Food traceability method and system based on food safety

Abstract

The application relates to the technical field of food safety traceability, in particular to a food traceability method and system based on food safety, which comprises the following steps: synchronously collecting environment data and vehicle running state data in a carriage within a preset time interval on a cold-chain transport vehicle, and encapsulating the collected environment data and the vehicle running state data into a context snapshot data packet; using a pre-stored private key to digitally sign the context snapshot data packet; transmitting the signed context snapshot data packet to a food traceability distributed ledger through an encryption channel, verifying, and according to a preset logic judgment rule, cross-comparing the internal logic consistency between the environment data and the vehicle running state data in the context snapshot data packet; the application can verify the authenticity of data from the dual dimensions of the physical world and the digital world through the cross comparison of the environment data and the vehicle running state data and in combination with the digital signature technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of food safety traceability, and particularly relates to a food traceability method and system based on food safety. BACKGROUND

[0002] In the modern food industry, in order to protect the food safety of consumers, it is crucial to establish a full-process traceability system from the source to the table. The food traceability method based on distributed ledger technology emerges as the times require, and its core lies in using the decentralized, data tamper-proof and open and transparent characteristics of such technology to provide a credible data recording platform for each link in the food supply chain. However, even if the data on the chain itself cannot be changed, if the source link where the data interacts between the physical world and the digital world is tampered with before being recorded to the distributed ledger, the distributed ledger will record false information, which fundamentally shakes the credibility of the entire traceability system. Especially in the link of cold chain logistics and the like which has strict requirements on environmental parameters, it is a severe challenge to ensure that the original data collected by the sensor is true and reliable, and is not disturbed or falsified in the transmission process.

[0003] For example, in the process of cold chain transportation, the temperature and humidity sensors inside the refrigerated truck will periodically collect data, together with the real-time geographic location information of the vehicle, and upload and record them in a new data block. However, in actual business operations, there are malicious parties (such as transport drivers) who will interfere with the sensor through physical means, or tamper with the data in the local network link from the sensor to the distributed ledger node, so as to write the falsified normal state information (such as temperature, humidity) into the distributed ledger to cover up the actual abnormal situation (such as refrigeration equipment failure). The untrustworthy problem of the data source in the off-chain is a key technical bottleneck that must be solved when the distributed ledger traceability application moves from an ideal model to a real deployment.

[0004] In the prior art, although the security of the traceability system depends on the tamper-proof nature of the on-chain data, it cannot ensure the authenticity of the data at the source. When the refrigeration equipment of the cold chain transport vehicle fails during transportation, causing the temperature inside the vehicle to abnormally rise, the driver may manually edit and modify the temperature record during the failure period on the local data terminal in order to avoid bearing high compensation, and then upload the falsified "perfect" data to the distributed ledger. Since the distributed ledger itself is only responsible for faithfully recording the submitted data and ensuring that it is not tampered with later, it cannot distinguish whether the submitted data is true at the source. Therefore, this falsified and seemingly credible data chain not only fails to reveal the truth, but also becomes a "accomplice" to cover up the problem, which fundamentally shakes the credibility of the entire traceability system.

[0005] In view of the above problems, the prior art needs to be improved. SUMMARY

[0006] The purpose of the present application is to solve the problems existing in the prior art and propose a food traceability method and system based on food safety.

[0007] In a first aspect, the present application provides a food traceability method based on food safety, comprising the following steps:

[0008] On a cold chain transport vehicle, synchronously collecting environmental data and vehicle operating state data in a carriage within a preset time interval, and packaging the collected environmental data and the vehicle operating state data into a context snapshot data packet;

[0009] digitally signing the context snapshot data packet using a pre-stored private key;

[0010] transmitting the signed context snapshot data packet to a food traceability distributed ledger through an encrypted channel, verifying the digital signature of the context snapshot data packet using the food traceability distributed ledger, and cross-comparing the internal logical consistency between the environmental data and the vehicle operating state data in the context snapshot data packet according to a preset logical judgment rule;

[0011] If the cross-comparison judgment is not logically consistent, the context snapshot data packet is marked as abnormal through the food traceability distributed ledger, and the context snapshot data packet after abnormal marking is recorded.

[0012] In a second aspect, a food traceability system based on food safety is provided, comprising:

[0013] A data acquisition module is configured to synchronously collect environmental data and vehicle operating state data in a carriage within a preset time interval on a cold chain transport vehicle, and package the collected environmental data and the vehicle operating state data into a context snapshot data packet;

[0014] A digital signature module is configured to digitally sign the context snapshot data packet using a pre-stored private key;

[0015] A data transmission module is configured to transmit the signed context snapshot data packet to a food traceability distributed ledger through an encrypted channel, verify the digital signature of the context snapshot data packet using the food traceability distributed ledger, and cross-compare the internal logical consistency between the environmental data and the vehicle operating state data in the context snapshot data packet according to a preset logical judgment rule;

[0016] An abnormality marking and recording module is configured to mark the context snapshot data packet as abnormal and record the context snapshot data packet marked as abnormal in the food traceability distributed ledger if the cross comparison determines that there is a logical inconsistency.

[0017] Compared with the prior art, the present application has the following beneficial effects:

[0018] By introducing cross comparison of environmental data and vehicle operating state data and combining with digital signature technology, the present application can verify the authenticity of data from the dual dimensions of the physical world and the digital world. This method can effectively identify and expose behaviors such as drivers tampering with temperature data to cover up refrigeration failures, and avoid false information being recorded into the distributed ledger, thereby fundamentally improving the credibility of the entire food traceability system. Compared with the prior art, the present application not only ensures the non-tamperability 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, effectively making up for the shortcomings of the existing traceability system in terms of data source authenticity. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A method flowchart of the present application.

[0020] Figure 2 A system structure schematic diagram of the present application.

[0021] In the figure: 201, data acquisition module; 202, digital signature module; 203, data transmission module; 204, abnormality marking and recording module. DETAILED DESCRIPTION

[0022] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application.

[0023] The terms "first", "second" are only used for description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise specifically limited.

[0024] 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.

[0025] 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.

[0026] like Figure 1 The method shown is a food traceability method based on food safety, which includes the following steps:

[0027] 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.

[0028] It should be noted that first, on the cold chain transport vehicle, it is necessary to synchronously collect the environmental data in the vehicle compartment and the vehicle running state data. The environmental data can be achieved by deploying temperature sensors and humidity sensors inside the vehicle compartment, which can automatically collect data periodically (e.g. every 5 or 10 minutes). The vehicle running state data can be obtained by the vehicle GPS module to obtain the geographical position and speed information, by the vehicle CAN bus interface to obtain the engine speed, fuel consumption and other data, and by the controller interface of the refrigeration unit to obtain the refrigerant pressure, evaporator temperature and other refrigeration system running parameters. After these data are collected, they will be packaged into a context snapshot data packet. For example, a context snapshot data packet can contain timestamp, vehicle compartment temperature, vehicle compartment humidity, vehicle latitude and longitude, vehicle speed, and refrigeration unit running mode and other information.

[0029] S102, digitally signing the context snapshot data packet using a pre-stored private key;

[0030] It should be noted that the digital signature in the above step can be achieved by various encryption algorithms, such as RSA algorithm or elliptic curve digital signature algorithm (ECDSA). A private key is pre-stored in the data collection device or vehicle terminal, and when the context snapshot data packet is generated, the 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, and any tampering with the contents of the data packet will result in a failed signature verification.

[0031] S103, transmitting the signed context snapshot data packet to the food traceability distributed ledger through an encrypted channel, verifying the digital signature of the context snapshot data packet using the food traceability distributed ledger, and cross-comparing the internal logical consistency between the environmental data and the vehicle running state data in the context snapshot data packet according to the preset logical judgment rule;

[0032] It should be noted that the signed context snapshot data packet is transmitted to the food traceability distributed ledger through an encrypted channel. The establishment of the encrypted channel can be realized through technologies such as transport layer security protocol (TLS / SSL) or virtual private network (VPN), etc., to ensure the confidentiality and integrity of the data in the transmission process. Once the data packet arrives at the food traceability distributed ledger, the distributed ledger will verify the digital signature of the context snapshot data packet using the pre-stored public key. If the digital signature verification is passed, it means that the data packet has not been tampered with in the transmission process, and indeed comes from a legitimate collection device. According to the preset logical judgment rule, the internal logical consistency between the environmental data and the vehicle operating state data in the context snapshot data packet is cross-compared. For example, if the context snapshot data packet reports that the temperature in the carriage is normal, but the vehicle operating state data shows that the refrigeration unit has been in the off state for a long time or the power consumption is abnormally low, it may indicate that the data is logically inconsistent. The preset logical judgment rule can be a set of rules based on physical laws and empirical knowledge, such as "when the temperature in the carriage 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 the temperature".

[0033] S104, if the cross-comparison judgment exists logical inconsistency, the context snapshot data packet is marked as abnormal through the food traceability distributed ledger, and the context snapshot data packet after the abnormal marking is recorded.

[0034] It should be noted that this step means that even if the digital signature verification is passed, if the data content itself has logical contradictions, the data packet will still be identified as abnormal by the system. The data packet after the abnormal marking will be recorded on the distributed ledger, but will be accompanied by an abnormal identification, so that potential problem data can be identified during subsequent audit and traceability. This mechanism effectively prevents the behavior of covering up the actual abnormal situation by forging data.

[0035] The overall working principle of the present application is to build 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 non-tamperability of on-chain data, but cannot effectively solve the problem of tampering with the data source. The present application ensures the integrity and authenticity of the data in the transmission process by digitally signing the context snapshot data package before chaining the data and verifying it using a distributed ledger. More importantly, the present application introduces the internal logical consistency judgment between environmental data and vehicle operating state data. For example, when the vehicle cabin temperature data shows normal, but the vehicle operating state 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 determine that the data is abnormal. This cross comparison mechanism makes it difficult for malicious parties to fake all associated and physically logical data even if they have faked a single normal-looking data (such as temperature data) through technical means. Once a logical inconsistency is found, even if the digital signature of the data package is valid, the data package will still be marked as abnormal and recorded on the distributed ledger, providing key abnormal information for subsequent auditing and problem tracing. Thus, the present application fundamentally improves the reliability of food traceability data, effectively solves the challenge of untrustworthy data sources in cold chain logistics, and ensures the credibility of the entire traceability system.

[0036] Compared with the prior art, the present application has made significant technical progress in solving the problem of untrustworthy data sources in food traceability. The prior art mainly focuses on the non-tamperability of distributed ledger on-chain data, but lacks sufficient protection of data authenticity before it is collected and transmitted to the chain. For example, in cold chain transportation, even if the temperature data is recorded on the blockchain, if the data has been maliciously tampered with at the collection end, the blockchain record is still false information. The core innovation of the present application is not only to use digital signatures to ensure the integrity and authenticity of the data in the transmission process, but also to introduce cross comparison of the internal logical consistency between environmental data and vehicle operating state data in the context snapshot data package.

[0037] As an embodiment of the present application, according to the preset logical judgment rule, the step of cross comparing the internal logical consistency between the environmental data and the vehicle operating state data in the context snapshot data package includes:

[0038] Collecting power consumption data of the refrigeration compressor;

[0039] It should be noted that the above step refers to installing a power consumption sensor on the cold chain transportation vehicle or obtaining the power consumption data of the refrigeration unit from its control unit. These power consumption data reflect the actual energy consumed by the refrigeration unit to maintain the target temperature in the vehicle cabin.

[0040] The collected power consumption data is encapsulated into a context snapshot data package together with environmental data and vehicle operating state data;

[0041] It should be noted that this step means that in generating the context snapshot data package, in addition to the original car interior environmental data (such as temperature, humidity) and vehicle operating state data (such as speed, position, engine state), the real-time power consumption data of the refrigeration compressor is also included. Thus, the context snapshot data package carries more comprehensive cold chain operation context information.

[0042] When the reported car temperature in the context snapshot data package remains within the target temperature range, it is determined whether the power consumption data contained in the context snapshot data package is lower than the preset lower limit of power consumption required to maintain the target temperature;

[0043] It should be noted that the preset lower limit of power consumption in the above step is the minimum power consumption value calculated in advance or set by experience according to the efficiency of the refrigeration unit, the heat insulation performance of the car, the external environmental conditions and the target temperature, etc., to ensure that the refrigeration unit must consume the minimum energy under normal operating conditions.

[0044] If the power consumption data contained in the context snapshot data package is lower than the preset lower limit of power consumption required to maintain the target temperature, it is considered that there is a logical inconsistency in the cross-comparison judgment.

[0045] It should be noted that the above step means that although the reported temperature may be within the normal range, the actual power consumption of the refrigeration unit is not sufficient to maintain the temperature, thus revealing a potential abnormal situation, such as refrigeration unit failure, low efficiency or human intervention (such as turning off the refrigeration unit to save fuel).

[0046] The scheme of the present application deepens the data logic consistency judgment in the cold chain transportation process by introducing the power consumption data of the refrigeration compressor. In the traditional judgment, only by comparing the temperature of the vehicle compartment with the running state of the vehicle, it may not be able to effectively identify the abnormal situation that the refrigeration unit is actually not working normally, but the temperature of the vehicle compartment is temporarily not significantly increased. For example, when the external environment temperature is low or the vehicle is temporarily stopped, even if the refrigeration unit stops running, the temperature of the vehicle compartment may remain in the target range for a period of time. At this time, if only according to the temperature data, the system may misjudge as normal. However, by collecting the power consumption data of the refrigeration compressor and including it in the context snapshot data package, the present application can establish the internal correlation between the temperature of the vehicle compartment and the actual work of the refrigeration unit. Specifically, when the temperature of the vehicle compartment is reported to remain in the target temperature range, the system will further check the power consumption data of the refrigeration unit. If the power consumption data is lower than the preset lower limit of the power consumption required to maintain the target temperature, it indicates that the refrigeration unit has not put in enough energy to maintain the temperature, which is logically contradictory to the reported normal temperature. Therefore, even if the temperature data itself does not show abnormalities, the system can accurately identify potential cold chain abnormalities or data falsification behaviors based on the inconsistency between the power consumption data and the temperature data. This cross verification based on the internal logical relationship between the physical quantity (power consumption) and the environmental quantity (temperature) significantly improves the authenticity and reliability of the traceability data.

[0047] In some preferred embodiments, the following is described by a specific example. Assume that a cold chain transportation vehicle is transporting frozen food in a high temperature environment in summer, and the target vehicle compartment temperature is set to -18°C. At the collection time of a certain context snapshot data package, the temperature sensor in the vehicle compartment reports a temperature of -17.5°C, which is within the target temperature range, and the vehicle is driving normally. If only according to the above environmental data and vehicle running state data, the context snapshot data package can be judged as normal. However, the actual situation is that the driver temporarily turned off the refrigeration unit during the vehicle driving process in order to save fuel. Due to the good heat insulation performance of the vehicle compartment and the short off time, the temperature of the vehicle compartment has not yet significantly increased. At this time, the scheme of the present application discovers that while the reported temperature is -17.5°C, the power consumption data of the refrigeration compressor is 0 watts, which is far below the preset lower limit of the power consumption required to maintain -18°C (for example, at least 500 watts of power consumption is required to maintain -18°C under the current external environment temperature). According to the logical judgment rule of the present application, the system will immediately identify the logical inconsistency between the power consumption and the temperature, and mark the context snapshot data package as abnormal. Therefore, even if the temperature data itself does not show abnormalities, the scheme of the present application can timely and accurately reveal the potential irregular operation in the cold chain transportation process, effectively avoiding the occurrence of food safety risks.

[0048] As an embodiment of the present application, the determination process of the preset lower limit of power consumption required to maintain the target temperature comprises:

[0049] Obtaining external environment temperature data and humidity data;

[0050] It should be noted that the above step can be understood as real-time collection by an environmental sensor installed outside the cold chain transport vehicle, or obtaining meteorological data of the current geographic position by interacting with an external meteorological service platform. These data are dynamically changing and can reflect the actual thermal load of the environment where the vehicle is located.

[0051] According to the external environment temperature data, the external environment humidity data, and the preset operating characteristics of the refrigeration unit, a lower limit value of power consumption required to maintain the target temperature is determined.

[0052] It should be noted that the above step refers to a mathematical model or lookup table established using the performance parameters of the refrigeration unit under different operating conditions (for example, refrigeration capacity, energy efficiency ratio, power consumption and temperature difference relationship, etc.). The preset operating characteristics of the refrigeration unit can be a performance curve or parameter set obtained after testing a specific model of refrigeration unit under laboratory conditions, which reflects the efficiency and power consumption performance of the refrigeration unit under ideal or standard operating conditions. By taking the real-time obtained external environment temperature data 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 in the current external environment can be calculated, i.e. the lower limit value of power consumption. For example, when the external temperature is higher or the humidity is larger, the lower limit value of power consumption required to maintain the same temperature in the vehicle will be correspondingly increased.

[0053] The scheme of the present application introduces external environment temperature data and humidity data, and combines the preset operating characteristics of the refrigeration unit, so that the preset lower limit of power consumption required to maintain the target temperature can be dynamically adjusted according to the actual external environment conditions. This solves the problem that the preset lower limit of power consumption in the traditional scheme is fixed and cannot adapt to changes in the external environment. Specifically, when the external environment temperature rises or the humidity increases, the thermal load of the vehicle will increase accordingly, and the refrigeration unit will consume more energy to maintain the target temperature. By dynamically calculating the lower limit value of power consumption, the actual operating requirements of the refrigeration unit can be more accurately reflected, thereby avoiding misjudgment caused by changes in the external environment. Thus, when the actual power consumption is lower than this dynamically adjusted lower limit, it can more reliably indicate that the refrigeration system may have abnormalities, such as refrigerant leakage, compressor efficiency decline, or human intervention, thereby improving the accuracy of traceability judgment.

[0054] In some preferred embodiments, it is assumed that the cold chain transport vehicle is transporting food from a city in the south to a city in the north in summer. During the transportation, the vehicle will experience different external environmental temperatures and humidities. For example, in a 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. Combined with the pre-stored preset operating characteristics of the refrigeration unit (for example, the unit heat load power consumption required to maintain a-18°C vehicle compartment temperature at an external temperature of 35°C and a humidity of 80%), the system can calculate that the power consumption lower limit value required to maintain a-18°C vehicle compartment temperature under the current working condition is X kilowatts. When the vehicle travels to a dry area in the north, the external environmental temperature may decrease to 25°C and the humidity is 40%. The system will obtain the new external environmental data again, and according to the preset operating characteristics of the refrigeration unit, recalculate the power consumption lower limit value required to maintain a-18°C vehicle compartment temperature at this time as Y kilowatts (YX). Through this dynamic adjustment, even if the vehicle compartment temperature is maintained within the target range, if the actual power consumption is lower than the power consumption lower limit value calculated under the current external environment, the system can accurately identify that the refrigeration system may have abnormalities, such as the refrigeration unit not running at full load or having efficiency problems, so as to timely mark the abnormalities and ensure the authenticity of the traceability data.

[0055] As an embodiment of the present application, the step of determining the power consumption lower limit value required to maintain the target vehicle compartment temperature according to the external environmental temperature data, the external environmental humidity data, and the preset operating characteristics of the refrigeration unit comprises:

[0056] monitoring the operating state parameters of the refrigeration unit;

[0057] It should be noted that the above step refers to continuously collecting various key performance indicators of the refrigeration unit during actual operation. These parameters can include but are not limited to refrigerant pressure (such as high-pressure side pressure and low-pressure side pressure), evaporator temperature, condenser temperature, compressor current, voltage, fan speed, and defrosting cycle state, etc. The purpose is to obtain the real performance of the refrigeration unit under the current working condition in real time.

[0058] According to the deviation of the operating state parameters of the refrigeration unit from the reference operating parameters of the refrigeration unit under standard working conditions, adjusting the efficiency coefficient in the preset operating characteristics of the refrigeration unit to obtain an adjusted efficiency coefficient;

[0059] It should be noted that the above step can be understood as follows: first, compare the real-time monitored operating state parameters with the reference operating parameters of the refrigeration unit of this model under ideal or controlled standard working conditions. This comparison can quantify the gap or deviation between the actual operating efficiency of the refrigeration unit and the design efficiency. For example, when there is a large deviation between the actual operating parameters and the reference parameters, it indicates that the actual efficiency of the refrigeration unit may be lower than its preset value. Subsequently, based on the degree of deviation, the efficiency coefficient representing the energy conversion efficiency of the preset operating characteristic model of the refrigeration unit is dynamically adjusted. For example, if the actual performance of the refrigeration unit decreases, the efficiency coefficient will be adjusted accordingly to reflect its current energy consumption level more accurately.

[0060] According to the external environment temperature data, the external environment humidity data and the adjusted efficiency coefficient, a lower limit value of power consumption required to maintain the target temperature in the vehicle cabin is determined.

[0061] It should be noted that the above step refers to the step of, after obtaining the adjusted efficiency coefficient reflecting the current real efficiency of the refrigeration unit, inputting it into the thermodynamic model or the preset calculation formula together with the temperature and humidity data of the external environment, so as to calculate the minimum power consumption required to maintain the target temperature in the vehicle cabin under the current external environment. The calculation process will comprehensively consider the heat load, the thermal insulation performance and the actual operating efficiency of the refrigeration unit.

[0062] The scheme of the present application quantifies the deviation of the actual operating efficiency of the refrigeration unit by introducing real-time monitoring of the operating state parameters of the refrigeration unit and comparing them with the reference parameters under standard working conditions. It is precisely because this degree of deviation is used to dynamically adjust the efficiency coefficient in the preset operating characteristics of the refrigeration unit that the determined lower limit value of power consumption can more accurately reflect the energy consumption demand of the refrigeration unit under the current actual working conditions. Thus, the method can overcome the limitations brought by relying solely on static preset characteristics, ensure that the calculation of the lower limit value of power consumption is closer to the real situation, and thus provide a more reliable benchmark for the subsequent logical consistency judgment of the context snapshot data packet.

[0063] As an embodiment of the present application, the step of 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 comprises:

[0064] Obtain 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 a deviation amount;

[0065] It should be noted that the above step refers to quantifying the degree of performance deviation of the refrigeration unit by comparing the current actual operating data (such as refrigerant pressure, evaporator temperature, etc.) of the refrigeration unit with the performance benchmark data of the unit under ideal or standard test conditions.

[0066] According to the deviation, an efficiency coefficient adjustment factor is determined through a preset nonlinear mapping relationship;

[0067] It should be noted that the above step can be understood as using a pre-established nonlinear model or function that can reflect the complex performance characteristics of the refrigeration unit to convert the above deviation into a factor for correcting the efficiency coefficient. The nonlinear mapping relationship can be constructed based on a large amount of experimental data, simulation models or expert experience, and its purpose is to more accurately capture the nonlinear law of efficiency change of the refrigeration unit under different deviation degrees, for example, when the deviation is small, the efficiency may show a slow downward trend, and when the deviation reaches a certain threshold, the efficiency may decrease sharply.

[0068] The efficiency coefficient adjustment factor is applied to the efficiency coefficient in the preset operating characteristics of the refrigeration unit to obtain an adjusted efficiency coefficient.

[0069] It should be noted that the above step specifically refers to applying the adjustment factor obtained through the nonlinear mapping relationship to the initial or nominal efficiency coefficient of the refrigeration unit in a multiplication or addition manner, thereby obtaining a corrected efficiency coefficient that is more consistent with the current actual operating state. The adjusted efficiency coefficient will be used for subsequent calculation of the lower limit of power consumption to improve the accuracy of the calculation.

[0070] The scheme of the present application can more accurately reflect the actual operating efficiency of the refrigeration unit under non-standard operating conditions by introducing a nonlinear mapping relationship to determine the efficiency coefficient adjustment factor. Traditional linear adjustment may not be able to capture the complex changes in the performance of the refrigeration unit under different deviation degrees, resulting in deviations in the estimation of the efficiency coefficient. By using nonlinear mapping, such as a function relationship based on empirical data or physical models, the adjustment of the efficiency coefficient can be made more closely to the real operating state of the refrigeration unit, thereby ensuring the accuracy of the subsequent calculation of the lower limit of power consumption required to maintain the target car temperature. As a result, when the car temperature is maintained within the target range, the logical consistency of the power consumption data will be more reliable, effectively avoiding false positives or false negatives caused by inaccurate estimation of the efficiency coefficient.

[0071] By the technical solution, the accuracy of the refrigeration unit efficiency coefficient adjustment can be improved, and the lower limit value of the power consumption required to maintain the target vehicle compartment temperature can be calculated more accurately. This helps to more reliably identify abnormal behaviors that may exist in the cold chain transportation process, such as concealing insufficient refrigeration by falsely reporting the temperature, thereby improving the accuracy and reliability of the food traceability distributed ledger in logically consistent judgment of the situational snapshot data packet, and ultimately enhancing the credibility and security of the entire food traceability system.

[0072] As an embodiment of the present application, the step of obtaining the deviation between the operating state parameters of the refrigeration unit and the reference operating parameters of the refrigeration unit under standard working conditions includes:

[0073] Calculate the absolute difference or percentage deviation between each operating state parameter and each reference operating parameter to obtain a plurality of single parameter deviations.

[0074] According to the plurality of single parameter deviations, the plurality of single parameter deviations are weighted and summed or averaged to obtain the deviation.

[0075] Wherein, the operating state parameters can include but are not limited to refrigerant pressure, evaporator temperature, condenser temperature, compressor speed, current, voltage, etc., and the reference operating parameters refer to the theoretical or empirical values of these parameters when the refrigeration unit is in standard, ideal working conditions. When calculating the single parameter deviation, the absolute difference value, i.e. the absolute value of the operating state parameter minus the reference operating parameter, or the percentage deviation, i.e. (operating state parameter - reference operating parameter) / reference operating parameter * 100%, can be used. These single parameter deviations reflect the deviation of the refrigeration unit from the ideal state at a specific operating time. Further, in order to comprehensively evaluate the overall operating condition of the refrigeration unit, these single parameter deviations can be integrated. For example, the plurality of single parameter deviations can be weighted and summed, i.e. each parameter deviation is assigned a weight, and then the weighted deviation values are added, wherein the weight can be set according to the importance of the parameter to the performance of the refrigeration unit. Or, the plurality of single parameter deviations can be simply arithmetically averaged to obtain an overall deviation. The deviation aims to quantify the gap between the actual operating efficiency and the theoretical efficiency of the refrigeration unit.

[0076] The scheme of the present application can quantify the deviation degree of each key operating index by calculating the absolute difference or percentage deviation between each operating state parameter and the reference operating parameter. Thus, by weighted sum or average of these single parameter deviations, the deviation degree of the overall operating state of the refrigeration unit from the standard working condition can be comprehensively and comprehensively evaluated, so as to obtain a deviation quantity that can accurately reflect the actual efficiency of the refrigeration unit. The deviation quantity provides an accurate input for subsequent determination of the efficiency coefficient adjustment factor through the 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, and thus the preset lower limit of power consumption required to maintain the target car temperature can more accurately reflect the actual situation.

[0077] As an embodiment of the present application, the step of determining the efficiency coefficient adjustment factor through the preset nonlinear mapping relationship comprises:

[0078] The efficiency coefficient adjustment factor is calculated according to the deviation quantity by applying the preset nonlinear mathematical function.

[0079] The preset nonlinear mapping relationship refers to a nonlinear correspondence, and its purpose is to convert the deviation quantity between the operating state parameters of the refrigeration unit and the reference operating parameters under the standard working condition into an adjustment factor that can accurately reflect the actual efficiency change of the refrigeration unit. Specifically, this nonlinear mapping relationship can be realized by a preset nonlinear mathematical function. The preset nonlinear mathematical function can be modeled and optimized according to the actual operating characteristics, historical data and expert experience of the refrigeration unit, for example, a polynomial function, an exponential function, a logarithmic function, a Sigmoid function or a neural network model, etc. can be used to better fit the complex nonlinear relationship between the deviation quantity and the efficiency coefficient adjustment factor. The deviation quantity is a quantitative index that measures the difference between the actual operating state of the refrigeration unit and the ideal standard state, and its numerical value 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 according to the input deviation quantity, thereby providing a more accurate basis for subsequent determination of the lower limit of power consumption.

[0080] The scheme of the present application determines the efficiency coefficient adjustment factor by introducing a preset nonlinear mathematical function, the working principle of which is that there is often a complex nonlinear relationship between the efficiency change of the refrigeration unit and the degree of deviation of the operating parameters. For example, in certain operating intervals, a small parameter deviation can lead to a large efficiency drop, while in other intervals, even if the parameter deviation is large, the efficiency change may be relatively flat. If a simple linear mapping relationship is used, it may not be able to accurately capture this complex nonlinear characteristic, resulting in an inaccurate efficiency coefficient adjustment factor. By applying a preset nonlinear mathematical function, this nonlinear relationship can be more finely described, so that the calculated efficiency coefficient adjustment factor can more truly reflect the actual efficiency of the refrigeration unit under different degrees of deviation, thereby improving the accuracy of the calculation of the lower limit of power consumption.

[0081] As an embodiment of the present application, the step of determining the lower limit of power consumption required to maintain the target vehicle cabin temperature according to the external environment temperature data, the external environment humidity data, and the adjusted efficiency coefficient comprises:

[0082] Obtaining a vehicle cabin thermal insulation characteristic parameter of the cold chain transport vehicle;

[0083] Obtaining a cargo heat load characteristic parameter;

[0084] According to the external environment temperature data, the external environment humidity data, the adjusted efficiency coefficient, the vehicle cabin thermal insulation characteristic parameter, and the cargo heat load characteristic parameter, the lower limit of power consumption required to maintain the target vehicle cabin temperature is calculated.

[0085] Specifically, the vehicle cabin thermal insulation characteristic parameter of the cold chain transport vehicle refers to a series of physical quantities used to describe the thermal insulation performance of the cold chain transport vehicle cabin, such as the heat transfer coefficient (K value) of the cabin wall, door, floor and roof, the material thermal conductivity, the thickness, and the surface area of the cabin. These parameters jointly determine the efficiency and rate of heat exchange between the inside of the cabin and the external environment through the envelope structure. The cargo heat load characteristic parameter refers to the characteristic data related to the transported goods that will affect the heat balance inside the cabin, such as the type, quantity, initial temperature, specific heat capacity, latent heat of the goods, and the respiration heat of fresh produce. These parameters are used to quantify the heat generated by the goods itself or the heat absorbed / released during temperature change.

[0086] The scheme of the present application obtains the compartment thermal insulation characteristic parameters and the cargo heat load characteristic parameters of the cold chain transport vehicle, and incorporates these parameters with the external environment temperature data, the external environment humidity data and the adjusted efficiency coefficient into the calculation of the power consumption lower limit value. Specifically, the compartment thermal insulation characteristic parameters are used to quantify the heat transfer efficiency between the compartment enclosure structure and the external environment, so as to more accurately estimate the heat load of the compartment due to the external temperature difference. At the same time, the cargo heat load characteristic parameters are used to evaluate the heat generated or absorbed by the transported goods, such as the respiration heat of fresh products or the sensible heat load of frozen food. Thus, the calculated power consumption lower limit value not only considers the influence of the external environment on the refrigeration demand, but also more comprehensively reflects the contribution of the compartment internal structure and the loaded goods to the refrigeration demand. This comprehensive calculation method makes the power consumption lower limit value more truly and accurately reflect the actual minimum energy consumption required to maintain the target compartment temperature, thereby effectively making up for the shortcomings of calculation only relying on the external environment and the efficiency of the refrigeration unit, and significantly improving the accuracy and reliability of the identification of potential abnormal behaviors in the cold chain transport process.

[0087] As an embodiment of the present application, the operating state parameters include refrigerant pressure and evaporator temperature.

[0088] Among them, the refrigerant pressure refers to the pressure of the refrigerant at different components (such as compressor outlet, condenser, expansion valve inlet, evaporator outlet) in the refrigeration cycle, which directly reflects the circulation state of the refrigerant and the load condition of the refrigeration unit. The evaporator temperature refers to the temperature inside the evaporator in the refrigeration unit, which directly affects the cooling effect and heat exchange efficiency inside the compartment.

[0089] The present application can obtain the key operating indicators of the refrigeration unit in real time by monitoring the refrigerant pressure and the evaporator temperature. The numerical changes of these parameters can reflect the actual working efficiency of the refrigeration unit and whether there is an abnormality. For example, when the refrigerant pressure or the evaporator temperature deviates from the normal range, it may mean refrigerant leakage, system blockage, compressor failure or refrigeration capacity reduction, etc. By comparing these actual operating state parameters with the reference operating parameters of the refrigeration unit under standard working conditions, the performance deviation degree of the refrigeration unit can be accurately evaluated, and then used to adjust the efficiency coefficient in the preset operating characteristics of the refrigeration unit. This efficiency coefficient adjustment based on actual operating parameters makes the calculation of the power consumption lower limit value more close to the real performance of the refrigeration unit, thereby improving the accuracy of logical judgment.

[0090] 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.

[0091] like Figure 2 The illustrated food traceability system based on food safety includes:

[0092] 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.

[0093] Digital signature module 202 is used to digitally sign the context snapshot data packet using a pre-stored private key;

[0094] 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.

[0095] 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.

[0096] 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 by, The method comprises the following steps: Synchronously collecting environmental data and vehicle running state data in the vehicle compartment within a preset time interval, and encapsulating the collected environmental data and vehicle running state data into a context snapshot data packet on a cold chain transport vehicle; Digitally signing the context snapshot data packet using a pre-stored private key; Transmitting the signed context snapshot data packet to a food traceability distributed ledger through an encrypted channel, verifying the digital signature of the context snapshot data packet using the food traceability distributed ledger, and cross-comparing the internal logical consistency between the environmental data and the vehicle running state data in the context snapshot data packet according to a preset logical judgment rule; If the cross-comparison judgment has logical inconsistency, marking the context snapshot data packet as abnormal through the food traceability distributed ledger, and recording the context snapshot data packet after the abnormal marking; The step of cross-comparing the internal logical consistency between the environmental data and the vehicle running state data in the context snapshot data packet according to a preset logical judgment rule comprises: Collecting power consumption data of the refrigeration compressor; Encapsulating the collected power consumption data into the context snapshot data packet together with the environmental data and the vehicle running state data; When the reported temperature of the vehicle compartment in the context snapshot data packet is maintained within a target temperature range, determining whether the power consumption data contained in the context snapshot data packet is lower than a preset lower limit of power consumption required to maintain the target temperature; If the power consumption data contained in the context snapshot data packet is lower than the preset lower limit of power consumption required to maintain the target temperature, it is considered that the cross-comparison judgment has logical inconsistency; The determination process of the preset lower limit of power consumption required to maintain the target temperature comprises: Obtaining external environmental temperature data and humidity data; According to the external environmental temperature data, external environmental humidity data, and preset running characteristics of the refrigeration unit, determining the lower limit of power consumption required to maintain the target vehicle compartment temperature; The step of determining the lower limit of power consumption required to maintain the target vehicle compartment temperature according to the external environmental temperature data, external environmental humidity data, and preset running characteristics of the refrigeration unit comprises: Monitoring the running state parameters of the refrigeration unit; According to the deviation of the running state parameters of the refrigeration unit from the reference running parameters of the refrigeration unit under standard working conditions, adjusting the efficiency coefficient in the preset running characteristics of the refrigeration unit to obtain an adjusted efficiency coefficient; According to the external environmental temperature data, external environmental humidity data, and the adjusted efficiency coefficient, determining the lower limit of power consumption required to maintain the target vehicle compartment temperature; The step of determining the lower limit of power consumption required to maintain the target vehicle compartment temperature according to the external environmental temperature data, external environmental humidity data, and the adjusted efficiency coefficient comprises: Obtaining the thermal insulation characteristic parameters of the vehicle compartment of the cold chain transport vehicle; Obtaining the thermal load characteristic parameters of the goods; According to the external environmental temperature data, external environmental humidity data, the adjusted efficiency coefficient, the thermal insulation characteristic parameters of the vehicle compartment, and the thermal load characteristic parameters of the goods, calculating the lower limit of power consumption required to maintain the target vehicle compartment temperature.

2. A food traceability method based on food safety according to claim 1, characterized in that, The step of adjusting the efficiency coefficient in the preset operating characteristic of the refrigerating unit according to the deviation of the operating state parameter of the refrigerating unit from the reference operating parameter of the refrigerating unit under the standard working condition comprises: obtaining a deviation between the operating state parameter of the refrigerating unit and the reference operating parameter of the refrigerating unit under the standard working condition, to obtain a deviation amount; determining an efficiency coefficient adjustment factor through a preset nonlinear mapping relationship according to the deviation amount; applying the efficiency coefficient adjustment factor to the efficiency coefficient in the preset operating characteristic of the refrigerating unit to obtain an adjusted efficiency coefficient.

3. A food traceability method based on food safety according to claim 2, characterized in that, The step of obtaining a deviation between the operating state parameter of the refrigerating unit and the reference operating parameter of the refrigerating unit under the standard working condition, to obtain a deviation amount, comprises: calculating an absolute difference or a percentage deviation between each operating state parameter and each reference operating parameter to obtain a plurality of single parameter deviations; obtaining the deviation amount by weighted sum or average of the plurality of single parameter deviations.

4. The food traceability method based on food safety according to claim 2, characterized in that, The step of determining an efficiency coefficient adjustment factor through a preset nonlinear mapping relationship comprises: calculating the efficiency coefficient adjustment factor according to the deviation amount by applying a preset nonlinear mathematical function.

5. The food traceability method based on food safety according to claim 1, characterized in that, The operating state parameter comprises refrigerant pressure and evaporator temperature.

6. A food safety-based food traceability system for performing a food safety-based food traceability method according to any one of claims 1 to 5, characterized in that, The system comprises: a data acquisition module configured to synchronously acquire environmental data and vehicle operating state data in a carriage and within a preset time interval on a cold-chain transport vehicle, and encapsulate the acquired environmental data and vehicle operating state data into a context snapshot data packet; a digital signature module configured to digitally sign the context snapshot data packet using a pre-stored private key; a data transmission module configured to transmit the signed context snapshot data packet to a food traceability distributed ledger through an encrypted channel, verify the digital signature of the context snapshot data packet using the food traceability distributed ledger, and cross-compare the internal logical consistency between the environmental data and vehicle operating state data in the context snapshot data packet according to a preset logical judgment rule; an abnormality marking and recording module configured to mark the context snapshot data packet as abnormal through the food traceability distributed ledger and record the context snapshot data packet after abnormality marking if the cross-comparison judgment has logical inconsistency.

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