponding to the preset equipment group in the target risk dimension; the target score is used to characterize the risk level of the preset equipment group in the target risk dimension. In this way, the second score obtained based on the initial evaluation rules and equipment data can provide a unified standard for equipment evaluation, ensuring the objectivity and consistency of the score. The first score obtained by evaluating based on the actual operating conditions of the equipment can directly reflect the operating status and performance of the equipment, improving the intuitiveness of the score. Furthermore, based on the first score and the second score, the target score corresponding to the preset equipment group in the target risk dimension is determined. By combining the first score and the second score, and supplementing and revising each other, a more comprehensive, objective and accurate gas equipment scoring system can be formed, avoiding the impact of the solidified initial scoring rules on complex businesses. The risk assessment based on the first score obtained in combination with the actual operating conditions on the basis of the second score has more flexible application value that is more combined with actual scenarios, and then the gas equipment is evaluated from multiple dimensions to ensure the comprehensiveness and accuracy of the score, thereby improving the practicality and effectiveness of the target score.

[0096] Optionally, the first determining module 604 includes:

[0097] The first determination submodule is used to calculate the target score corresponding to the preset device group in the target risk dimension based on the first score, the score weight corresponding to the first score, the second score, and the score weight corresponding to the second score.

[0098] Optionally, the first determining module 604 includes:

[0099] The second determination submodule is used to determine a target score corresponding to the preset device group in the target risk dimension based on the first score and the second score when a preset score condition is met.

[0100] Optionally, the device further comprises:

[0101] The third acquisition module is used to perform data cleaning on the multiple first scores to obtain first target data.

[0102] Optionally, the device further comprises:

[0103] The first updating module is used to update the initial evaluation rule when the difference between the target score and the second score is greater than or equal to a preset threshold.

[0104] The present invention also provides an electronic device, see Figure 3 , including: a processor 701, a memory 702, and a computer program 7021 stored in the memory and executable on the processor, wherein when the processor executes the program, the device risk assessment method of the aforementioned embodiment is implemented.

[0105] The present invention also provides a readable storage medium. When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the device risk assessment method of the aforementioned embodiment.

[0106] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0107] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the description of the above specific languages ​​is for disclosing the best mode of the present invention.

[0108] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0109] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting the following intention: that the claimed invention requires more features than the features explicitly recited in each claim. More specifically, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Therefore, the claims that follow the specific embodiment are hereby expressly incorporated into the specific embodiment, with each claim itself serving as a separate embodiment of the present invention.

[0110] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition they may be divided into a plurality of submodules or subunits or subcomponents. Except that at least some of such features and / or processes or units are mutually exclusive, all features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed in this manner may be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0111] The various component embodiments of the present invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components in the sorting device according to the present invention. The present invention may also be implemented as a device or apparatus program for executing part or all of the methods described herein. Such a program for implementing the present invention may be stored on a computer-readable medium, or may be in the form of one or more signals. Such a signal may be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0112] It should be noted that the above embodiments illustrate the present invention rather than limit it, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference symbol between brackets shall not be construed as a limitation on the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "one" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising a number of different elements and by means of a suitably programmed computer. In a unit claim enumerating a number of devices, several of these devices may be embodied by the same hardware item. The use of the words first, second, and third, etc., does not indicate any order. These words may be interpreted as names.

[0113] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0114] It should be noted that all actions of acquiring signals, information or data in this application are carried out in compliance with the relevant data protection laws and policies of the country where they are located and with the authorization given by the owner of the corresponding device.

[0115] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0116] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for equipment risk assessment, characterized in that: The method comprises: Acquire target device data collected for a preset device group; the target device data includes device data corresponding to multiple risk dimensions; For any target risk dimension among the multiple risk dimensions, obtaining a first score obtained by evaluating the preset device group based on the actual operation conditions corresponding to each device in the preset device group; Based on the initial evaluation rule and the device data, the preset device group is evaluated according to the target risk dimension to obtain a second score; Based on the first score and the second score, a target score corresponding to the preset device group in the target risk dimension is determined; the target score is used to characterize the risk level of the preset device group in the target risk dimension.

2. The method according to claim 1, characterized in that The determining, based on the first score and the second score, a target score corresponding to the preset device group in the target risk dimension includes: A target score corresponding to the preset device group in the target risk dimension is calculated based on the first score, the score weight corresponding to the first score, the second score, and the score weight corresponding to the second score.

3. The method according to claim 1, characterized in that The determining, based on the first score and the second score, a target score corresponding to the preset device group in the target risk dimension includes: When a preset scoring condition is met, a target score corresponding to the preset device group in the target risk dimension is determined based on the first score and the second score.

4. The method according to claim 1, characterized in that: The method further comprises: Data cleaning is performed on the multiple first scores to obtain first target data.

5. The method according to claim 1, characterized in that The method further comprises: When the difference between the target score and the second score is greater than or equal to a preset threshold, the initial evaluation rule is updated.

6. An equipment risk assessment device, characterized in that: The device comprises: A first acquisition module is used to acquire target device data collected for a preset device group; the target device data includes device data corresponding to multiple risk dimensions; A second acquisition module is used to obtain, for any target risk dimension among the multiple risk dimensions, a first score obtained by evaluating the preset device group based on the actual operation conditions corresponding to each device in the preset device group; A first evaluation module, configured to evaluate the preset device group according to the target risk dimension based on the initial evaluation rules and the device data, to obtain a second score; A first determination module is configured to determine a target score corresponding to the preset device group in the target risk dimension based on the first score and the second score; the target score is configured to characterize a risk level of the preset device group in the target risk dimension.

7. The device according to claim 6, characterized in that The first determining module includes: The first determination submodule is used to calculate the target score corresponding to the preset device group in the target risk dimension based on the first score, the score weight corresponding to the first score, the second score, and the score weight corresponding to the second score.

8. The device according to claim 6, characterized in that The first determining module includes: The second determination submodule is used to determine a target score corresponding to the preset device group in the target risk dimension based on the first score and the second score when a preset score condition is met.

9. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the device risk assessment method according to any one of claims 1 to 5 is implemented.

10. A readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the device risk assessment method described in any one of claims 1-5.

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