Enterprise production risk assessment method and device

Abstract

The invention relates to the technical field of enterprise production risk assessment, and discloses an enterprise production risk assessment method and device, and the method comprises the steps: obtaining the multi-source safety data of an enterprise, the enterprise comprises a plurality of mutually independent production regions, and the multi-source safety data comprises a plurality of types of safety data; fusing the multi-source security data of each production area, and performing risk scoring on each production area; and carrying out resource allocation on each production area according to a risk scoring result. According to the invention, unified modeling and scoring can be carried out on security data of different production areas and different dimensions, the breadth and depth of enterprise risk identification are improved, and accurate identification and positioning of enterprise security risks are realized; meanwhile, resource allocation can be automatically optimized based on a risk scoring result fusing multi-dimensional security data, and the resource utilization rate and the production management effect of an enterprise are improved.

Description

Technical Field

[0001] This application relates to the field of enterprise production risk assessment technology, specifically to an enterprise production risk assessment method and apparatus. Background Technology

[0002] With the continuous advancement of industrial automation and lean management, factory environment, health, and safety (EHS) management is receiving increasing attention. During factory operations, safety accidents, potential hazards, and dangerous sources are widespread. Therefore, how to scientifically and systematically identify, assess, and dynamically manage the risks in various areas of the factory has become a key focus of enterprise management and supervision.

[0003] Currently, mainstream factory EHS risk management systems typically use single-dimensional data such as accident statistics, hazard investigation, and hazard identification for risk assessment. This approach cannot accurately assess enterprise production risks, and consequently cannot accurately allocate resources. As a result, some areas suffer from resource waste, while others are unable to eliminate risks in a timely manner due to insufficient resource allocation, thus affecting the enterprise's safe production. Summary of the Invention

[0004] This application aims to at least address the technical problems in related technologies where using single-dimensional data to assess enterprise risk results in an inaccurate assessment of enterprise production risks and an inability to accurately allocate resources.

[0005] To address the aforementioned technical problems, embodiments of this application provide a method for enterprise production risk assessment, comprising:

[0006] Acquire multi-source security data of an enterprise, wherein the enterprise includes multiple independent production areas, and the multi-source security data includes multiple types of security data;

[0007] The multi-source security data of each production area are fused together to perform risk scoring for each production area;

[0008] Resources are allocated to each of the aforementioned production areas based on the risk scoring results.

[0009] In some embodiments, the multi-source security data of each of the production areas are fused to perform risk scoring on each of the production areas, including:

[0010] Based on the data type of the multi-source security data, a risk score for each type of security data in each of the production areas is determined;

[0011] Based on the risk weights of each type of safety data, the risk scores of each type of safety data in each production area are weighted to obtain the total risk score for each production area.

[0012] In some embodiments, the multi-source safety data includes accident data, hazard data, and danger source data. Based on the data type of the multi-source safety data, a risk score for each type of safety data in each production area is determined, including:

[0013] The accident data is risk-scored based on the severity of the accident.

[0014] Risk scoring is performed on the hazard data based on the rectification status and risk level of the hazard.

[0015] Risk scoring is performed on the hazard source data based on the hazard level of the hazard source.

[0016] In some embodiments, the method further includes:

[0017] Risk scores for each type of safety data in each of the production areas are determined based on time decay.

[0018] In some embodiments, after determining the risk score for each type of security data in each of the production areas, the method further includes:

[0019] The risk scores of various types of safety data are standardized.

[0020] In some embodiments, resource allocation is performed on each of the production areas based on risk scoring results, including:

[0021] The resource allocation ratio for each of the production areas is determined based on the total risk score of each production area.

[0022] The enterprise's resources are allocated to each of the aforementioned production areas according to the resource allocation ratio.

[0023] The resource allocation formula determined based on the resource allocation ratio is as follows:

[0024]

[0025] x i x represents the resources allocated to the i-th production region. i >0, B represents the total available resources, R i This represents the total risk score for the i-th production area.

[0026] In some embodiments, the method further includes:

[0027] Calculate the marginal benefit of each production area based on the allocated resources;

[0028] Resources are allocated to each of the production areas based on their marginal benefits.

[0029] In some embodiments, the method further includes:

[0030] The risk scoring results are monitored;

[0031] If the risk score exceeds a preset score threshold, a risk warning will be issued for the production area corresponding to the risk score.

[0032] In some embodiments, the method further includes:

[0033] Generate a corporate risk heatmap based on the risk scoring results;

[0034] Risk warnings and / or resource allocation are performed for each of the aforementioned production areas based on the enterprise risk heat map.

[0035] This application embodiment also provides an enterprise production risk assessment device, including:

[0036] The acquisition module is configured to acquire multi-source security data of an enterprise, wherein the enterprise includes multiple independent production areas, and the multi-source security data includes multiple types of security data.

[0037] The risk scoring module is configured to fuse the multi-source security data of each of the production areas and perform risk scoring on each of the production areas;

[0038] The resource allocation module is configured to allocate resources to each of the production areas based on the risk scoring results.

[0039] This application also provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and the processor implements the above-described enterprise production risk assessment method when executing the computer program in the memory.

[0040] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described enterprise production risk assessment method.

[0041] The enterprise production risk assessment method, apparatus, electronic device, and storage medium provided in this application acquire multi-source security data of an enterprise, wherein the enterprise includes multiple independent production areas, and the multi-source security data includes multiple types of security data; integrates the multi-source security data of each production area, performs risk scoring on each production area; and allocates resources to each production area based on the risk scoring results. This enables unified modeling and scoring of security data from different production areas and different dimensions, avoiding information silos, improving the breadth and depth of enterprise risk identification, and achieving accurate identification and positioning of enterprise security risks. Simultaneously, it can automatically optimize resource allocation based on the risk scoring results of the integrated multi-dimensional security data, improving the enterprise's resource utilization rate and production governance effectiveness. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of the enterprise production risk assessment method according to an embodiment of this application;

[0044] Figure 2 This is another flowchart of the enterprise production risk assessment method according to an embodiment of this application;

[0045] Figure 3 This is a schematic diagram of the risk scoring results of the enterprise production risk assessment method in this application embodiment;

[0046] Figure 4 This is a schematic diagram of the enterprise production risk assessment device according to an embodiment of this application. Detailed Implementation

[0047] Various embodiments and features of this application are described herein with reference to the accompanying drawings.

[0048] It should be understood that various modifications can be made to the embodiments described herein. Therefore, the above description should not be considered as limiting, but merely as an example of embodiments. Other modifications within the scope and spirit of this application will be apparent to those skilled in the art.

[0049] The accompanying drawings, which are included in and form part of this specification, illustrate embodiments of the present application and, together with the general description of the present application given above and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0050] These and other features of this application will become apparent from the following description of preferred forms of embodiments given as non-limiting examples, with reference to the accompanying drawings.

[0051] It should also be understood that although this application has been described with reference to some specific examples, those skilled in the art can certainly implement many other equivalent forms of this application, which have the features described in the claims and are therefore all within the scope of protection defined herein.

[0052] The above and other aspects, features and advantages of this application will become more apparent when taken in conjunction with the accompanying drawings and in view of the following detailed description.

[0053] Specific embodiments of this application are described thereafter with reference to the accompanying drawings; however, it should be understood that the claimed embodiments are merely examples of this application, which can be implemented in various ways. Well-known and / or repeated functions and structures are not described in detail to avoid unnecessary or redundant details that could obscure the application. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as the basis and representative basis for the claims to teach those skilled in the art to use this application in a variety of substantially any suitable detailed structures.

[0054] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0055] Example 1

[0056] Figure 1 A flowchart illustrating an embodiment of the enterprise production risk assessment method of this application is shown. Figure 1 As shown in the embodiment of this application, a method for assessing enterprise production risks is provided, including:

[0057] S101: Obtain multi-source security data from the enterprise.

[0058] A company comprises multiple independent production or work areas. That is, a company can be divided into N non-overlapping spatial units (such as sections, workshops, departments, or workstations), each spatial unit representing a production area, denoted as a set.

[0059] L = {1, 2, 3, i…N}

[0060] Here, each i∈L represents a section, workshop, department, or workstation. The time window for acquiring the enterprise's multi-source safety data is T years.

[0061] Multi-source safety data includes multiple types of safety data. For example, in this embodiment, it includes accident data (e.g., Class A), hazard data (e.g., Class B), and hazard source data (e.g., Class C). Accident data, also known as event data, records the time, location, severity level, and economic losses of an accident. Accident data can include data on workplace accidents, equipment accidents, and other types of accidents. Hazard data records data on hazard investigation, management, and rectification, including the time of hazard discovery, rectification status, hazard type, and classification. Hazard source data (Class C) records the location, category, and risk level of hazard sources. Hazard sources include hazardous chemicals, special equipment, etc.

[0062] S102: The multi-source security data of each production area are fused together to perform risk scoring on each production area.

[0063] Acquire multi-source security data from enterprises, integrate different types of security data from each production area according to the division of production areas, establish a scientific and dynamic risk scoring model, and score the security risks of each production area.

[0064] S103: Allocate resources to each of the production areas based on the risk scoring results.

[0065] After risk scoring is performed on each production area, resources can be allocated to each production area based on the risk scoring results. This allows for priority management of high-risk areas to avoid impacting enterprise production, as well as avoiding ineffective investment in low-risk areas, thus achieving the best cost-effectiveness in resource utilization and risk management.

[0066] The enterprise production risk assessment method provided in this application acquires multi-source security data of an enterprise, wherein the enterprise includes multiple independent production areas, and the multi-source security data includes multiple types of security data; it integrates the multi-source security data of each production area and performs risk scoring on each production area; and it allocates resources to each production area based on the risk scoring results. This method can uniformly model and score security data from different production areas and different dimensions, avoiding information silos, improving the breadth and depth of enterprise risk identification, and achieving accurate identification and positioning of enterprise security risks. At the same time, it can automatically optimize resource allocation based on the risk scoring results of the integrated multi-dimensional security data, improving the enterprise's resource utilization rate and production governance effectiveness.

[0067] In addition, the risk scoring results mentioned above can be dynamically adjusted based on multi-source safety data and changes in the enterprise's production area. This allows for real-time reflection of changes in enterprise risk through dynamic scoring, such as the impact of dynamic changes in hazard rectification measures and accident occurrences on risk levels, thereby achieving dynamic management of enterprise risk.

[0068] In some embodiments, step S102 involves fusing the multi-source security data from each of the production areas and performing a risk score on each of the production areas, including:

[0069] S1021: Determine the risk score of each type of security data in each of the production areas based on the data type of the multi-source security data;

[0070] S1022: Based on the risk weight of each type of safety data, the risk scores of each type of safety data in each production area are weighted to obtain the total risk score of each production area.

[0071] After acquiring multi-source security data from the enterprise, the data is aggregated according to the divided production areas to obtain risk scores for different types of security data in each production area. The specific data aggregation process is as follows:

[0072] 1) Accident data

[0073] Let n A，i S represents the total number of accidents in production area i within year T. A，i，j This represents the weighted score of the j-th accident (j = 1, 2, ..., n). A，i If the accident data is such that the original risk score is:

[0074]

[0075] The raw risk score of accident data can be graded according to the severity of the accident. For example, death = 100 points, serious injury = 50 points, and minor injury = 10 points.

[0076] In specific implementation, such as Figure 3 As shown, risk scores can also be assigned to accident data based on other scoring criteria. For example, risk scores can be assigned to accident data based on the impact of the accident (including lost working hours, whether work capacity is limited, pain, false alarm, etc.) and the accident handling method (including medical treatment, first aid, etc.).

[0077] 2) Hazard data

[0078] Let n B，i S represents the total number of potential hazards in production area i within year T. B，i，j This represents the weighted score of the j-th hazard (j = 1, 2, ..., n). B，i ).

[0079] The original risk score for the hazard data is:

[0080]

[0081] The raw risk score for hazard data can be graded based on the hazard's rectification status and risk level, such as: Major hazard / Unrectified = 20 points, General hazard / Rectified = 5 points. Accurate scoring of hazard data can be achieved by using both rectification status and risk level as dual criteria.

[0082] In specific implementation, such as Figure 3 As shown, risk scores can also be assigned to hazard data based on other scoring criteria. For example, risk scores can be assigned to hazard data based on environmental / occupational health and safety risk levels: major risk = 5 points, significant risk = 2 points, general risk = 0 points, and low risk = 0 points.

[0083] 3) Hazard source data

[0084] Let n c，i Let S represent the total number of hazardous sources in production area i within year T. c，i，j This represents the risk score of the j-th hazard source (j = 1, 2, ..., n). c，i ).

[0085] The original risk score for the hazard source data is:

[0086]

[0087] The raw risk score of hazard source data can be assigned based on the enterprise's hazard source classification standard (hazard level). For example, a hazard source with a higher hazard level (high-risk hazard source) will have a higher score, and a hazard source with a lower hazard level (low-risk hazard source) will have a lower score.

[0088] Optionally, the method further includes:

[0089] S1023: Determine the risk score of each type of safety data in each of the production areas based on time decay.

[0090] In practice, time decay is introduced to reflect the weight of the latest data, and λ is set as the time decay coefficient (0 < λ < 1). For example, λ = 0.8 means that for each year earlier, the score decays to 80% of its original value. Since the latest data has a significant impact on enterprise production, the time decay coefficient can be used to more accurately assess the enterprise's safety risks.

[0091] For example, when determining the risk score of accident data based on time decay, the following scoring formula can be used to score the risk of each accident:

[0092]

[0093] Among them, t now t represents the current time. A,i,jS represents the time of occurrence of the i-th accident, which can be expressed in year, month, or day. A,i,j This represents the weighted score of the j-th accident (j = 1, 2, ..., n). A,i ).

[0094] The same processing applies to data on potential hazards and data on sources of danger.

[0095] In some embodiments, after determining the risk score of each type of security data in each of the production areas through step S1021, the method further includes:

[0096] S1024: Standardize the risk scores of various types of safety data.

[0097] In this step, data standardization can be performed on various types of security data to eliminate the order-of-magnitude differences in risk scores between different data types. For example, the maximum value can be used for data normalization to obtain a normalized score value for each type of data risk score. The specific normalization formula is expressed as follows:

[0098]

[0099] The normalized score range is [0,1], and the maximum value can be determined by the maximum score of all production areas in the entire historical period of the plant.

[0100] After standardizing the data for each type of security data, the initial weights for each type of security data are determined to be ω. A ω B ω C Each weight can be preset according to the actual situation of the enterprise. Then, the overall risk of each production area is assessed based on the determined weights. The formula for assessing the overall risk is:

[0101]

[0102] Among them, R i Let represent the total risk score of the i-th production area, with an interval of [0,1].

[0103] In this embodiment, a standardized and tiered risk scoring system is adopted, and dynamic adjustment of risk scores is supported. Compared with static and experience-based scoring methods, this system can more accurately score enterprise risks. Figure 3 As shown, the total risk score of each department of the enterprise can be output and displayed in the form of a bar chart.

[0104] In some embodiments, step S103, allocating resources to each of the production areas based on the risk scoring results, includes:

[0105] S1031: Determine the resource allocation ratio for each of the production areas based on the total risk score of each production area;

[0106] S1032: Allocate enterprise resources to each of the production areas according to the resource allocation ratio.

[0107] After determining the overall risk score for each production area, the required resource allocation ratio for each production area is determined based on the overall risk score. Then, the company's resources are allocated to each production area according to a preset resource allocation formula. Company resources may include capital, personnel, and technological resources, etc.

[0108] The resource allocation formula determined based on the resource allocation ratio is as follows:

[0109]

[0110] x i x represents the resources allocated to the i-th production region. i >0, B represents total available resources (e.g., annual budget amount), R i This represents the total risk score for the i-th production area.

[0111] In some embodiments, the method further includes:

[0112] S201: Calculate the marginal benefit of each of the production areas based on the allocated resources;

[0113] S202: Allocate resources to each of the production areas based on their marginal benefits.

[0114] In practical risk management, the risk reduction benefit per unit of resource is diminishing marginally. The risk reduction function after resource input can be expressed as:

[0115]

[0116] Among them, f i (x i Let α represent the risk reduction function after resource input in the i-th production area. i This represents the maximum risk that can be reduced in this area, β. i This represents the efficiency parameter.

[0117] Therefore, resource allocation can be optimized based on the risk reduction function, and the objective function for optimization is:

[0118]

[0119] The constraints are:

[0120]

[0121] By using the above objective function, resources are preferentially allocated to the production area with the highest current marginal benefit. Combined with the risk score allocation ratio, resources are allocated in a full and reasonable manner. In other words, intelligent optimization and dynamic adjustment of resources can be achieved based on the risk score results.

[0122] In other embodiments, resources may be allocated to each production area based solely on the marginal benefit of resource input.

[0123] Specifically, the resources of each production area are first initialized, that is, all x i =0, calculate the marginal risk reduction brought by each unit of resource (e.g., each unit of resource) in each production area; then, allocate resources cyclically according to the marginal risk reduction, allocating one unit of resource to the production area with the highest current marginal benefit each time; after allocation, the marginal benefit of each production area can be recalculated, and the cycle continues until the resources are exhausted, finally outputting the optimal resource allocation x1,…,x for each production area. N .

[0124] In some embodiments, the method further includes:

[0125] S301: Monitor the risk scoring results;

[0126] S302: If the risk score result exceeds the preset score threshold, a risk warning is issued for the production area corresponding to the risk score result.

[0127] In this embodiment, dynamic risk scoring can be performed on each production area of ​​the enterprise, and the risk scoring results can be monitored in real time. Based on the risk scoring results, it can be determined whether the corresponding production area is in a high-risk state. When the risk scoring result exceeds the preset scoring threshold, it is determined that the production area corresponding to the risk scoring result is in a high-risk state. At this time, a risk warning can be issued for the production area to avoid the accumulation of enterprise risks and ensure the safe operation of the enterprise.

[0128] Dynamic scoring and real-time early warning mechanisms significantly shorten the time from risk discovery to response, enabling enterprises to respond more promptly. At the same time, they can significantly improve enterprise safety performance, reduce accident rates and the recurrence rate of hidden dangers, and continuously improve enterprise EHS performance.

[0129] In some embodiments, the method further includes:

[0130] S401: Generate a corporate risk heatmap based on the risk scoring results;

[0131] S402: Based on the enterprise risk heat map, conduct risk warnings and / or resource allocation for each of the production areas.

[0132] After obtaining the risk scores for each production area of ​​the enterprise, the risk scores are mapped onto the enterprise's regional risk map to generate an enterprise risk heat map. The enterprise risk heat map can mark the risk distribution of each production area of ​​the enterprise by color category and / or color intensity, for example, high-risk areas are darker and low-risk areas are lighter. This can accurately identify the risk distribution of each production area of ​​the enterprise, realize the visualization of the enterprise's spatial distribution and the targeted identification of risks, intuitively display high-risk areas, and carry out risk improvement and three-level inspections for high-risk areas. This provides data basis for the enterprise's precise rectification and inspection route optimization, and improves the digitalization and intelligence level of the enterprise's EHS management.

[0133] This implementation can form a closed-loop optimization of enterprise risk management, consisting of "data-scoring-allocation-rectification-feedback". Through closed-loop processes such as risk warning, rectification tracking, and effect evaluation (feedback), a continuous improvement mechanism for risk management is formed, making the enterprise risk management closed loop more complete and improving the scientific nature and execution of enterprise risk management.

[0134] like Figure 2 As shown, the enterprise production risk assessment method provided in this application embodiment is specifically explained using a manufacturing enterprise as an example.

[0135] Enterprises can use a production risk assessment system to regularly collect safety data such as accident history, hazard investigation, and hazard identification from various departments, areas, and workstations, assigning standardized risk scores to each. Then, the multi-source data is fused and scored; for each production area, a weighted sum is calculated based on accident scores, hazard scores, and hazard scores to form a comprehensive risk score. Next, the production risk assessment system performs spatial distribution analysis, automatically generating an enterprise risk heatmap based on the comprehensive risk scores of each production area, highlighting high-risk areas. Simultaneously, the system optimizes resource allocation based on the scoring results, recommending priority allocation of rectification funds, personnel, and technical resources to high-risk areas, and automatically generating rectification plans. The production risk assessment system can also dynamically monitor enterprise risks and implement closed-loop risk management. It monitors changes in risk scores in real time; if the risk score of a certain area exceeds a preset threshold, it automatically issues an early warning and tracks the progress and effectiveness of rectification, achieving closed-loop management of enterprise production risks.

[0136] The enterprise production risk assessment method provided in this application embodiment will be specifically described below with reference to specific multi-source safety data. Table 1 shows the multi-source safety data of three workshops of the enterprise.

[0137] Table 1. Multi-source safety data for each production area

[0138]

[0139] Assume the time decay coefficient λ = 0.8.

[0140] First, risk scores were assigned to each workshop, and risk scores for each type of safety data in each workshop were calculated.

[0141] 1) Workshop A

[0142] Accident data:

[0143] Serious injury: 50 x 0.8 1 =40.00

[0144] Minor injury: 10 x 0.8 0.5 ≈8.94

[0145] Minor injury: 10 x 0.8 0.1 ≈9.74

[0146] Total accident score = 40.00 + 8.94 + 9.74 = 58.68 Hazard data:

[0147] Major Issue - Rectified: 20 × 0.8 1.5 ≈14.32

[0148] Major Issue - Unrectified: 20 × 0.8 0.3 ≈18.76

[0149] General (rectified): 5 x 0.8 0.2 ≈4.81

[0150] Total hazard score = 14.32 + 18.76 + 4.81 = 37.89 Hazard source data:

[0151] High risk: 20 × 0.8 1 =16.00

[0152] Medium risk: 10 × 0.8 0.5 ≈8.94

[0153] Low risk: 5 x 0.8 0.1 ≈4.87

[0154] Total hazard score = 16.00 + 8.94 + 4.87 = 29.812) Workshop B

[0155] Accident data:

[0156] Minor injury: 10 x 0.8 1 =8.00

[0157] Total accident data = 8.00

[0158] Hazard data:

[0159] Major Issue - Rectified: 20 × 0.8 1.2 ≈15.34

[0160] General (rectified): 5 x 0.8 1 =4.00

[0161] General - No rectification: 5 x 0.8 0.4 ≈4.56

[0162] Total hazard score = 15.34 + 4.00 + 4.56 = 23.90 Hazard source data:

[0163] High risk: 20 × 0.8 0.2 ≈19.22

[0164] Total hazard score = 19.22

[0165] 3) Workshop C

[0166] Accident data:

[0167] Accident=0

[0168] Hazard data:

[0169] General (rectified): 5 x 0.8 0.9 ≈4.13

[0170] Total hazard score = 4.13

[0171] Hazard source data:

[0172] Medium risk: 10 × 0.8 1 =8.00

[0173] Low risk: 5 x 0.8 0.5 ≈4.47

[0174] Total hazard score = 8.00 + 4.47 = 12.47

[0175] The risk scores for each type of safety data in each workshop, calculated as shown in Table 2, are as follows.

[0176] Table 2 Risk Scoring of Multi-Source Safety Data for Each Production Area

[0177] workshop Accident Score Hazard score Hazard source score A 58.68 37.89 29.81 B 8.00 23.90 19.22 C 0.00 4.13 12.47

[0178] Then, the risk scores for each type of safety data were normalized. Data normalization was performed based on the maximum values ​​in each column of Table 2. Accident data score: Workshop A had the highest score, 58.68; Hazard data score: Workshop A had the highest score, 37.89; Hazard source data score: Workshop A had the highest score, 29.81. The risk scores for multi-source safety data in each production area after data normalization are shown in Table 3.

[0179] Table 3 shows the risk scores of multi-source safety data for each production area after data normalization.

[0180] workshop Accident normalization value Hidden dangers are classified into one value. Hazard source normalization value A 1.00 1.00 1.00 B 0.136 0.631 0.645 C 0.000 0.109 0.418

[0181] Next, based on the risk weights of each type of safety data, a comprehensive score is given to each production area. Assume the weights of each type of safety data are: accident data weight ω A =0.5, weight ω of hidden danger data B =0.3, Hazard source data weight ω C =0.2. The risk scoring formula after weighting according to data type is:

[0182] R i = 0.5 × Accident Normalization Value + 0.3 × Hidden Danger Normalization Value + 0.2 × Hazard Source Normalization Value.

[0183] The calculation process of the comprehensive score for each production area based on the above risk scoring formula and the total risk score results are shown in Table 4.

[0184] Table 4. Calculation process of comprehensive score and total risk score for each production area

[0185] workshop Calculation process Risk Score A 0.5×1+0.3×1+0.2×1 1.00 B 0.5×0.136+0.3×0.631+0.2×0.645 0.386 C 0.5×0+0.3×0.109+0.2×0.418 0.117

[0186] As shown in Table 4, among the overall risk scores, workshop A has the highest risk (1.00), workshop B has a medium risk (0.386), and workshop C has a low risk (0.117).

[0187] Assume the total resources are allocated to an annual safety rectification budget of 1 million yuan.

[0188] Using resource allocation formula The budget funds allocated to each workshop according to the risk score ratio are as follows:

[0189]

[0190] Workshop A's allocated budget is:

[0191] Workshop B's allocated budget is:

[0192] The budget allocated to workshop C is as follows:

[0193] Example 2

[0194] Figure 4 A schematic diagram of the structure of an enterprise production risk assessment device according to an embodiment of this application is shown. Figure 4As shown in the illustration, this application also provides an enterprise production risk assessment device, comprising:

[0195] The acquisition module 10 is configured to acquire multi-source security data of an enterprise, wherein the enterprise includes multiple independent production areas and the multi-source security data includes multiple types of security data.

[0196] Risk scoring module 20 is configured to fuse the multi-source security data of each of the production areas and score the risk of each of the production areas;

[0197] The resource allocation module 30 is configured to allocate resources to each of the production areas based on the risk scoring results.

[0198] The enterprise production risk assessment device provided in this application corresponds to the enterprise production risk assessment method in the above embodiments. Any option in the enterprise production risk assessment method embodiments is also applicable to the enterprise production risk assessment device embodiments, and will not be repeated here.

[0199] Example 3

[0200] This application also provides an electronic device, which includes at least a memory and a processor. The memory stores a computer program, and the processor implements the above-described enterprise production risk assessment method when executing the computer program in the memory.

[0201] In some embodiments, the processor executing a computer program may be a processing device that includes one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a Complex Instruction Set Computing (CISC) microprocessor, a Reduced Instruction Set Computing (RISC) microprocessor, a Very Long Instruction Word (VLIW) microprocessor, a processor that runs other instruction sets, or a processor that runs a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as an Application-Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), a System-on-a-Chip (SoC), etc.

[0202] The memory may be a read-only memory (ROM), random access memory (RAM), phase-change random access memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), electrically erasable programmable read-only memory (EEPROM), other types of random access memory (RAM), flash drives or other forms of flash memory, cache, registers, static memory, optical disc read-only memory (CD-ROM), digital versatile optical disc (DVD) or other optical storage, magnetic tape cassette or other magnetic storage device, or any other possible non-transitory medium used to store information or instructions that can be accessed by computer equipment.

[0203] The electronic devices in this application embodiment may include, but are not limited to, fixed terminal devices such as servers, desktop computers, and digital TVs, as well as mobile terminal devices such as in-vehicle devices (e.g., head-up displays), handheld devices (e.g., mobile phones, tablets, etc.), and wearable devices (e.g., smartwatches, smart bracelets, etc.).

[0204] Example 4

[0205] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described enterprise production risk assessment method.

[0206] The computer-readable storage medium in this application embodiment can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. In this application embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device; for example, it can be the aforementioned memory.

[0207] The computer programs of embodiments of this application can be organized into one or more computer-executable components or modules. Various aspects of this application can be implemented with any number and combination of such components or modules. For example, aspects of this application are not limited to the specific computer-executable instructions or specific components or modules shown in the drawings and described herein. Other embodiments may include different computer-executable instructions or components having more or fewer functions than those shown and described herein.

[0208] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for assessing enterprise production risks, characterized in that, include: Acquire multi-source security data of an enterprise, wherein the enterprise includes multiple independent production areas, and the multi-source security data includes multiple types of security data; The multi-source security data of each production area are fused together to perform risk scoring for each production area; Resources are allocated to each of the aforementioned production areas based on the risk scoring results.

2. The method according to claim 1, characterized in that, The multi-source security data from each of the aforementioned production areas are fused to perform risk scoring for each of the aforementioned production areas, including: Based on the data type of the multi-source security data, a risk score for each type of security data in each of the production areas is determined; Based on the risk weights of each type of safety data, the risk scores of each type of safety data in each production area are weighted to obtain the total risk score for each production area.

3. The method according to claim 2, characterized in that, The multi-source safety data includes accident data, hazard data, and danger source data. Based on the data type of the multi-source safety data, a risk score for each type of safety data in each production area is determined, including: The accident data is risk-scored based on the severity of the accident. Risk scoring is performed on the hazard data based on the rectification status and risk level of the hazard. Risk scoring is performed on the hazard source data based on the hazard level of the hazard source.

4. The method according to claim 2, characterized in that, The method further includes: Risk scores for each type of safety data in each of the production areas are determined based on time decay.

5. The method according to claim 2, characterized in that, After determining the risk score for each type of safety data in each of the production areas, the method further includes: The risk scores of various types of safety data are standardized.

6. The method according to claim 1, characterized in that, Based on the risk assessment results, resources are allocated to each of the aforementioned production areas, including: The resource allocation ratio for each of the production areas is determined based on the total risk score of each production area. The enterprise's resources are allocated to each of the aforementioned production areas according to the resource allocation ratio. The resource allocation formula determined based on the resource allocation ratio is as follows: x i x represents the resources allocated to the i-th production region. i >0, B represents the total available resources, R i This represents the total risk score for the i-th production area.

7. The method according to claim 6, characterized in that, The method further includes: Calculate the marginal benefit of each production area based on the allocated resources; Resources are allocated to each of the production areas based on their marginal benefits.

8. The method according to claim 1, characterized in that, The method further includes: Monitor the risk scoring results; If the risk score exceeds a preset score threshold, a risk warning will be issued for the production area corresponding to the risk score.

9. The method according to claim 1, characterized in that, The method further includes: Generate a corporate risk heatmap based on the risk scoring results; Risk warnings and / or resource allocation are performed for each of the aforementioned production areas based on the enterprise risk heat map.

10. A device for assessing enterprise production risks, characterized in that, include: The acquisition module is configured to acquire multi-source security data of an enterprise, wherein the enterprise includes multiple independent production areas, and the multi-source security data includes multiple types of security data. The risk scoring module is configured to fuse the multi-source security data of each of the production areas and perform risk scoring on each of the production areas; The resource allocation module is configured to allocate resources to each of the production areas based on the risk scoring results.

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