Food traceability method and system based on food safety

Abstract

The invention relates to the technical field of food safety traceability, in particular to a food traceability method and system based on food safety, and the method comprises the following steps: synchronously collecting environment data in a carriage and vehicle operation state data in a preset time interval on a cold chain transport vehicle, packaging the collected environment data and the vehicle running state data into a situation snapshot data packet; performing digital signature on the situation snapshot data packet by using a pre-stored private key; the signed situation snapshot data packet is transmitted to a food traceability distributed account book through an encryption channel for verification, and according to a preset logic judgment rule, internal logic consistency between environment data and vehicle running state data in the situation snapshot data packet is subjected to cross comparison; by introducing cross comparison of the environment data and the vehicle running state data and combining a digital signature technology, the authenticity of the data can be verified from double dimensions of a physical world and a digital world.

Description

Technical Field

[0001] This invention relates to the field of food safety traceability technology, and in particular to a food traceability method and system based on food safety. Background Technology

[0002] In the modern food industry, establishing a complete traceability system from source to table is crucial to ensuring food safety for consumers. Food traceability methods based on distributed ledger technology have emerged, leveraging the decentralized, tamper-proof, and transparent characteristics of this technology to provide a reliable data recording platform for every link in the food supply chain. However, even if the data itself is immutable, if it is tampered with at the source of the interaction between the physical and digital worlds before being recorded in the distributed ledger, the distributed ledger will record false information, fundamentally undermining the credibility of the entire traceability system. Especially in areas with stringent environmental parameter requirements, such as cold chain logistics, ensuring the authenticity and reliability of raw data collected by sensors and preventing interference or falsification during transmission remains a serious challenge.

[0003] For example, during cold chain transportation, temperature and humidity sensors inside refrigerated trucks periodically collect data, which, along with the vehicle's real-time geographical location information, is uploaded and recorded in new data blocks. However, in actual commercial operations, malicious parties (such as transport drivers) may physically interfere with the sensors or tamper with the data during the local network link from the sensors to the distributed ledger nodes, thereby writing falsified normal status information (such as temperature and humidity) into the distributed ledger to cover up actual anomalies (such as refrigeration equipment malfunctions). This untrustworthy off-chain data source is a key technical bottleneck that must be addressed when current distributed ledger traceability applications move from ideal models to real-world deployments.

[0004] While the security of existing traceability systems relies on the immutability of on-chain data, it cannot guarantee the authenticity of the data at the source. When a cold chain transport vehicle experiences a refrigeration equipment malfunction during transport, causing an abnormally high temperature inside the vehicle, the driver, to avoid hefty compensation claims, might manually edit and modify the temperature records during the malfunction period on their local data terminal, overwriting the abnormal data with normal temperature data before uploading this fabricated "perfect" data to the distributed ledger. Since the distributed ledger is only responsible for faithfully recording submitted data and ensuring its integrity, it cannot verify the authenticity of the submitted data at the source. Therefore, this fabricated, seemingly credible data chain not only fails to reveal the truth but also becomes an accomplice in covering up the problem, fundamentally undermining the credibility of the entire traceability system.

[0005] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technologies by proposing a food traceability method and system based on food safety.

[0007] In a first aspect, the present invention provides a food traceability method based on food safety, the method comprising the following steps: On cold chain transport vehicles, environmental data inside the compartment and vehicle operating status data are collected synchronously within a preset time interval, and the collected environmental data and vehicle operating status data are encapsulated into a context snapshot data package. The scenario snapshot data packet is digitally signed using a pre-stored private key; The signed context snapshot data package is transmitted to the food traceability distributed ledger through an encrypted channel. The digital signature of the context snapshot data package is verified by the food traceability distributed ledger. According to the preset logical judgment rules, the inherent logical consistency between the environmental data and the vehicle operation status data in the context snapshot data package is cross-checked. If there is a logical inconsistency in the cross-comparison judgment, the situation snapshot data packet is marked as abnormal through the food traceability distributed ledger, and the situation snapshot data packet after the abnormality is recorded.

[0008] Secondly, a food traceability system based on food safety is provided, which includes: The data acquisition module is used to synchronously collect environmental data and vehicle operating status data inside the compartment of a cold chain transport vehicle within a preset time interval, and encapsulate the collected environmental data and the vehicle operating status data into a context snapshot data package. The digital signature module is used to digitally sign the scenario snapshot data packet using a pre-stored private key; The data transmission module is used to transmit the signed context snapshot data package to the food traceability distributed ledger through an encrypted channel, and to verify the digital signature of the context snapshot data package using the food traceability distributed ledger. According to the preset logical judgment rules, the module cross-compares the inherent logical consistency between the environmental data and the vehicle operation status data in the context snapshot data package. The anomaly marking and recording module is used to mark the context snapshot data packet as anomaly through the food traceability distributed ledger if there is a logical inconsistency in the cross-comparison judgment, and to record the context snapshot data packet after the anomaly marking.

[0009] Compared with the prior art, the present invention has the following beneficial effects: This application, by introducing cross-referencing of environmental data and vehicle operating status data, combined with digital signature technology, can verify the authenticity of data from both the physical and digital worlds. This method can effectively identify and expose behaviors such as drivers tampering with temperature data to cover up refrigeration malfunctions, preventing false information from being recorded in the distributed ledger, thereby fundamentally enhancing the credibility of the entire food traceability system. Compared with existing technologies, this application not only guarantees the immutability of on-chain data, but more importantly, it ensures the authenticity of off-chain data sources, providing consumers with truly reliable food safety traceability information and effectively compensating for the shortcomings of existing traceability systems in ensuring the authenticity of data sources. Attached Figure Description

[0010] Figure 1 This is a flowchart of the method of the present invention.

[0011] Figure 2 This is a schematic diagram of the system structure of the present invention.

[0012] In the diagram: 201, Data Acquisition Module; 202, Digital Signature Module; 203, Data Transmission Module; 204, Anomaly Marking and Recording Module. Detailed Implementation

[0013] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0014] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0015] The food traceability method proposed in this application aims to ensure the authenticity and immutability of food data during cold chain transportation. "Environmental data" typically refers to parameters such as temperature and humidity inside the cold chain transport vehicle, directly reflecting the environmental conditions of food storage. "Vehicle operating status data" includes information such as the vehicle's geographical location, speed, engine operating status, and refrigeration unit operating status, reflecting the vehicle's operational status and the working condition of the refrigeration equipment. The "context snapshot data package" is a data set that integrates and encapsulates environmental data and vehicle operating status data collected at a specific point in time, representing the complete context of cold chain transportation at that point. The "pre-stored private key" is an encryption key used for digital signatures, ensuring the data package's origin is trustworthy and untampered. "Digital signature" is an encryption technology used to verify data integrity and sender identity. "Encrypted channel" refers to a data link protected by encryption technology during data transmission to prevent eavesdropping or tampering. The "food traceability distributed ledger" is a decentralized, immutable distributed database used to record and verify food traceability information. "Preset logical judgment rules" are a set of pre-defined rules used to determine the logical consistency between environmental data and vehicle operating status data.

[0016] By introducing digital signatures and distributed ledger verification mechanisms for contextual snapshot data packets, and combining the inherent logical consistency judgment between environmental data and vehicle operation status data, the problem of unreliable data sources in traditional traceability systems is effectively solved, thereby significantly improving the reliability and credibility of food traceability.

[0017] like Figure 1 The method shown is a food traceability method based on food safety, which includes the following steps: S101. On a cold chain transport vehicle, environmental data inside the compartment and vehicle operating status data are collected synchronously within a preset time interval, and the collected environmental data and vehicle operating status data are encapsulated into a context snapshot data package. It's important to note that, firstly, refrigerated transport vehicles require simultaneous collection of environmental data from the cargo compartment and vehicle operating status data. Environmental data can be obtained by deploying temperature and humidity sensors within the cargo compartment, which can automatically collect data periodically (e.g., every 5 or 10 minutes). Vehicle operating status data can be obtained through the onboard GPS module (geographic location and speed), the vehicle's CAN bus interface (engine speed, fuel consumption, etc.), and the refrigeration unit's controller interface (refrigerant pressure, evaporator temperature, etc.). After collection, this data is encapsulated into scenario snapshot data packets. For example, a scenario snapshot data packet might include a timestamp, cargo compartment temperature, cargo compartment humidity, vehicle latitude and longitude, vehicle speed, and the refrigeration unit's operating mode.

[0018] S102. Digitally sign the scenario snapshot data packet using the pre-stored private key; It should be noted that the digital signature in this step can be implemented using various encryption algorithms, such as RSA or Elliptic Curve Digital Signature Algorithm (ECDSA). A private key is pre-stored in the data acquisition device or vehicle terminal. After the scenario snapshot data packet is generated, this private key is used to sign the hash value of the data packet. This signing process ensures the integrity and authenticity of the data packet's origin; any tampering with the data packet content will cause signature verification to fail.

[0019] S103. Transmit the signed context snapshot data package to the food traceability distributed ledger through an encrypted channel. Use the food traceability distributed ledger to verify the digital signature of the context snapshot data package. According to the preset logical judgment rules, cross-compare the inherent logical consistency between the environmental data and the vehicle operation status data in the context snapshot data package. It should be noted that the signed scenario snapshot data packet is transmitted to the food traceability distributed ledger via an encrypted channel. The establishment of the encrypted channel can be achieved using technologies such as Transport Layer Security (TLS / SSL) or Virtual Private Network (VPN) to ensure the confidentiality and integrity of the data during transmission. Once the data packet arrives at the food traceability distributed ledger, the ledger will verify the digital signature of the scenario snapshot data packet using a pre-stored public key. If the digital signature verification passes, it indicates that the data packet has not been tampered with during transmission and indeed originates from a legitimate data collection device. Based on preset logical judgment rules, the inherent logical consistency between the environmental data and vehicle operating status data in the scenario snapshot data packet is cross-checked. For example, if the scenario snapshot data packet reports a normal cabin temperature, but the vehicle operating status data shows that the refrigeration unit has been off for an extended period or its power consumption is abnormally low, this may indicate a logical inconsistency in the data. The preset logical judgment rules can be a set of rules based on physical laws and empirical knowledge, such as "when the cabin temperature is maintained within the target temperature range, the power consumption of the refrigeration unit should not be lower than the preset lower limit of power consumption required to maintain that temperature."

[0020] S104. If there is a logical inconsistency in the cross-comparison judgment, the situation snapshot data packet is marked as abnormal through the food traceability distributed ledger, and the situation snapshot data packet after the abnormality is marked is recorded.

[0021] It's important to note that this step means that even if the digital signature verification passes, if the data content itself contains logical inconsistencies, the data packet will still be identified as abnormal by the system. The abnormally marked data packet will be recorded on the distributed ledger, but with an anomaly identifier attached, so that potentially problematic data can be identified during subsequent auditing and tracing. This mechanism effectively prevents the attempt to cover up actual anomalies by forging data.

[0022] The overall working principle of this application lies in constructing a more robust and reliable food traceability system through multi-dimensional data collection, digital signature technology, and cross-comparison based on logical consistency. Traditional traceability methods mainly rely on the immutability of on-chain data, but cannot effectively solve the problem of data source tampering. This application ensures the integrity and authenticity of data during transmission by digitally signing the context snapshot data packet before data is uploaded to the blockchain and verifying it using a distributed ledger. More importantly, this application introduces an inherent logical consistency judgment between environmental data and vehicle operating status data. For example, when the cabin temperature data shows normal, but the vehicle operating status data shows that the refrigeration unit is not working properly or the power consumption is abnormally low, the system can identify this logical contradiction and thus determine that the data is abnormal. This cross-comparison mechanism makes it difficult for a malicious party to simultaneously forge all related data that conforms to physical logic, even if a seemingly normal single data (such as temperature data) is forged through technical means. Once a logical inconsistency is found, even if the digital signature of the data packet is valid, the data packet will still be marked as abnormal and recorded on the distributed ledger, thus providing crucial abnormal information for subsequent auditing and problem tracing. Therefore, this application fundamentally improves the credibility of food traceability data, effectively solves the challenge of unreliable data sources in cold chain logistics, and ensures the credibility of the entire traceability system.

[0023] Compared to existing technologies, this application represents a significant technological advancement in addressing the issue of unreliable data sources for food traceability. Existing technologies primarily focus on the immutability of data on the distributed ledger blockchain, but they lack sufficient assurance regarding the authenticity of data before it is collected and transmitted to the blockchain. For example, in cold chain transportation, even if temperature data is recorded on the blockchain, if this data has been maliciously tampered with at the collection point, the blockchain record will still contain false information. The core innovation of this application lies not only in utilizing digital signatures to ensure the integrity and authenticity of the data transmission process, but also in introducing a cross-comparison of the inherent logical consistency between environmental data and vehicle operating status data in the contextual snapshot data package.

[0024] As one embodiment of the present invention, the step of cross-checking the inherent logical consistency between environmental data and vehicle operating status data in the context snapshot data packet according to preset logical judgment rules includes: Collect power consumption data of the refrigeration compressor; It should be noted that the above step refers to obtaining power consumption data from the refrigeration unit's control unit by installing power consumption sensors on the refrigeration vehicle. This power consumption data reflects the actual energy expended by the refrigeration unit to maintain the target temperature inside the vehicle compartment.

[0025] The collected power consumption data, environmental data, and vehicle operating status data are encapsulated together into a scenario snapshot data package; It should be noted that this step means that when generating the scenario snapshot data package, in addition to the original cabin environment data (such as temperature and humidity) and vehicle operating status data (such as speed, location, and engine status), it also includes the real-time power consumption data of the refrigeration compressor. Therefore, the scenario snapshot data package carries more comprehensive information about the cold chain operating scenario.

[0026] When the cabin temperature reported in the scenario snapshot data packet remains within the target temperature range, determine whether the power consumption data contained in the scenario snapshot data packet is lower than the preset power consumption lower limit required to maintain the target temperature. It should be noted that the preset power consumption lower limit in the above step is a minimum power consumption value that is pre-calculated or empirically set based on factors such as the efficiency of the refrigeration unit, the thermal insulation performance of the carriage, external environmental conditions, and target temperature, in order to ensure the minimum energy that the refrigeration unit must consume under normal operating conditions.

[0027] If the power consumption data contained in the scenario snapshot data packet is lower than the preset power consumption lower limit required to maintain the target temperature, then the cross-comparison judgment is considered to have a logical inconsistency.

[0028] It should be noted that this step means that although the reported temperature may be within the normal range, the actual power consumption of the refrigeration unit is insufficient to maintain that temperature, thus revealing a potential anomaly, such as refrigeration unit failure, inefficiency, or human intervention (such as shutting down the refrigeration unit to save fuel).

[0029] This application's solution deepens the judgment of data logic consistency in the cold chain transportation process by introducing power consumption data of the refrigeration compressor. Traditional judgment methods, which only compare the compartment temperature with the vehicle's operating status, may not effectively identify abnormal situations where the refrigeration unit is not actually operating normally, but the compartment temperature has not temporarily increased significantly. For example, when the external ambient temperature is low or the vehicle is briefly stopped, even if the refrigeration unit stops operating, the compartment temperature may remain within the target range for a period of time. In this case, if only temperature data is used, the system may misjudge it as normal. However, by collecting the power consumption data of the refrigeration compressor and incorporating it into the scenario snapshot data package, this application can establish an intrinsic correlation between the compartment temperature and the actual workload of the refrigeration unit. Specifically, when the compartment temperature is reported to be maintained within the target temperature range, the system further checks the power consumption data of the refrigeration unit. If the power consumption data is lower than the preset lower limit of power consumption required to maintain the target temperature, it indicates that the refrigeration unit is not expending enough energy to maintain the temperature, which creates a logical contradiction with the reported normal temperature. Therefore, even if the temperature data itself does not show any abnormality, the system can accurately identify potential cold chain anomalies or data falsification based on the inconsistency between the power consumption data and the temperature data. This cross-validation, based on the inherent logical relationship between physical quantities (power consumption) and environmental quantities (temperature), significantly improves the authenticity and reliability of traceability data.

[0030] In some preferred embodiments, a specific example is given below. Assume a cold chain transport vehicle is transporting frozen food in a high-temperature summer environment, with the target compartment temperature set at -18°C. At the moment a scenario snapshot data packet is collected, the temperature sensor inside the compartment reports a temperature of -17.5°C, within the target temperature range, and the vehicle is operating normally. Based solely on the aforementioned environmental data and vehicle operating status data, this scenario snapshot data packet might be judged as normal. However, in reality, to save fuel, the driver briefly shuts off the refrigeration unit while the vehicle is in motion. Due to the good insulation of the compartment and the short shutdown time, the compartment temperature has not immediately risen significantly. At this point, the solution of this application, by collecting the power consumption data of the refrigeration compressor, discovers that while the reported temperature is -17.5°C, the power consumption data of the refrigeration compressor is 0 watts, far below the preset power consumption lower limit required to maintain -18°C (for example, at the current external ambient temperature, maintaining -18°C requires at least 500 watts of power consumption). According to the logical judgment rules of this application, the system will immediately identify this logical inconsistency between power consumption and temperature, thereby marking the scenario snapshot data packet as abnormal. Therefore, even if the temperature data itself does not show any abnormalities, the solution proposed in this application can promptly and accurately reveal potential violations in the cold chain transportation process, effectively preventing food safety risks.

[0031] As one embodiment of the present invention, the process of determining the preset lower limit of power consumption required to maintain the target temperature includes: Acquire external ambient temperature and humidity data; It should be noted that the above step can be understood as collecting real-time environmental data through environmental sensors installed on the exterior of the refrigerated transport vehicle, or obtaining meteorological data of the current geographical location through data interaction with an external meteorological service platform. This data is dynamically changing and can reflect the actual heat load of the environment in which the vehicle is located.

[0032] Based on external ambient temperature data, external ambient humidity data, and the preset operating characteristics of the refrigeration unit, the lower limit of power consumption required to maintain the target compartment temperature is determined.

[0033] It should be noted that the above step refers to establishing a mathematical model or lookup table using the performance parameters of the refrigeration unit under different operating conditions (e.g., cooling capacity, energy efficiency ratio, relationship between power consumption and temperature difference, etc.). The preset operating characteristics of the refrigeration unit can be performance curves or parameter sets obtained after testing a specific model of refrigeration unit under laboratory conditions, reflecting the efficiency and power consumption performance of the refrigeration unit under ideal or standard operating conditions. By using real-time acquired external ambient temperature and humidity data as input, combined with these preset operating characteristics, the minimum electrical energy or power theoretically required by the refrigeration unit to maintain the target temperature inside the carriage under the current external environment can be calculated, i.e., the lower limit of power consumption. For example, when the external temperature is high or the humidity is high, the lower limit of power consumption required to maintain the same carriage temperature will be correspondingly increased.

[0034] This application's solution incorporates external ambient temperature and humidity data, combined with the refrigeration unit's preset operating characteristics, to dynamically adjust the preset lower limit of power consumption required to maintain the target temperature based on actual external environmental conditions. This solves the problem of traditional solutions where the preset lower limit of power consumption remains fixed and cannot adapt to changes in the external environment. Specifically, when the external ambient temperature or humidity increases, the heat load on the carriage increases accordingly, requiring the refrigeration unit to consume more energy to maintain the target temperature. By dynamically calculating the lower limit of power consumption, the actual operating requirements of the refrigeration unit can be more accurately reflected, thus avoiding misjudgments caused by changes in the external environment. Therefore, when the actual power consumption is lower than this dynamically adjusted lower limit, it can more reliably indicate potential abnormalities in the refrigeration system, such as refrigerant leakage, decreased compressor efficiency, or human intervention, thereby improving the accuracy of tracing and judgment.

[0035] In some preferred embodiments, it is assumed that a cold-chain transport vehicle transports food from a southern city to a northern city in summer. During the transportation process, the vehicle will experience different external environmental temperatures and humidities. For example, in the high-temperature and high-humidity area in the south, the external environmental temperature is 35°C and the humidity is 80%. At this time, the system will obtain these external environmental data. Combining with the preset operating characteristics of the refrigeration unit stored in advance (for example, the power consumption per unit heat load required to maintain a -18°C compartment temperature at an external temperature of 35°C and a humidity of 80%), the system can calculate that the lower limit value of the power consumption required to maintain a -18°C compartment temperature under the current working conditions is X kilowatts. When the vehicle travels to the dry area in the north, the external environmental temperature may drop to 25°C and the humidity is 40%. The system will obtain new external environmental data again and recalculate the lower limit value of the power consumption required to maintain a -18°C compartment temperature at this time as Y kilowatts (Y < X). Through this dynamic adjustment, even if the compartment temperature is maintained within the target range, but if the actual power consumption is lower than the lower limit value of the power consumption calculated under the current external environment, the system can accurately identify that there may be an abnormality in the refrigeration system, such as the refrigeration unit not operating at full load or having an efficiency problem, so as to mark the abnormality in time and ensure the authenticity of the traceability data.

[0036] As an implementation manner of the present invention, the steps of determining the lower limit value of the power consumption required to maintain the target compartment temperature according to the external environmental temperature data, the external environmental humidity data, and the preset operating characteristics of the refrigeration unit include: Monitoring the operating state parameters of the refrigeration unit; It should be noted that this step above refers to continuously collecting various key performance indicators of the refrigeration unit during actual operation. These parameters may include, but are not limited to, refrigerant pressure (such as high-side pressure and low-side pressure), evaporator temperature, condenser temperature, compressor current, voltage, fan speed, and defrost cycle status, etc. The purpose is to obtain the true performance of the refrigeration unit under the current working conditions in real time.

[0037] Adjusting the efficiency coefficient in the preset operating characteristics of the refrigeration unit according to the deviation between the operating state parameters of the refrigeration unit and the reference operating parameters of the refrigeration unit under standard working conditions to obtain the adjusted efficiency coefficient; It should be noted that the above step can be understood as follows: First, the real-time monitored operating parameters are compared with the preset reference operating parameters for this type of chiller unit under ideal or controlled standard operating conditions. This comparison quantifies the difference or deviation between the actual operating efficiency and the design efficiency of the chiller unit. For example, when the actual operating parameters deviate significantly from the reference parameters, it indicates that the actual efficiency of the chiller unit may be lower than its preset value. Subsequently, based on this degree of deviation, the efficiency coefficient representing the energy conversion efficiency in the preset operating characteristic model of the chiller unit is dynamically adjusted. For example, if the actual performance of the chiller unit declines, the efficiency coefficient will be lowered accordingly to more accurately reflect its current energy consumption level.

[0038] Based on external ambient temperature data, external ambient humidity data, and the adjusted efficiency coefficient, the lower limit of power consumption required to maintain the target compartment temperature is determined.

[0039] It should be noted that the above step refers to obtaining the adjusted efficiency coefficient, which reflects the current true efficiency of the refrigeration unit, and then substituting it along with the temperature and humidity data of the external environment into a thermodynamic model or a preset calculation formula to calculate the minimum power consumption required to maintain the target temperature inside the carriage under the current external environment. This calculation process comprehensively considers heat load, insulation performance, and the actual operating efficiency of the refrigeration unit.

[0040] This application's solution quantifies the deviation of the chiller's actual operating efficiency by introducing real-time monitoring of the chiller's operating status parameters and comparing them with reference parameters under standard operating conditions. Because this deviation is used to dynamically adjust the efficiency coefficient in the chiller's preset operating characteristics, the determined lower limit of power consumption more accurately reflects the chiller's energy consumption demand under current actual operating conditions. Therefore, this method overcomes the limitations of relying solely on static preset characteristics, ensuring that the calculation of the lower limit of power consumption is closer to reality, thus providing a more reliable benchmark for the logical consistency judgment of subsequent scenario snapshot data packets.

[0041] As one embodiment of the present invention, the step of adjusting the efficiency coefficient in the preset operating characteristics of the refrigeration unit based on the deviation between the operating state parameters of the refrigeration unit and the reference operating parameters of the refrigeration unit under standard operating conditions includes: The deviation between the operating status parameters of the refrigeration unit and the reference operating parameters of the refrigeration unit under standard operating conditions is obtained, and the deviation amount is obtained. It should be noted that the above step refers to quantifying the degree of performance deviation by comparing the current actual operating data of the refrigeration unit (such as refrigerant pressure, evaporator temperature, etc.) with the performance benchmark data of the unit under ideal or standard test conditions. This deviation can be a comprehensive indicator, reflecting the overall operating status of the refrigeration unit under the current operating conditions.

[0042] Based on the deviation, the efficiency coefficient adjustment factor is determined through a preset nonlinear mapping relationship; It should be noted that the above step can be understood as using a pre-established nonlinear model or function that reflects the complex performance characteristics of the refrigeration unit to transform the aforementioned deviation into a factor used to correct the efficiency coefficient. This nonlinear mapping relationship can be constructed based on a large amount of experimental data, simulation models, or expert experience. Its purpose is to more accurately capture the nonlinear law of efficiency changes of the refrigeration unit under different degrees of deviation. For example, when the deviation is small, the efficiency may show a slow downward trend, while when the deviation reaches a certain threshold, the efficiency may drop sharply.

[0043] The efficiency coefficient adjustment factor is applied to the efficiency coefficient in the preset operating characteristics of the refrigeration unit to obtain the adjusted efficiency coefficient.

[0044] It should be noted that this step specifically refers to applying the adjustment factor obtained through the nonlinear mapping relationship to the initial or nominal efficiency coefficient of the chiller unit using multiplication or addition, thereby obtaining a corrected efficiency coefficient that better reflects the current actual operating conditions. This adjusted efficiency coefficient will be used in subsequent calculations of the lower limit of power consumption to improve the accuracy of the calculations.

[0045] This application's solution introduces a nonlinear mapping relationship to determine the efficiency coefficient adjustment factor, enabling a more accurate reflection of the actual operating efficiency of the refrigeration unit under non-standard conditions. Traditional linear adjustments may fail to capture the complex changes in refrigeration unit performance under different deviations, leading to biases in efficiency coefficient estimation. By employing a nonlinear mapping, such as a function relationship established based on empirical data or a physical model, the efficiency coefficient adjustment can more closely approximate the actual operating state of the refrigeration unit, thereby ensuring the accuracy of subsequent calculations of the lower limit of power consumption required to maintain the target compartment temperature. Consequently, when the compartment temperature is maintained within the target range, the logical consistency judgment of power consumption data becomes more reliable, effectively avoiding misjudgments or omissions caused by inaccurate efficiency coefficient estimation.

[0046] Through the above technical solution, this application can significantly improve the accuracy of refrigeration unit efficiency coefficient adjustment, thereby making the calculation of the lower limit of power consumption required to maintain the target compartment temperature more accurate. This helps to more reliably identify abnormal behaviors that may exist in the cold chain transportation process, such as masking insufficient refrigeration by falsely reporting temperatures, thereby improving the accuracy and reliability of the food traceability distributed ledger in making logical consistency judgments on context snapshot data packets, and ultimately enhancing the credibility and security of the entire food traceability system.

[0047] As one embodiment of the present invention, the step of obtaining the deviation between the operating status parameters of the refrigeration unit and the reference operating parameters of the refrigeration unit under standard operating conditions, and obtaining the deviation amount, includes: Calculate the absolute difference or percentage deviation between each operating status parameter and each reference operating parameter to obtain multiple individual parameter deviations; The deviation is obtained by weighted summation or averaging of the deviations of multiple individual parameters.

[0048] Operating status parameters may include, but are not limited to, refrigerant pressure, evaporator temperature, condenser temperature, compressor speed, current, and voltage. Reference operating parameters refer to the theoretical or empirical values ​​of these parameters under standard, ideal operating conditions. When calculating the deviation of a single parameter, an absolute difference can be used, i.e., the absolute value of the operating status parameter minus the absolute value of the reference operating parameter, or a percentage deviation can be used, i.e., (operating status parameter - reference operating parameter) / reference operating parameter * 100%. These individual parameter deviations reflect the degree of deviation of the refrigeration unit from its ideal state at a specific operating moment. Furthermore, to comprehensively evaluate the overall operating status of the refrigeration unit, these individual parameter deviations can be integrated. For example, multiple individual parameter deviations can be weighted and summed, i.e., each parameter deviation can be assigned a weight, and then the weighted deviation values ​​can be added together. The weights can be set according to the importance of the parameter to the performance of the refrigeration unit. Alternatively, multiple individual parameter deviations can be simply averaged to obtain an overall deviation. This deviation aims to quantify the gap between the actual operating efficiency and the theoretical efficiency of the refrigeration unit.

[0049] The proposed solution quantifies the deviation of each key operating indicator by calculating the absolute difference or percentage deviation between each operating state parameter and the reference operating parameter. Therefore, by weighted summation or averaging of these individual parameter deviations, the overall operating state of the refrigeration unit can be comprehensively and holistically assessed to determine the degree of deviation from standard operating conditions, thus obtaining a deviation that accurately reflects the actual efficiency of the refrigeration unit. This deviation provides precise input for subsequently determining the efficiency coefficient adjustment factor through a preset nonlinear mapping relationship, ensuring that the adjustment of the efficiency coefficient in the preset operating characteristics of the refrigeration unit is more scientific and reasonable. This, in turn, allows the preset lower limit of power consumption required to maintain the target compartment temperature to more accurately reflect the actual situation.

[0050] As one embodiment of the present invention, the step of determining the efficiency coefficient adjustment factor through a preset nonlinear mapping relationship includes: By applying a preset nonlinear mathematical function, the efficiency coefficient adjustment factor is calculated based on the deviation.

[0051] The preset nonlinear mapping relationship refers to a nonlinear correspondence that aims to transform the deviation between the operating parameters of the chiller unit and the reference operating parameters under standard conditions into an adjustment factor that accurately reflects the actual efficiency changes of the chiller unit. Specifically, this nonlinear mapping relationship can be achieved through a preset nonlinear mathematical function. This preset nonlinear mathematical function can be modeled and optimized based on the actual operating characteristics of the chiller unit, historical data, and expert experience. For example, it can employ polynomial functions, exponential functions, logarithmic functions, sigmoid functions, or neural network models to better fit the complex nonlinear relationship between the deviation and the efficiency coefficient adjustment factor. The deviation is a quantitative indicator measuring the difference between the actual operating state of the chiller unit and the ideal standard state; its magnitude directly affects the calculation of the efficiency coefficient adjustment factor. By applying the preset nonlinear mathematical function, the corresponding efficiency coefficient adjustment factor can be accurately calculated based on the input deviation, thus providing a more accurate basis for determining the subsequent power consumption lower limit.

[0052] The proposed solution determines the efficiency coefficient adjustment factor by introducing a pre-defined nonlinear mathematical function. Its working principle lies in the complex nonlinear relationship between the efficiency variation of a chiller unit and the degree of deviation of its operating parameters. For example, in some operating ranges, a small parameter deviation may lead to a significant efficiency decrease, while in other ranges, even a large parameter deviation may result in a relatively gradual efficiency change. Using a simple linear mapping relationship may fail to accurately capture this complex nonlinear characteristic, leading to an inaccurate efficiency coefficient adjustment factor. By applying the pre-defined nonlinear mathematical function, this nonlinear relationship can be characterized more precisely, allowing the calculated efficiency coefficient adjustment factor to more realistically reflect the actual efficiency of the chiller unit under different degrees of deviation, thereby improving the accuracy of the power consumption lower limit calculation.

[0053] As one embodiment of the present invention, the step of determining the lower limit of power consumption required to maintain the target carriage temperature based on external ambient temperature data, external ambient humidity data, and an adjusted efficiency coefficient includes: Obtain the thermal insulation characteristics parameters of the compartment of cold chain transport vehicles; Obtain the thermal load characteristic parameters of the goods; Based on external ambient temperature data, external ambient humidity data, adjusted efficiency coefficient, carriage insulation characteristic parameters, and cargo heat load characteristic parameters, the lower limit of power consumption required to maintain the target carriage temperature is calculated.

[0054] Specifically, the thermal insulation characteristics of a cold chain transport vehicle's cargo compartment refer to a series of physical quantities used to describe the thermal insulation performance of the vehicle's compartment. These include the heat transfer coefficient (K-value), material thermal conductivity, thickness, and surface area of ​​the compartment walls, doors, floor, and roof. These parameters collectively determine the efficiency and rate of heat exchange between the interior and exterior environments through the enclosure structure. The cargo heat load characteristics refer to the characteristic data related to the transported goods that affect the heat balance within the compartment. These include the type and quantity of the goods, initial temperature, specific heat capacity, latent heat, and, for fresh products, respiration heat. These parameters are used to quantify the heat generated by the goods themselves or the heat absorbed / released during temperature changes.

[0055] This application's solution obtains the thermal insulation characteristics of the cold chain transport vehicle's compartment and the heat load characteristics of the cargo, and incorporates these parameters, along with external ambient temperature data, external ambient humidity data, and an adjusted efficiency coefficient, into the calculation of the lower limit of power consumption. Specifically, the compartment's thermal insulation characteristics are used to quantify the heat transfer efficiency between the compartment's enclosure structure and the external environment, thereby more accurately estimating the heat load generated by the compartment due to external temperature differences. Simultaneously, the cargo heat load characteristics are used to assess the heat generated or absorbed by the transported cargo itself, such as the respiratory heat of fresh produce or the sensible heat load of frozen foods. Therefore, the calculated lower limit of power consumption not only considers the impact of the external environment on refrigeration demand but also comprehensively reflects the contribution of the compartment's internal structure and the cargo to the refrigeration demand. This comprehensive calculation method allows the lower limit of power consumption to more realistically and accurately reflect the actual minimum energy consumption required to maintain the target compartment temperature, effectively compensating for the shortcomings of calculations relying solely on the external environment and refrigeration unit efficiency, and significantly improving the accuracy and reliability of identifying potential abnormal behaviors during cold chain transportation.

[0056] In one embodiment of the present invention, the operating parameters include refrigerant pressure and evaporator temperature.

[0057] Refrigerant pressure refers to the pressure of the refrigerant at different components (such as the compressor outlet, condenser, expansion valve inlet, and evaporator outlet) during the refrigeration cycle. These pressure values ​​directly reflect the refrigerant's circulation status and the load condition of the refrigeration unit. Evaporator temperature refers to the internal temperature of the evaporator in the refrigeration unit, which directly affects the cooling effect and heat exchange efficiency inside the vehicle compartment.

[0058] This application enables real-time acquisition of key operating indicators of the refrigeration unit by monitoring refrigerant pressure and evaporator temperature. Changes in these parameters reflect the actual operating efficiency of the refrigeration unit and the presence of any anomalies. For example, deviations in refrigerant pressure or evaporator temperature from the normal range may indicate refrigerant leakage, system blockage, compressor failure, or reduced cooling capacity. By comparing these actual operating parameters with reference operating parameters under standard conditions, the degree of performance deviation can be accurately assessed, which can then be used to adjust the efficiency coefficient in the unit's preset operating characteristics. This efficiency coefficient adjustment based on actual operating parameters makes the calculation of the lower limit of power consumption more closely reflect the true performance of the refrigeration unit, thereby improving the accuracy of logical judgments.

[0059] By using refrigerant pressure and evaporator temperature as operating parameters, this application enables a more refined assessment of the actual operating status of the refrigeration unit. This allows for more precise adjustments to the refrigeration unit's efficiency coefficient, resulting in a more accurate calculation of the lower limit of power consumption required to maintain the target compartment temperature. Consequently, when performing cross-comparisons between environmental data and vehicle operating status data in the scenario snapshot data package to ensure inherent logical consistency, inconsistencies caused by refrigeration unit performance abnormalities or data falsification can be more effectively identified, significantly improving the reliability and data integrity of food traceability.

[0060] like Figure 2 The illustrated food traceability system based on food safety includes: Data acquisition module 201 is used to synchronously collect environmental data and vehicle operating status data inside the compartment of a cold chain transport vehicle within a preset time interval, and encapsulate the collected environmental data and vehicle operating status data into a context snapshot data package. Digital signature module 202 is used to digitally sign the context snapshot data packet using a pre-stored private key; The data transmission module 203 is used to transmit the signed context snapshot data package to the food traceability distributed ledger through an encrypted channel, and to verify the digital signature of the context snapshot data package using the food traceability distributed ledger. According to the preset logical judgment rules, the inherent logical consistency between the environmental data and the vehicle operation status data in the context snapshot data package is cross-checked. The anomaly marking and recording module 204 is used to mark the context snapshot data packet as anomaly through the food traceability distributed ledger if there is a logical inconsistency in the cross-comparison judgment, and to record the context snapshot data packet after the anomaly marking.

[0061] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A food traceability method based on food safety, characterized in that, The method includes the following steps: On cold chain transport vehicles, environmental data inside the compartment and vehicle operating status data are collected synchronously within a preset time interval, and the collected environmental data and vehicle operating status data are encapsulated into a context snapshot data package. The scenario snapshot data packet is digitally signed using a pre-stored private key; The signed context snapshot data package is transmitted to the food traceability distributed ledger through an encrypted channel. The digital signature of the context snapshot data package is verified by the food traceability distributed ledger. According to the preset logical judgment rules, the inherent logical consistency between the environmental data and the vehicle operation status data in the context snapshot data package is cross-checked. If there is a logical inconsistency in the cross-comparison judgment, the situation snapshot data packet is marked as abnormal through the food traceability distributed ledger, and the situation snapshot data packet after the abnormality is recorded.

2. The food traceability method based on food safety according to claim 1, characterized in that, The step of cross-checking the inherent logical consistency between environmental data and vehicle operating status data in the context snapshot data packet according to preset logical judgment rules includes: Collect power consumption data of the refrigeration compressor; The collected power consumption data, along with the environmental data and the vehicle operating status data, are encapsulated into the scenario snapshot data package; When the cabin temperature reported in the scenario snapshot data packet remains within the target temperature range, it is determined whether the power consumption data contained in the scenario snapshot data packet is lower than the preset power consumption lower limit required to maintain the target temperature. If the power consumption data contained in the scenario snapshot data packet is lower than the preset power consumption lower limit required to maintain the target temperature, then the cross-comparison judgment is considered to have a logical inconsistency.

3. The food traceability method based on food safety according to claim 2, characterized in that, The process of determining the preset lower limit of power consumption required to maintain the target temperature includes: Acquire external ambient temperature and humidity data; Based on the external ambient temperature data, external ambient humidity data, and the preset operating characteristics of the refrigeration unit, the lower limit of power consumption required to maintain the target compartment temperature is determined.

4. A food traceability method based on food safety according to claim 3, characterized in that, The step of determining the lower limit of power consumption required to maintain the target compartment temperature based on the external ambient temperature data, external ambient humidity data, and the preset operating characteristics of the refrigeration unit includes: Monitor the operating status parameters of the refrigeration unit; Based on the deviation between the operating status parameters of the refrigeration unit and the reference operating parameters of the refrigeration unit under standard operating conditions, the efficiency coefficient in the preset operating characteristics of the refrigeration unit is adjusted to obtain the adjusted efficiency coefficient. Based on the external ambient temperature data, external ambient humidity data, and the adjusted efficiency coefficient, the lower limit of power consumption required to maintain the target carriage temperature is determined.

5. A food traceability method based on food safety according to claim 4, characterized in that, The step of adjusting the efficiency coefficient in the preset operating characteristics of the refrigeration unit based on the deviation between the operating status parameters of the refrigeration unit and the reference operating parameters of the refrigeration unit under standard operating conditions includes: The deviation between the operating status parameters of the refrigeration unit and the reference operating parameters of the refrigeration unit under standard operating conditions is obtained, and the deviation amount is obtained. Based on the deviation, an efficiency coefficient adjustment factor is determined through a preset nonlinear mapping relationship; The efficiency coefficient adjustment factor is applied to the efficiency coefficient in the preset operating characteristics of the refrigeration unit to obtain the adjusted efficiency coefficient.

6. A food traceability method based on food safety according to claim 5, characterized in that, The step of obtaining the deviation between the operating status parameters of the refrigeration unit and the reference operating parameters of the refrigeration unit under standard operating conditions, and obtaining the deviation amount, includes: Calculate the absolute difference or percentage deviation between each of the stated operating state parameters and each of the reference operating parameters to obtain multiple individual parameter deviations; The deviation amount is obtained by weighted summation or averaging of the multiple individual parameter deviations.

7. A food traceability method based on food safety according to claim 5, characterized in that, The step of determining the efficiency coefficient adjustment factor through a preset nonlinear mapping relationship includes: The efficiency coefficient adjustment factor is calculated based on the deviation by applying a preset nonlinear mathematical function.

8. A food traceability method based on food safety according to claim 4, characterized in that, The step of determining the lower limit of power consumption required to maintain the target carriage temperature based on the external ambient temperature data, external ambient humidity data, and the adjusted efficiency coefficient includes: Obtain the thermal insulation characteristics parameters of the compartment of cold chain transport vehicles; Obtain the thermal load characteristic parameters of the goods; Based on the external ambient temperature data, external ambient humidity data, the adjusted efficiency coefficient, the thermal insulation characteristic parameters of the carriage, and the heat load characteristic parameters of the cargo, the lower limit of power consumption required to maintain the target carriage temperature is calculated.

9. A food traceability method based on food safety according to claim 4, characterized in that, The operating parameters include refrigerant pressure and evaporator temperature.

10. A food traceability system based on food safety, used to execute a food traceability method based on food safety as described in any one of claims 1-9, characterized in that, The system includes: The data acquisition module is used to synchronously collect environmental data and vehicle operating status data inside the compartment of a cold chain transport vehicle within a preset time interval, and encapsulate the collected environmental data and the vehicle operating status data into a context snapshot data package. The digital signature module is used to digitally sign the scenario snapshot data packet using a pre-stored private key; The data transmission module is used to transmit the signed context snapshot data package to the food traceability distributed ledger through an encrypted channel, and to verify the digital signature of the context snapshot data package using the food traceability distributed ledger. According to the preset logical judgment rules, the module cross-compares the inherent logical consistency between the environmental data and the vehicle operation status data in the context snapshot data package. The anomaly marking and recording module is used to mark the context snapshot data packet as anomaly through the food traceability distributed ledger if there is a logical inconsistency in the cross-comparison judgment, and to record the context snapshot data packet after the anomaly marking.

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