Food safety risk assessment method and device, computer equipment and storage medium

Abstract

The invention relates to a food safety risk assessment method and device, computer equipment and a computer readable storage medium. The method comprises the following steps: acquiring risk monitoring data related to each risk assessment index in a food production process; the risk assessment index is determined by performing failure mode and influence FMEA analysis on each management and control object in the food production process; determining an index score corresponding to each risk assessment index based on the risk monitoring data; calculating a food safety risk index according to the index score corresponding to each risk assessment index; the food safety risk index is used for indicating the risk degree of the food safety accident. According to the method, the FMEA analysis is introduced into the food safety risk assessment process, so that the risk assessment indexes can more accurately point to real and key risk points in the production process, thereby realizing more timely risk state quantification and early warning, improving the timeliness of food safety risk early warning, and improving the risk assessment efficiency. Food safety accidents are effectively avoided, and food safety is guaranteed.

Description

Technical Field

[0001] This application relates to the field of food safety technology, and in particular to a food safety risk assessment method, apparatus, computer equipment, and computer-readable storage medium. Background Technology

[0002] Production enterprises need to conduct safety risk management and analysis of the food production process in order to avoid food safety incidents and ensure food safety.

[0003] Traditional food safety risk management models mainly rely on regular manual inspections, product sampling, and retrospective analysis based on historical complaints or incidents. Therefore, due to factors such as personnel capabilities, information asymmetry, numerous and widespread risk points, and complex and volatile risks, the timeliness of food safety risk warnings is poor. Summary of the Invention

[0004] Therefore, it is necessary to provide a food safety risk assessment method, device, computer equipment, and computer-readable storage medium to address the aforementioned technical problems, so as to achieve timely and accurate food safety risk early warning and improve the timeliness of food safety early warning.

[0005] Firstly, this application provides a food safety risk assessment method, including:

[0006] Obtain risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined through Failure Mode and Effects Analysis (FMEA) of various controlled objects during the food production process.

[0007] Based on the risk monitoring data, determine the index score corresponding to each of the risk assessment indicators;

[0008] The food safety risk index is calculated based on the scores of each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0009] In one embodiment, before acquiring risk monitoring data related to various risk assessment indicators during the food production process, the method further includes:

[0010] Redundant elements are eliminated, merged, reorganized, simplified, and new elements are added to each controlled object in the food production process to construct multiple risk factors;

[0011] FMEA analysis was performed on each of the aforementioned risk factors to select multiple risk assessment indicators.

[0012] In one embodiment, the FMEA analysis of each of the risk factors is performed to select multiple risk assessment indicators from the risk factors, including:

[0013] Determine the severity of the failure consequences, the probability of failure, and the ease of failure detection for each of the aforementioned risk factors;

[0014] For each of the aforementioned risk factors, a risk priority coefficient is determined based on the severity of the corresponding failure consequences, the probability of failure occurrence, and the ease of failure detection.

[0015] Based on the risk priority coefficient, multiple risk assessment indicators are determined from each of the risk elements; the risk assessment indicators include at least one of the following: compliance indicators, raw material indicators, product output indicators, accident and hazard indicators, retrospective analysis compliance, production condition compliance, and material balance rate.

[0016] In one embodiment, the risk assessment indicators include non-veto type indicators; determining the indicator score corresponding to each of the risk assessment indicators based on the risk monitoring data includes:

[0017] For each of the aforementioned non-veto type indicators, the indicator score for each non-veto type indicator is calculated based on the risk monitoring data corresponding to the non-veto type indicator and the evaluation strategy corresponding to the non-veto type indicator.

[0018] In one embodiment, the risk assessment indicators further include a veto type indicator; determining the indicator score corresponding to each of the risk assessment indicators based on the risk monitoring data includes:

[0019] For each of the aforementioned veto type indicators, if the risk monitoring data corresponding to the veto type indicator meets the corresponding deduction conditions, then the indicator score of the veto type indicator is determined as the corresponding preset deduction value; or,

[0020] If the risk monitoring data corresponding to the veto type indicator does not meet the corresponding deduction conditions, the indicator score of the veto type indicator will be determined as zero.

[0021] In one embodiment, calculating the food safety risk index based on the index scores corresponding to each of the risk assessment indicators includes:

[0022] Based on the indicator weights corresponding to each of the aforementioned non-veto type indicators, the sum of the indicator scores for each of the aforementioned non-veto type indicators is calculated;

[0023] Subtracting the score of each of the veto types from the sum yields the food safety risk index.

[0024] In one embodiment, the compliance indicators include compliance with production and operation qualifications, compliance with organizational and personnel requirements, compliance with training management, compliance with document management, and compliance with health management requirements.

[0025] The production raw material indicators include water type compliance, raw material benchmarking satisfaction rate, and carbon dioxide type compliance.

[0026] The product output indicators include product sampling pass rate, product quality physicochemical score, product benchmarking satisfaction rate, product physicochemical pass rate, and product type inspection conformity.

[0027] The accident and hidden danger indicators include the number of public opinion incidents, the number of food safety incidents, the compliance of emergency drills, the number of major quality abnormalities, the rate of defective wines in warehouse sampling inspections, the rate of hidden danger handling, and the rate of negative experiences.

[0028] Secondly, this application also provides a food safety risk assessment device, comprising:

[0029] The acquisition module is used to acquire risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined by failure mode and impact analysis (FMEA) of various controlled objects in the food production process.

[0030] The determination module is used to determine the index score corresponding to each of the risk assessment indicators based on the risk monitoring data.

[0031] The calculation module is used to calculate the food safety risk index based on the index scores corresponding to each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0032] Thirdly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0033] Obtain risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined through Failure Mode and Effects Analysis (FMEA) of various controlled objects during the food production process.

[0034] Based on the risk monitoring data, determine the index score corresponding to each of the risk assessment indicators;

[0035] The food safety risk index is calculated based on the scores of each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0036] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0037] Obtain risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined through Failure Mode and Effects Analysis (FMEA) of various controlled objects during the food production process.

[0038] Based on the risk monitoring data, determine the index score corresponding to each of the risk assessment indicators;

[0039] The food safety risk index is calculated based on the scores of each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0040] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0041] Obtain risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined through Failure Mode and Effects Analysis (FMEA) of various controlled objects during the food production process.

[0042] Based on the risk monitoring data, determine the index score corresponding to each of the risk assessment indicators;

[0043] The food safety risk index is calculated based on the scores of each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0044] The aforementioned food safety risk assessment methods, devices, computer equipment, computer-readable storage media, and computer program products introduce FMEA (Failure Mode and Effects Analysis) into the food safety risk assessment process. By analyzing the potential failure modes and their impacts on various controlled objects, multiple risk assessment indicators are identified, enabling these indicators to more accurately pinpoint real and critical risk points in the production process. After acquiring risk monitoring data related to each risk assessment indicator during food production, the corresponding indicator score can be determined, and a food safety risk index can be calculated to promptly identify the degree of risk of a food safety incident. Therefore, it can eliminate the reliance on manual inspections, historical complaints, or retrospective analysis of incidents in traditional models, enabling more timely quantification and early warning of risk status before risks lead to actual incidents or product non-compliance. This improves the timeliness of food safety risk warnings and more effectively prevents food safety incidents and ensures food safety. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0046] Figure 1 Here is a flowchart of a food safety risk assessment method in one embodiment;

[0047] Figure 2 This is a system architecture diagram of a food safety risk assessment method in one embodiment;

[0048] Figure 3 This is a structural block diagram of a food safety risk assessment device in one embodiment;

[0049] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0051] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0052] Traditional food safety risk management models mainly rely on regular manual inspections, product sampling, and retrospective analysis based on historical complaints or incidents. Therefore, due to factors such as personnel capabilities, information asymmetry, numerous and widespread risk points, and complex and volatile risks, the timeliness of food safety risk warnings is poor.

[0053] Based on this, in an exemplary embodiment, such as Figure 1 As shown, this application provides a food safety risk assessment method that can be applied to management equipment with data calculation and processing capabilities.

[0054] The management device can be a terminal or a server. For example, it can include, but is not limited to, various personal computers, laptops, smartphones, tablets, drones, low-altitude aircraft, IoT devices, and portable wearable devices. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services such as cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and basic cloud computing services such as big data and artificial intelligence platforms. The server can be the backend server for the aforementioned target application, used to provide backend services to the client of the target application.

[0055] Specifically, the method includes the following steps:

[0056] Step 101: Obtain risk monitoring data related to each risk assessment indicator during the food production process; the risk assessment indicators are determined by failure mode and impact (FMEA) analysis of each controlled object during the food production process.

[0057] First, management equipment can collect targeted risk monitoring data related to each risk assessment indicator during the food production process, based on pre-determined risk assessment indicators. Risk assessment indicators are determined through FMEA analysis of various controllable objects during the food production process. These controllable objects are specific risk management items established to prevent, eliminate, or reduce food safety risks during food production, typically focusing on core dimensions such as compliance, raw material safety, production process, product quality, hazard management, and emergency preparedness.

[0058] Understandably, FMEA analysis is a systematic and forward-looking risk assessment engineering method. It can identify all possible ways in which errors might occur before the failure of each controlled object or process happens, assess their risks, and thus prioritize preventative measures. Therefore, compared to traditional methods that rely on periodic manual inspections, product sampling, or retrospective analysis based on historical complaints or incidents, the risk assessment indicators determined by FMEA analysis are derived through a rigorous engineering analysis process, making them more targeted and forward-looking.

[0059] Based on the identified risk assessment indicators, risk monitoring data related to each risk assessment indicator can be obtained during the food production process. For each risk assessment indicator, the corresponding risk monitoring data is a monitoring value, test result, or status record that is directly related to the specific risk assessment indicator and can be used to calculate or reflect the status or performance of the risk assessment indicator, thereby ensuring the accuracy and relevance of data collection.

[0060] Specifically, risk monitoring data can be obtained through manual uploading and / or automatic retrieval by the system. These two methods complement each other and work together to comprehensively cover all types of data sources required for food safety risk management, providing solid data support for subsequent risk analysis and evaluation.

[0061] For example, unstructured data or compliance documents that do not meet the conditions for automatic system collection and require manual verification and filing can be obtained through manual uploading. These include, but are not limited to, enterprise production and operation licenses, organizational personnel qualification certificates, appointment documents of food safety directors and food safety officers, training plans and assessment files, lists of laws, regulations and enterprise standards, emergency drill plans and summary reports, etc.

[0062] Structured data, real-time monitoring data, and cross-system flow data generated during food production can be collected automatically through the system, achieving real-time synchronization and extraction without manual intervention. This includes, but is not limited to, testing data such as water type test results, carbon dioxide type test data, product quality evaluation physicochemical indicators, and raw material benchmarking satisfaction rates from the LIMS (Laboratory Information Management System); feedback data such as the rate of poor customer experience and customer complaint information recorded by the 400 customer service system; operational data such as the number of major quality anomalies and accident records from the OA (Office Automation System); and traceability data such as material balance rates generated collaboratively by the SRM (Supplier Relationship Management), MDCS (Manufacturing Data Collection System), and ERP (Enterprise Resource Planning) systems. This ensures the timeliness and accuracy of dynamic data on production processes, product quality, and hazard management.

[0063] Furthermore, after the risk monitoring data is collected, it can undergo data cleaning, quantification, and normalization preprocessing, including but not limited to validating unstructured data, removing duplicate uploads, incomplete information, or invalid data that does not meet the specifications; identifying and processing outliers in structured data, using preset logical verification rules to screen out extreme data, missing data, and logically contradictory data that exceed reasonable ranges; standardizing and normalizing risk monitoring data from different sources and in different formats, converting non-numerical data into numerical data, etc.; the preprocessed risk monitoring data has a unified format, consistent logic, and can be directly used for risk assessment indicator scoring and risk index calculation.

[0064] Step 102: Determine the index scores corresponding to each risk assessment indicator based on the risk monitoring data.

[0065] After obtaining risk monitoring data, based on the differentiated evaluation strategy for each risk assessment indicator, the indicator score corresponding to each risk assessment indicator can be determined according to the risk monitoring data corresponding to each risk assessment indicator. This will output a numerical indicator score for each risk assessment indicator, laying the foundation for subsequent calculation of the food safety risk index.

[0066] In this step, the indicator scores can be calculated according to a preset time window. The preset time window can be a preset time length, such as a day, a month, or a quarter; or the preset time window can be determined based on preset time points, such as the 1st and 15th of each month, to determine the indicator scores corresponding to each risk assessment indicator based on the acquired risk monitoring data; in addition, the preset time window can also be determined in response to an assessment instruction. Whenever the management device receives an assessment instruction, it determines the indicator scores corresponding to each risk assessment indicator based on the acquired risk monitoring data and opens a new time window; this application does not specifically limit this.

[0067] Step 103: Calculate the food safety risk index based on the index scores corresponding to each risk assessment indicator; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0068] After determining the scores for each risk assessment indicator, statistical calculations can be performed on these scores to output a comprehensive food safety risk index. This index provides a clear and dynamic indication of the overall risk level of food safety incidents throughout the entire food production process, offering timely and objective data support for management decisions. This approach integrates the risk profiles of various dispersed control targets into a unified food safety risk index, transforming food safety risk assessment from qualitative description to quantitative analysis and providing enterprises with clear quantitative standards for risk warning.

[0069] Furthermore, based on the range of the food safety risk index, the risk level of a food safety incident within the food production process can be determined. For example, if the food safety risk index is below 80 points, the risk level can be classified as Level 4, indicating a very high probability of a food safety incident and a severe situation in food safety risk control. If the food safety risk index is between 80 (inclusive) and 85 points, the risk level can be classified as Level 3, indicating a relatively high probability of a food safety incident and a basically stable situation in food safety risk control. If the food safety risk index is between 85 (inclusive) and 90 points, the risk level can be classified as Level 2, indicating a relatively low probability of a food safety incident and a stable and improving situation in food safety risk control. If the food safety risk index reaches 90 points or above, the risk level can be classified as Level 1, indicating an extremely low probability of a food safety incident and a good situation in food safety risk control.

[0070] As can be seen from the above, the solution provided in this application introduces FMEA (Failure Mode and Effects Analysis) into the food safety risk assessment process. By analyzing the potential failure modes and their impacts of each controlled object, multiple risk assessment indicators are determined, enabling these indicators to more accurately pinpoint the real and critical risk points in the production process. After obtaining risk monitoring data related to each risk assessment indicator during food production, the corresponding indicator score can be determined, and a food safety risk index can be calculated to promptly identify the degree of risk of a food safety incident. Therefore, it can break away from the reliance on manual inspections, historical complaints, or retrospective analysis of incidents in traditional models, thereby achieving more timely risk quantification and early warning before risks lead to actual incidents or product non-compliance, improving the timeliness of food safety risk warnings, and more effectively preventing food safety incidents and ensuring food safety.

[0071] In one exemplary embodiment, prior to step 101, the method further includes:

[0072] Redundant elements are eliminated, merged, reorganized, simplified, and new elements are added to each controlled object in the food production process to construct multiple risk factors;

[0073] FMEA analysis was conducted on each risk element to select multiple risk assessment indicators.

[0074] In this exemplary embodiment, risk assessment indicators need to be constructed before acquiring risk monitoring data.

[0075] Specifically, we can first focus on the various controllable objects in the entire food production process, and combine the ECRSI (Eliminate-Combine-Rearrange-Simplify-Increase) principle of industrial engineering and management to carry out redundant elimination, merger and reorganization, simplification and addition processing for each controllable object, thereby constructing multiple risk factors.

[0076] For example, redundant control objects that have become ineffective, have redundant functions, or have no practical control value due to technological updates or process optimizations in the food production process can be eliminated; control objects that are closely related and have the same goal, such as raw material inspection upon arrival and supplier qualification review, can be merged and reorganized; control objects with cumbersome and inefficient operating procedures can be simplified; and corresponding risk factors can be added for production process upgrades and new testing technologies, and so on.

[0077] After constructing multiple risk factors, FMEA analysis can be further conducted on each risk factor. By analyzing the potential failure modes, failure effects, and failure causes of each risk factor, several risk factors with a high degree of impact on food safety can be screened out. These risk factors are then identified as risk assessment indicators, thereby ensuring that the risk assessment indicators can focus on the key risk points in the food production process and providing a reasonable basis for the risk assessment based on risk assessment indicators in this application embodiment.

[0078] In one implementation, FMEA analysis is performed on each risk element to select multiple risk assessment indicators, including:

[0079] Determine the severity of the consequences of failure, the probability of failure, and the ease of failure detection for each risk factor;

[0080] For each risk factor, a risk priority coefficient is determined based on the severity of the corresponding failure consequences, the probability of failure, and the ease of failure detection.

[0081] Based on the risk priority coefficient, multiple risk assessment indicators are determined from each risk element; the risk assessment indicators include at least one of the following: compliance indicators, raw material indicators, product output indicators, accident and hazard indicators, retrospective analysis compliance, production condition compliance, and material balance rate.

[0082] In this implementation method, the risk elements obtained after optimization by the ECRSI rule can be further analyzed by FMEA one by one, thereby selecting multiple risk assessment indicators.

[0083] Specifically, for each risk factor, we can first determine its corresponding severity of failure consequences, probability of failure, and difficulty of failure detection.

[0084] Specifically, regarding the severity of failure consequences, a knowledge graph of different failure modes and their consequence levels can be constructed by structuring official standards, industry norms, and historical major accident cases. Then, a rule engine is used to match keywords and map rules to each risk element with the knowledge graph to determine the severity of the failure consequences of that risk element. The severity of failure consequences indicates the level of consequences caused by the failure mode associated with that risk element. For example, a high severity value indicates that the failure mode of that risk element may lead to a serious food safety incident, triggering product recalls, the spread of foodborne diseases, or a major public opinion event; a low severity value indicates that the failure mode of that risk element may only lead to minor cosmetic defects or other consequences.

[0085] The probability of failure can be calculated by statistically analyzing historical failure events associated with each risk element. The probability of failure indicates the frequency and / or probability of the failure mode associated with that risk element occurring.

[0086] To assess the difficulty of failure detection, the acquisition methods and frequency of risk monitoring data for each risk element can be identified, and the difficulty of failure detection can be determined through rule matching. The difficulty of failure detection indicates the ease with which the failure mode related to that risk element can be detected before it actually occurs. For example, a high difficulty value indicates that the failure mode of that risk element is difficult to detect in a timely manner through conventional detection or real-time monitoring; a low difficulty value indicates that the failure mode of that risk element can be detected in real time through automated monitoring.

[0087] Based on this, the Risk Priority Number (RPN) of each risk element can be determined by combining the severity of its failure consequences, the probability of failure, and the ease of failure detection. The RPN directly reflects the overall impact of each risk element on food safety and the urgency of its control. For example, the RPN can be obtained by directly multiplying the severity of the failure consequences, the probability of failure, and the ease of failure detection for each risk element; alternatively, a risk matrix, weighted calculation, or other risk quantification models can be used to calculate the RPN of each risk element, without specific limitations.

[0088] Furthermore, risk factors can be sorted from high to low RPN, and risk factors with higher RPN can be selected as risk assessment indicators. Risk assessment indicators can comprehensively cover the core risk points of the entire food production process, including but not limited to compliance indicators, raw material indicators, product output indicators, accident and hidden danger indicators, retrospective analysis compliance, production condition compliance, and material balance rate.

[0089] Among these, compliance indicators assess whether enterprises comply with industry standards and internal rules and regulations during food production and operation, serving as the fundamental compliance guarantee for production. Raw material indicators monitor the relevant attributes of raw materials to control food safety risks at the source, preventing substandard raw materials from entering the production process. Product output indicators assess the quality of finished products after production, ensuring they meet market requirements and are a crucial step in ensuring the safety of end products. Accident and hazard indicators monitor the occurrence of various risk events during food production and the effectiveness of hazard investigation and rectification, promptly identifying and addressing potential problems that could lead to food safety incidents.

[0090] The review and analysis compliance assessment is used to evaluate whether the periodic review and analysis conducted by an enterprise for HACCP (Hazard Analysis Critical Control Point), TACCP (Threat Assessment and Critical Control Point), VACCP (Vulnerability Assessment and Critical Control Point), and QACCP (Quality Analysis Critical Control Point) meet the preset requirements, including the standardization of the review and analysis work, the frequency of compliance, and the effectiveness of the results. Among them, HACCP is a preventative control system used to protect food and prevent food safety hazards caused by biological, chemical, and physical factors; TACCP assesses the likelihood of hazards by conducting threat analysis on process vulnerabilities, opportunities, and capabilities, reducing the possibility of intentional sabotage, minimizing consequences or negative impacts, and protecting corporate reputation; VACACCP analyzes the possibility of food fraud from the perspectives of supply chain, socioeconomics, behavioral science, geography, and historical data, including supplier auditing, monitoring strategies, source / label verification, specification management, analytical testing strategies, and anti-counterfeiting technologies; QACCP analyzes the product manufacturing process from the perspectives of consumer perception / national standards and existing process control levels to identify weaknesses in quality control and ensure product quality meets consumer / national standards requirements.

[0091] Production condition compliance is used to assess whether the hardware conditions such as the environment, equipment, and facilities of food production meet the requirements for safe production. Material balance rate, on the other hand, monitors for problems such as raw material waste, finished product loss, or abnormal production processes by calculating the balance between raw material input, finished product output, and losses during the production process.

[0092] In one implementation method, compliance indicators include compliance with production and operation qualifications, compliance with organizational and personnel requirements, compliance with training management, compliance with document management, and compliance with health management.

[0093] The production raw material indicators include water type compliance, raw material benchmarking satisfaction rate, and carbon dioxide type compliance.

[0094] Product output indicators include product sampling pass rate, product quality physicochemical score, product benchmarking satisfaction rate, product physicochemical pass rate, and product type inspection conformity.

[0095] The indicators for accidents and potential hazards include the number of public opinion incidents, the number of food safety incidents, the compliance of emergency drills, the number of major quality abnormalities, the rate of defective wines in warehouse sampling inspections, the rate of hazard handling, and the rate of negative experiences.

[0096] Specifically, compliance indicators are set for the legality, standardization, and personnel competence of a company's operations. These include, but are not limited to: Production and Operation Qualification Compliance: This assesses whether the company's food production and operation licenses are complete, valid, and comply with legal and regulatory requirements. Organization and Personnel Compliance: This assesses whether the company's food safety management organization is compliant and whether the staffing of relevant positions meets the required standards. Training Management Compliance: This assesses whether the company's food safety-related training for employees is implemented as planned and whether the coverage is comprehensive. Document Management Compliance: This assesses whether the company's preparation, updating, and archiving of food safety management-related documents comply with regulations. Health Management Compliance: This assesses whether the health certificates of food production employees are valid and whether health management measures are implemented.

[0097] The raw material production indicators are set for the safety of the supply chain and basic materials, specifically including but not limited to: water type test compliance, used to assess whether the type test results of water used in food production meet relevant standard requirements; raw material benchmarking satisfaction rate, used to assess the degree to which purchased raw materials conform to preset standards; and carbon dioxide type test compliance, used to assess whether the type test results of carbon dioxide used in production meet relevant standard requirements.

[0098] Product output indicators are set for the quality and safety level of products and the ability to control the production process. These include, but are not limited to: Product sampling pass rate (to assess the proportion of qualified products in a sampled batch of finished products); Product quality physicochemical score (to quantitatively score product quality through physicochemical testing); Product benchmarking satisfaction rate (to assess the degree of conformity between the finished product and the preset quality standards); Product physicochemical first-pass rate (to assess the pass rate of processes in which the product's physicochemical indicators pass on the first attempt); and Product type inspection conformity (to assess whether the type inspection results of the finished product meet the relevant standard requirements).

[0099] Accident and hazard indicators are set to monitor exposed or potential risk events and their handling effectiveness, specifically including but not limited to: Number of public opinion incidents (to count the number of public opinion incidents related to the company's food safety); Number of food safety incidents (to count the number of food safety accidents that have occurred within the company); Emergency drill compliance (to assess whether the company's food safety emergency drills meet the preset requirements); Number of major quality anomalies (to count the number of major quality anomalies that occurred during the production process); Defective wine rate in warehouse sampling (to assess the proportion of defective wine products in warehouse finished product sampling out of the total number of samples taken); Hazard handling rate (to assess the completion rate of handling food safety hazards identified by the company); Negative experience rate (to count the proportion of negative consumer feedback on the product out of the total feedback).

[0100] These risk assessment indicators can comprehensively cover the food production process from multiple dimensions, including compliance, raw materials, products, and potential accidents, thereby providing an assessment basis for quantitatively assessing food safety risks and effectively improving the efficiency of food safety risk early warning and control.

[0101] In an exemplary embodiment, the risk assessment indicators include non-veto type indicators; therefore, step 102 includes:

[0102] For each non-veto type indicator, the indicator score is calculated based on the risk monitoring data and evaluation strategy corresponding to the non-veto type indicator.

[0103] In this exemplary embodiment, the risk assessment indicators may include non-veto type indicators. Non-veto type indicators refer to those risk assessment indicators that require a specific score to be calculated within a continuous score range based on their corresponding risk monitoring data. Therefore, for these non-veto type indicators, an independent and standardized scoring calculation can be performed on each non-veto type indicator based on the corresponding risk monitoring data.

[0104] First, the collected risk monitoring data related to each non-veto type indicator is analyzed and quantified. For example, raw material testing reports are analyzed into specific qualified batch values, or training records are analyzed into the proportion of personnel who have completed the assessment. Then, the evaluation strategy preset for that non-veto type indicator is invoked. The evaluation strategy usually includes clear thresholds, calculation formulas, or tiered deduction standards and other core requirements.

[0105] For example, for the raw material benchmarking satisfaction rate indicator, the evaluation strategy might stipulate that each test item of the raw material is tested. For each test item whose difference from the standard value exceeds the tolerance range, 2 points are deducted from the total weighted score of the raw material benchmarking satisfaction rate indicator. For each test item that is not recorded or has an abnormal value, 1 point is deducted from the total weighted score of the raw material benchmarking satisfaction rate indicator. The system automatically performs calculations according to the strategy, generating an indicator score between zero and the maximum weighted score for that indicator.

[0106] In this way, the scores of each non-veto type indicator can be quantitatively calculated according to the evaluation strategy, ensuring that the indicator scores objectively reflect the actual performance level of each non-veto type indicator, and providing data support for the subsequent calculation of the food safety risk index.

[0107] In one exemplary embodiment, the risk assessment indicators further include a veto type indicator; therefore, step 102 includes:

[0108] For each veto type indicator, if the risk monitoring data corresponding to the veto type indicator meets the corresponding deduction conditions, then the indicator score of the veto type indicator will be determined as the corresponding preset deduction value; or,

[0109] If the risk monitoring data corresponding to the veto type indicator does not meet the corresponding deduction conditions, the indicator score for the veto type indicator will be set to zero.

[0110] In this exemplary embodiment, the risk assessment indicators may further include veto type indicators. Veto type indicators refer to those risk assessment indicators whose scores are determined by a yes / no judgment, with different states corresponding to different indicator scores. Therefore, for veto type indicators, a yes / no judgment can be made for each veto type indicator based on the risk monitoring data corresponding to each veto type indicator.

[0111] Specifically, for each veto type indicator, the collected risk monitoring data is first analyzed and quantified. Then, the risk monitoring data is compared with the preset deduction conditions for that veto type indicator. If the risk monitoring data meets the deduction conditions, it indicates that the veto type indicator has violated regulations or failed to meet standards. In this case, the indicator score for that veto type indicator can be directly determined as the preset deduction value. If the risk monitoring data does not meet the deduction conditions, it indicates that the veto type indicator meets risk control requirements. In this case, the indicator score for that veto type indicator is determined to be zero, and no deduction is generated.

[0112] This can create strong constraints on veto-type indicators in food production, ensuring that major safety hazards are identified and taken seriously in a timely manner.

[0113] In one exemplary embodiment, step 103 includes:

[0114] Based on the indicator weights corresponding to each non-veto type indicator, calculate the sum of the indicator scores for each non-veto type indicator;

[0115] Subtracting the scores of each veto type from the total score yields the food safety risk index.

[0116] In this exemplary embodiment, the indicator weights corresponding to each non-veto type indicator can first be retrieved. The indicator weights reflect the importance of the non-veto type indicator in risk assessment. These weights can be pre-set by domain experts or managers based on historical experience and management priorities, or determined through normalization calculations based on the RPN of the non-veto type indicator. Alternatively, methods such as the analytic hierarchy process (AHP) and regression analysis can be used to determine the weights; no specific limitations are imposed.

[0117] After determining the weights of each non-veto type indicator, the sum of the scores of each non-veto type indicator can be calculated by weighted summation. This sum directly reflects the risk control level of each non-veto type indicator in the food production process and is the benchmark value of the food safety risk index.

[0118] Based on this, the sum of the scores of each non-veto type indicator can be algebraically summed with the scores of each veto type indicator (all of which are negative or zero). That is, the cumulative score of each veto type indicator is subtracted from the benchmark value to obtain the food safety risk index.

[0119] The process of calculating the food safety risk index described above can be expressed as follows:

[0120] Food safety risk index = Σ(Indicator score of non-veto type indicators * indicator weight) × 100 - Indicator score of veto type indicators.

[0121] In this way, the significant reduction effect of veto-type indicators can be intuitively reflected, and the food safety risk index can be dynamically and quantitatively reflected to reflect the comprehensive food safety risk status of enterprises from daily management performance to major risk events.

[0122] like Figure 2 The diagram shown is a system architecture diagram of a food safety risk assessment method in a specific embodiment of this application, which includes a risk analysis layer, a data acquisition layer, a data processing layer, a risk assessment layer, and a risk response layer.

[0123] Among them, the risk analysis layer uses the ECRSI method to merge, rearrange, simplify and add new food safety risk control objects to construct multiple risk elements. Then, the RPN of each risk element is calculated through FMEA analysis, thereby selecting multiple risk assessment indicators.

[0124] The data acquisition layer is responsible for acquiring risk monitoring data related to various risk assessment indicators from multiple aspects, such as enterprise production and operation licenses, organizational and personnel qualifications, employee health status, production environment conditions, production process data, product quality testing data, hazard management data, and accident information.

[0125] The data processing layer preprocesses the collected risk monitoring data, including data cleaning, quantification and normalization, as well as converting non-numerical data into numerical data to generate risk monitoring data with a standardized data structure.

[0126] The risk assessment layer determines the index score corresponding to each risk assessment indicator based on the risk monitoring data, and then calculates the food safety risk index based on the index score corresponding to each risk assessment indicator.

[0127] The risk response layer classifies enterprises into categories based on a tiered management mechanism using a food safety risk index. If the food safety risk index is below 80, the risk level is classified as Level 4, requiring emergency response measures. If the index is between 80 and 85, the risk level is classified as Level 3, requiring focused supervision and guidance. If the index is between 85 and 90, the risk level is classified as Level 2, requiring general supervision. If the index reaches 90 or above, the risk level is classified as Level 1, requiring regular supervision and assessment. Furthermore, risk management strategies corresponding to each risk level can be generated to effectively address and reduce food safety risks.

[0128] For example, in one specific embodiment, the food safety risk assessment method provided in this application can determine 23 risk assessment indicators, including:

[0129] I. Veto Type Indicators (6 items in total):

[0130] 1. Compliance with production and operation qualifications:

[0131] Evaluation strategy: Verify the completeness, validity, and compliance of changes to production and operation qualifications. If the requirements are not met, deduct points according to the preset deduction values.

[0132] Data source: Manually uploaded.

[0133] 2. Compliance of organization and personnel:

[0134] Evaluation strategy: Verify the establishment of food safety management institutions and the qualifications of key personnel. If they do not meet the requirements, deduct points according to the preset deduction values.

[0135] Data source: Manually uploaded.

[0136] 3. Compliance with health management regulations:

[0137] Evaluation strategy: Verify the validity of the establishment of health records and health certificates of employees. If they do not meet the requirements, deduct points according to the preset deduction value.

[0138] Data source: Environmental Health and Safety Management System.

[0139] 4. Official product sampling pass rate:

[0140] Evaluation strategy: Compare the official product sampling pass rate with the preset sampling pass rate. If the pass rate is not met, deduct points according to the preset deduction value.

[0141] Data source: Manually uploaded.

[0142] 5. Number of public opinion incidents:

[0143] Evaluation strategy: Compare the actual number of food safety-related public opinion incidents with the preset number of incidents. If the target is not met, deduct points according to the preset deduction value.

[0144] Data source: Manually uploaded.

[0145] 6. Number of food safety incidents:

[0146] Evaluation strategy: Compare the actual number of food safety incidents with the preset number of safety incidents. If the target is not met, deduct points according to the preset deduction value.

[0147] Data source: Manually uploaded.

[0148] II. Non-veto type indicators (17 items in total):

[0149] 1. Training management compliance:

[0150] Evaluation strategy: Review the development and implementation of the training plan, the achievement of training hours, and the training assessment results for specific positions. If the requirements are not met, points will be deducted from the full score based on the number of times or people, and the corresponding indicator score will be obtained.

[0151] Indicator weight: 5%; Data source: Manually uploaded.

[0152] 2. Document management compliance:

[0153] Evaluation strategy: Review the establishment of the list of rules and standards, compliance evaluation, and the implementation of related training. If the requirements are not met, points will be deducted on the basis of full marks to obtain the corresponding indicator score.

[0154] Indicator weight: 5%; Data source: Manually uploaded.

[0155] 3. Water type conformity:

[0156] Evaluation strategy: Review the results of water type testing, testing frequency, internal sampling results, and water treatment configuration. If the requirements are not met, points will be deducted from the full score for each instance to obtain the corresponding indicator score.

[0157] Indicator weight: 5%; Data source: LIMS system.

[0158] 4. Raw material benchmarking satisfaction rate:

[0159] Evaluation strategy: Compare the raw material benchmarking results with the raw material benchmarking standards, and deduct points based on the degree of deviation from the full score to obtain the corresponding indicator score;

[0160] Indicator weight: 3%; Data source: LIMS system.

[0161] 5. Conformity of carbon dioxide type test:

[0162] Evaluation strategy: Review the type test results and frequency of carbon dioxide tests. If they do not meet the requirements, deduct points for each test on top of the full score to obtain the corresponding indicator score.

[0163] Indicator weight: 5%; Data source: LIMS system.

[0164] 6. Compliance with 4A review analysis:

[0165] Evaluation strategy: Review the implementation of the 4A review and analysis work. If it is not carried out, deduct points from the full score to obtain the corresponding indicator score.

[0166] Indicator weight: 5%; Data source: Manually uploaded.

[0167] 7. Quality assessment physicochemical score:

[0168] Evaluation strategy: Compare the scores of physicochemical indicators in the quality assessment with the physicochemical target values, and deduct points in a step-by-step manner based on the degree of decline on the basis of full marks to obtain the corresponding indicator scores;

[0169] Indicator weight: 8%; Data source: Manual upload.

[0170] 8. Product satisfaction rate:

[0171] Evaluation strategy: Compare the product benchmarking results with the product benchmarking standards, and deduct points in a tiered manner based on the pass rate of the inspection comparison on the basis of full marks, so as to obtain the corresponding indicator scores;

[0172] Indicator weight: 4%; Data source: LIMS system / manual upload.

[0173] 9. Physicochemical pass rate:

[0174] Evaluation strategy: Compare the first pass rate of the product's physicochemical indicators with the target physicochemical first pass rate, and deduct points in a step-by-step manner based on the decrease in the first pass rate, in addition to the full score, to obtain the corresponding indicator score;

[0175] Indicator weight: 5%; Data source: LIMS system.

[0176] 10. Product type conformity:

[0177] Evaluation strategy: Review the product type test results and test frequency. If the requirements are not met, deduct points for each test on top of the full score to obtain the corresponding indicator score.

[0178] Indicator weight: 5%; Data source: LIMS system.

[0179] 11. Compliance of emergency drills:

[0180] Evaluation strategy: Review the frequency of emergency drills and the completeness of drill records. If the requirements are not met, points will be deducted from the full score for each drill, and the corresponding indicator score will be obtained.

[0181] Indicator weight: 5%; Data source: Manually uploaded.

[0182] 12. Number of major quality anomalies:

[0183] Evaluation strategy: Compare the actual number of major quality anomalies with the preset number of quality anomalies, and deduct points based on the number of anomalies exceeding the preset number, in addition to the full score, to obtain the corresponding indicator score;

[0184] Indicator weight: 8%; Data source: OA system.

[0185] 13. Defective wine rate in warehouse spot checks:

[0186] Evaluation strategy: Compare the proportion of defective products found in warehouse spot checks with the target defect rate, and deduct points in a tiered manner based on the degree of exceedance on the basis of full marks, so as to obtain the corresponding indicator score;

[0187] Indicator weight: 10%; Data source: Manually uploaded.

[0188] 14. Hazard handling rate:

[0189] Evaluation strategy: Compare the completed hazard handling rate with the target hazard handling rate, and deduct points in a tiered manner based on the gap from the full score to obtain the corresponding indicator score;

[0190] Indicator weight: 7%; Data source: Manually uploaded.

[0191] 15. Poor customer experience rate:

[0192] Evaluation strategy: Compare the number of negative experiences reported by consumers with the preset negative experience rate, and deduct points in a tiered manner based on the number of negative experiences exceeding the maximum score to obtain the corresponding indicator score;

[0193] Indicator weight: 10%; Data source: 400 customer service system.

[0194] 16. Compliance with production conditions:

[0195] Evaluation strategy: Review the compliance of hygiene facilities, pest control and monitoring equipment at the production site. If the requirements are not met, deduct points on the basis of full marks for each item or instance to obtain the corresponding indicator score.

[0196] Indicator weight: 5%; Data source: Manually uploaded.

[0197] 17. Material balance rate:

[0198] Evaluation strategy: Compare the balance rate of actual material usage with theoretical consumption and the target material balance rate. Deduct points based on the decrease in the actual material usage with the target material balance rate.

[0199] Indicator weight: 5%; Data source: SRM, MDCS, ERP systems.

[0200] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0201] Based on the same inventive concept, this application also provides a food safety risk assessment device for implementing the food safety risk assessment method described above. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in one or more embodiments of the food safety risk assessment device provided below can be found in the limitations of the food safety risk assessment method described above, and will not be repeated here.

[0202] In one exemplary embodiment, such as Figure 3 As shown, a food safety risk assessment device is provided, comprising:

[0203] The acquisition module 201 is used to acquire risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined by failure mode and impact FMEA analysis of various controlled objects during the food production process.

[0204] The determination module 202 is used to determine the index score corresponding to each of the risk assessment indicators based on the risk monitoring data;

[0205] The calculation module 203 is used to calculate the food safety risk index based on the index scores corresponding to each of the risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0206] Each module in the aforementioned food safety risk assessment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0207] In one exemplary embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores measurement data and / or location information. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When executed by the processor, the computer program implements a food safety risk assessment method.

[0208] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0209] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0210] Obtain risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined through Failure Mode and Effects Analysis (FMEA) of various controlled objects during the food production process.

[0211] Based on the risk monitoring data, determine the index score corresponding to each of the risk assessment indicators;

[0212] The food safety risk index is calculated based on the scores of each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0213] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0214] Obtain risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined through Failure Mode and Effects Analysis (FMEA) of various controlled objects during the food production process.

[0215] Based on the risk monitoring data, determine the index score corresponding to each of the risk assessment indicators;

[0216] The food safety risk index is calculated based on the scores of each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0217] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0218] Obtain risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined through Failure Mode and Effects Analysis (FMEA) of various controlled objects during the food production process.

[0219] Based on the risk monitoring data, determine the index score corresponding to each of the risk assessment indicators;

[0220] The food safety risk index is calculated based on the scores of each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

[0221] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0222] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0223] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0224] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A food safety risk assessment method, characterized in that, The method includes: Obtain risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined through Failure Mode and Effects Analysis (FMEA) of various controlled objects during the food production process. Based on the risk monitoring data, determine the index score corresponding to each of the risk assessment indicators; The food safety risk index is calculated based on the scores of each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

2. The method according to claim 1, characterized in that, Before obtaining risk monitoring data related to various risk assessment indicators during the food production process, the process also includes: Redundant elements are eliminated, merged, reorganized, simplified, and new elements are added to each controlled object in the food production process to construct multiple risk factors; FMEA analysis was performed on each of the aforementioned risk factors to select multiple risk assessment indicators.

3. The method according to claim 2, characterized in that, The FMEA analysis of each of the aforementioned risk factors is used to select multiple risk assessment indicators, including: Determine the severity of the failure consequences, the probability of failure, and the ease of failure detection for each of the aforementioned risk factors; For each of the aforementioned risk factors, a risk priority coefficient is determined based on the severity of the corresponding failure consequences, the probability of failure occurrence, and the ease of failure detection. Based on the risk priority coefficient, multiple risk assessment indicators are determined from each of the risk elements; the risk assessment indicators include at least one of the following: compliance indicators, raw material indicators, product output indicators, accident and hazard indicators, retrospective analysis compliance, production condition compliance, and material balance rate.

4. The method according to claim 1, characterized in that, The risk assessment indicators include non-veto type indicators; the determination of the indicator scores corresponding to each risk assessment indicator based on the risk monitoring data includes: For each of the aforementioned non-veto type indicators, the indicator score for each non-veto type indicator is calculated based on the risk monitoring data corresponding to the non-veto type indicator and the evaluation strategy corresponding to the non-veto type indicator.

5. The method according to claim 4, characterized in that, The risk assessment indicators also include veto type indicators; the determination of the indicator scores corresponding to each risk assessment indicator based on the risk monitoring data includes: For each of the aforementioned veto type indicators, if the risk monitoring data corresponding to the veto type indicator meets the corresponding deduction conditions, then the indicator score of the veto type indicator is determined as the corresponding preset deduction value; or, If the risk monitoring data corresponding to the veto type indicator does not meet the corresponding deduction conditions, the indicator score of the veto type indicator will be determined as zero.

6. The method according to claim 5, characterized in that, The step of calculating the food safety risk index based on the index scores corresponding to each of the aforementioned risk assessment indicators includes: Based on the indicator weights corresponding to each of the aforementioned non-veto type indicators, the sum of the indicator scores for each of the aforementioned non-veto type indicators is calculated; Subtracting the score of each of the veto types from the sum yields the food safety risk index.

7. The method according to claim 3, characterized in that, The compliance indicators include compliance with production and operation qualifications, compliance with organizational and personnel requirements, compliance with training management, compliance with document management, and compliance with health management. The production raw material indicators include water type compliance, raw material benchmarking satisfaction rate, and carbon dioxide type compliance. The product output indicators include product sampling pass rate, product quality physicochemical score, product benchmarking satisfaction rate, product physicochemical pass rate, and product type inspection conformity. The accident and hidden danger indicators include the number of public opinion incidents, the number of food safety incidents, the compliance of emergency drills, the number of major quality abnormalities, the rate of defective wines in warehouse sampling inspections, the rate of hidden danger handling, and the rate of negative experiences.

8. A food safety risk assessment device, characterized in that, The device includes: The acquisition module is used to acquire risk monitoring data related to various risk assessment indicators during the food production process; the risk assessment indicators are determined by failure mode and impact analysis (FMEA) of various controlled objects in the food production process. The determination module is used to determine the index score corresponding to each of the risk assessment indicators based on the risk monitoring data. The calculation module is used to calculate the food safety risk index based on the index scores corresponding to each of the aforementioned risk assessment indicators; the food safety risk index is used to indicate the degree of risk of a food safety incident.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

Images