A supply chain interaction method and system for ensuring data trustworthiness

Abstract

This invention relates to the fields of supply chain finance risk control and industrial IoT technology, and discloses a supply chain interaction method and system to ensure data credibility. The method includes the following steps: maintaining an inventory ledger in the supervised area and calculating the theoretical equivalent heat capacity as a benchmark target value; determining the operating conditions in real time, and when in a steady-state window and meeting safety constraints, generating a temperature setpoint drift command to execute active perturbation, and simultaneously collecting high-frequency thermal response data; using a constructed thermodynamic model to inversely calculate the actual measured heat capacity of the supervised area, and calculating the physical conformity deviation between it and the benchmark target value; combining the physical conformity deviation with the passive energy conservation check results to calculate a comprehensive confidence score, and when the score is higher than a threshold, generating a verification data packet and sending it to a blockchain node for notarization. This invention verifies the authenticity of inventory through physical thermodynamic characteristics, realizing a closed-loop process from physical perception to blockchain notarization, and solving the problem of inventory data falsification in supply chain operations.

Description

Technical Field

[0001] This invention relates to the fields of supply chain finance risk control and industrial Internet of Things (IoT) technology, specifically to a supply chain interaction method and system that ensures data reliability. Background Technology

[0002] With the rapid development of supply chain finance and digital warehousing technology, inventory-backed financing has become an important means of solving the financing difficulties of SMEs. In this model, the authenticity and accuracy of inventory data within the regulated area are the core basis for financial institutions to conduct risk control. To achieve effective supervision of warehoused goods, the industry has generally established digital warehouse management systems and is attempting to match logistics information with cash flow information through various technological means to ensure the security of pledged assets.

[0003] Existing supply chain monitoring technologies primarily rely on video surveillance, radio frequency identification (RFID), and Internet of Things (IoT) sensor networks for cargo tracking and inventory management. Video surveillance utilizes computer vision to identify the appearance and movement of goods, RFID technology obtains inbound and outbound information by reading electronic tags attached to goods, and IoT sensors assist in collecting warehouse environmental parameters. Furthermore, to prevent tampering with electronic ledger data, some solutions are beginning to incorporate blockchain technology, uploading inventory ledger data summaries generated by the warehouse management system to a distributed ledger, leveraging the immutability of blockchain to ensure data security.

[0004] While existing technologies have improved the efficiency and transparency of warehouse management to some extent, several shortcomings remain: Current visual recognition and tag tracking technologies primarily monitor the external form or attached markings of goods, lacking the ability to verify the internal physical properties of goods. This makes it difficult to detect fraudulent activities such as selling inferior goods as superior ones or falsely declaring empty boxes, resulting in a substantial separation between information flow and physical flow. Relying solely on passively collected environmental monitoring data is limited by environmental thermal noise and low signal-to-noise ratios under steady-state conditions, making it impossible to accurately analyze thermodynamic parameters reflecting total inventory and material characteristics, and hindering the establishment of a strong correlation between data and physical goods. Furthermore, existing blockchain-based evidence storage applications mainly address the issue of preventing tampering after data is uploaded to the blockchain, but cannot guarantee the physical authenticity of the source data before it is uploaded. Once the source sensors are deceived or false records are entered, the blockchain can only solidify unreliable and erroneous data, failing to fundamentally solve the trust and traceability issues in supply chain interactions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a supply chain interaction method and system that ensures data reliability. It solves the problem that existing supply chain supervision methods are unable to effectively verify the internal physical attributes of goods, leading to falsified source data that cannot be identified by blockchain.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of this invention provides a supply chain interaction method to ensure data reliability, the method comprising the following steps: Obtain inventory ledger data and real-time air parameters within the regulatory area, calculate the equivalent heat capacity of the regulatory area under the current theoretical inventory state, and mark it as the benchmark target value for physical verification. Collect on-site thermal environment data and operating condition switch signals to determine the current operating condition of the warehouse. Under interference conditions, only perform passive energy conservation checks based on long cycles. When the operating condition is determined to be in a steady-state window and the safety constraints are met, a temperature setpoint drift command is generated to drive the refrigeration equipment to produce controlled power changes and collect high-frequency response data. A thermodynamic model is constructed using the high-frequency response data to invert and calculate the actual measured heat capacity value of the monitored area, and to calculate the physical conformity deviation between the measured heat capacity value and the benchmark target value. A comprehensive confidence score is calculated by combining the physical compliance deviation and the results of the passive energy conservation check. When the score is higher than the preset confidence threshold, a verification data packet is generated and sent to the blockchain node for evidence storage.

[0007] Preferably, the calculation of the equivalent heat capacity of the monitored area under the current theoretical inventory state specifically includes: Based on the product categories in the inventory ledger, a pre-set physical parameter database is accessed to obtain the density and specific heat capacity at constant pressure for each product. Following the principle of thermodynamic superposition of extensive quantities, the effective air volume of the empty warehouse is obtained by subtracting the volume displaced by all the physical inventory from the fixed net volume of the supervised area in an empty warehouse state. The baseline target value is obtained by weighting and superimposing the air heat capacity contribution corresponding to the effective air volume of the empty warehouse with the heat capacity contribution of all the inventory goods.

[0008] Preferably, determining the current operating status of the warehouse specifically includes: Real-time monitoring of the warehouse door opening and closing status signal and calculation of the time series volatility of the temperature inside the warehouse; When the gate opening / closing status signal indicates that the gate is open, or when the volatility exceeds a preset noise threshold, it is determined to be an interference condition, and the active parameter identification process is suspended. When the gate switch status signal indicates that the gate is closed, and the volatility remains below the noise threshold for a period of time exceeding the preset stable duration, it is determined to be the steady-state window.

[0009] Preferably, the passive energy conservation check specifically includes: Acquire the instantaneous active power, coefficient of performance, and temperature difference between inside and outside the refrigeration unit within a set long period; The energy conservation deviation is obtained by calculating the difference between the total heat removed by the refrigeration unit during the long period and the total heat infiltrated by the enclosure structure during the long period, and subtracting the change in internal energy caused by the temperature change inside the warehouse. If the absolute value of the energy conservation deviation is less than the allowable engineering cumulative error threshold, the passive monitoring result is determined to be normal; otherwise, it is marked as abnormal.

[0010] Preferably, the safety constraints include verifying the compressor running time and the cargo temperature control safety margin; the temperature setpoint drift command includes the target setpoint drift amount and the duration of the perturbation, wherein the direction of the target setpoint drift amount is configured to increase the output power of the refrigeration unit.

[0011] Preferably, the construction of the thermodynamic model specifically refers to the construction of a first-order lumped-parameter RC network model, whose differential equations describe the rate of change of the system's internal energy as equal to the difference between the heat flow entering the building envelope and the heat flow leaving the chiller unit. The heat flow entering the enclosure structure is calculated based on the comprehensive thermal conductivity of the enclosure structure and the temperature difference between the inside and outside of the warehouse, while the heat flow leaving the refrigeration unit is calculated based on the controlled changes in the input power of the refrigeration unit during the active perturbation and the corrected coefficient of performance. The overall thermal conductivity value is a system constant calibrated in advance under empty conditions through active perturbation testing.

[0012] Preferably, the inversion calculation of the actual measured heat capacity value of the monitored area specifically includes: The differential equation is converted into a discrete difference equation. Based on the principle of least squares, an objective function is constructed for the actual measured heat capacity value, which minimizes the sum of squared residuals of the equations at all sampling points during the duration of the perturbation, thereby numerically solving for the actual measured heat capacity value.

[0013] Preferably, the inversion calculation of the actual measured heat capacity value of the monitored area specifically includes: The differential equation is converted into a discrete difference equation. Based on the principle of least squares, an objective function is constructed for the actual measured heat capacity value, which minimizes the sum of squared residuals of the equations at all sampling points during the duration of the perturbation, thereby numerically solving for the actual measured heat capacity value.

[0014] Preferably, the calculation of the comprehensive confidence score specifically includes: The results of the passive energy conservation check are quantified to generate passive monitoring indicators; Calculate the absolute value of the relative error of the physical compliance deviation and normalize it using the deviation sensitivity coefficient; A weighted fusion algorithm is used to linearly weight the normalized deviation data with the passive monitoring indicators to obtain the comprehensive confidence score.

[0015] Preferably, the specific process for the blockchain node to perform evidence storage is as follows: Generate a verification data packet containing the verification session ID, verification timestamp, inventory ledger hash digest, comprehensive confidence score, and snapshot of key physical parameters; The verification data packet is digitally signed using a private key, the data digest is written into the distributed ledger through a smart contract interface, and the transaction hash is returned.

[0016] A second aspect of the present invention provides a supply chain interaction system for ensuring data reliability, using the method described in any of the preceding claims, the system comprising: The field sensing module is configured to collect thermal environment data, operating condition switch signals and energy consumption data inside and outside the warehouse, and respond to control commands to adjust the operating setpoint of the refrigeration unit; The edge transmission module is configured to perform protocol conversion of data, upload data, and distribute control commands; The processing module is configured to perform calculation of the benchmark target value, determine the operating conditions, generate a temperature setpoint drift command in the steady-state window to perform active perturbation, invert the actual measured heat capacity value based on the thermodynamic model and calculate the physical compliance deviation, and generate a verification data package. The interactive evidence storage module is configured to perform consensus verification on the verification data packet and write the verified data digest into the blockchain network.

[0017] This invention provides a method and system for ensuring reliable data exchange within a supply chain. It offers the following advantages: 1. This invention calculates the theoretical equivalent heat capacity by calling a physical property database and uses active perturbation technology to invert the actual measured heat capacity. Based on differences in thermodynamic response, it can directly verify the quality and category authenticity of inventory goods. This method expands the verification dimension from simple information flow to energy flow, enabling the identification of anomalies that are difficult to detect using traditional regulatory methods, such as false reporting of empty warehouses, goods substitution, or the substitution of inferior goods, without opening packaging or touching the goods. This ensures the physical authenticity of assets within the regulated area.

[0018] 2. This invention, after confirming that the compressor operating time and temperature control margin meet safety constraints, introduces a controlled temperature setpoint drift, thereby stimulating a clear transient thermal response and overcoming the shortcomings of low signal-to-noise ratio and unclear features in passive monitoring modes. Simultaneously, by combining the model with the pre-calibrated heat transfer coefficient of the building envelope, the interference of environmental heat leakage on the identification results is effectively eliminated. This allows for accurate calculation of the actual heat capacity of the cargo under complex operating conditions using a first-order lumped parameter model.

[0019] 3. This invention generates a quantitative comprehensive confidence score by integrating physical compliance deviation and passive energy conservation check results, and only packages verification data above the confidence threshold onto the blockchain. This mechanism acts as a filter connecting the physical world and the digital ledger, ensuring that the data written to the blockchain is not only tamper-proof in terms of encryption algorithms, but also objectively verified at its physical source, providing financial institutions or regulators with high-quality credit credentials containing snapshots of precise physical parameters. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the method flow of the present invention; Figure 2 This is a schematic diagram of the module architecture of the present invention; Figure 3 This is a schematic diagram of the digital physical mapping benchmark model construction process of the present invention; Figure 4 This is a schematic diagram of the working condition state machine determination and passive energy conservation monitoring logic of the present invention; Figure 5 This is a schematic diagram of the thermodynamic parameter identification process based on active perturbation in this invention. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see the appendix Figure 1 , Figure 1 This is a flowchart illustrating a supply chain interaction method for ensuring data reliability according to an embodiment of the present invention. The present invention provides a supply chain interaction method for ensuring data reliability, the method comprising the following steps: S1. Obtain inventory ledger data within the current regulatory area through the communication interface with the external warehouse management system; call the preset physical parameter database and match the corresponding physical attribute parameters according to the goods category in the inventory ledger; combine the real-time collected air parameters and use the weighted superposition algorithm to calculate the equivalent heat capacity value of the regulatory area under the current theoretical inventory state, and mark the equivalent heat capacity value as the benchmark target value for physical verification.

[0023] S2. Real-time acquisition of on-site thermal environment data, operating condition switch signals, and energy consumption data; determination of the current warehouse operating condition by calculating the fluctuation rate of temperature data and combining it with switch signals; when the condition is determined to be a disturbance, the active parameter identification process is paused to avoid introducing non-steady-state data noise, while only a long-cycle passive energy conservation check is performed in the background.

[0024] S3. When the operating condition is determined to be in a steady state window, and the compressor running time and cargo temperature control safety constraints are verified to be met, a temperature setpoint drift command is generated; the command is issued to drive the refrigeration equipment to produce controlled power changes, and the data sampling frequency is adjusted to high frequency mode to synchronously record the temperature response sequence in the warehouse and the power consumption sequence of the refrigeration equipment.

[0025] S4. Using the collected transient temperature response sequence and power consumption sequence, construct a first-order lumped-parameter thermodynamic model reflecting the thermal dynamic characteristics of the monitored area; solve the differential equations in the model numerically based on the least squares method, and calculate the actual measured heat capacity of the monitored area; calculate the physical conformity deviation between the actual measured heat capacity and the benchmark target value obtained in step S1.

[0026] S5. Based on the physical compliance deviation and the passive energy conservation check results in step S2, calculate the comprehensive confidence score using a weighted algorithm; compare the comprehensive confidence score with a preset confidence threshold; when the score is higher than the threshold, generate a verification data packet containing a summary of the verification results and key physical parameters; send the verification data packet to the blockchain node for on-chain storage.

[0027] Please see the appendix Figure 2 , Figure 2 This is a schematic diagram of the module architecture of a supply chain interaction system for ensuring data trustworthiness according to an embodiment of the present invention. The supply chain interaction system for ensuring data trustworthiness is logically divided into a field sensing module 10, an edge transmission module 20, a processing module 30, and an interaction and evidence storage module 40.

[0028] The on-site sensing module 10 is deployed at the physical site of the warehouse and is configured to collect multi-dimensional physical data and provide feedback for action execution. The on-site sensing module 10 specifically includes a thermal sensing unit 11, an operating condition sensing unit 12, an energy consumption metering unit 13, and an execution drive unit 14.

[0029] The thermal sensing unit 11 includes temperature sensors distributed inside the warehouse and meteorological sensors deployed outdoors, used to collect temperature field data inside the warehouse and ambient temperature data outside the warehouse.

[0030] The operating condition sensing unit 12 includes an opening and closing status detection sensor installed at the warehouse door and a refrigeration unit operating status monitoring point, which is used to collect switch signals that characterize the operating condition of the warehouse.

[0031] The energy consumption metering unit 13 is connected to the power supply circuit of the refrigeration unit and is used to collect the instantaneous active power and cumulative electrical energy data of the refrigeration unit.

[0032] The drive unit 14 is connected in communication with the controller of the refrigeration unit to receive control commands and adjust the operating setpoint of the refrigeration unit.

[0033] The edge transmission module 20 connects the field sensing module 10 and the processing module 30, and is configured to perform data aggregation, protocol conversion, and instruction distribution. Specifically, the edge transmission module 20 includes a protocol conversion unit 21 and a data forwarding unit 22.

[0034] Protocol conversion unit 21 is configured to convert heterogeneous data collected by field sensing module 10 into a unified transmission protocol format.

[0035] The data forwarding unit 22 is configured to upload the processed data to the processing module 30 and forward the control commands issued by the processing module 30 to the execution drive unit 14.

[0036] The processing module 30 is configured to execute parameter identification algorithms and security control logic. Specifically, the processing module 30 includes a data mapping unit 31, a parameter storage unit 32, an identification calculation unit 33, and a security policy unit 34.

[0037] The data mapping unit 31 is equipped with a communication interface with an external warehouse management system for querying and obtaining inventory ledger information.

[0038] The parameter storage unit 32 is used to store the thermophysical property parameter table of the material and the performance curve data of the refrigeration equipment.

[0039] The identification and calculation unit 33 is configured to construct a thermodynamic model and inversely calculate the actual heat capacity value and physical conformity deviation based on the collected data.

[0040] The safety policy unit 34 is used to monitor the operating status of the equipment and verify whether the safety constraints are met before generating instructions.

[0041] The interactive evidence storage module 40 is configured to implement distributed storage of verification results. Specifically, the interactive evidence storage module 40 includes a consensus verification unit 41 and a distributed ledger unit 42.

[0042] The consensus verification unit 41 is used to perform digital signature verification on the received verification data packet.

[0043] Distributed ledger unit 42 is used to write verified data digests into the blockchain network and return transaction hash credentials.

[0044] Please see the appendix Figure 3 , Figure 3 This is a schematic diagram illustrating the construction process of a digital-physical mapping benchmark model according to an embodiment of the present invention. The data mapping unit establishes a data communication connection with an external warehouse management system through an application programming interface or middleware service, and initiates a query command to obtain real-time inventory ledger information within the current regulatory target area. This inventory ledger information includes the set of categories of currently stored goods, the declared quality corresponding to each category, and their respective storage location codes.

[0045] Subsequently, the identification and calculation unit retrieves the physical attribute table pre-stored in the parameter storage unit based on the goods category identifier in the inventory ledger information. This physical attribute table stores the thermophysical parameters of various materials, specifically including the isobaric specific heat capacity constant and density of each category of goods. In this embodiment, the parameter storage unit pre-stores specific heat capacity and density data for common cold chain goods. For example, the specific heat capacity of frozen meat products is typically set to range from 1.6 kJ / (kg·K) to 3.2 kJ / (kg·K), and the density range is set to 900 kg / m³. 3 Up to 1100kg / m 3 The identification and calculation unit matches the corresponding specific heat capacity value based on the category identifier. If there is a category that is not included, the preset default value of the heat capacity of general materials is called.

[0046] While determining the cargo parameters, the identification and calculation unit determines the heat capacity contribution of the air within the monitored area. The unit reads pre-configured geometric parameters of the monitored area and calculates the effective air volume of the empty warehouse within that area. Simultaneously, the unit acquires real-time warehouse temperature data collected by the thermal sensing unit and, based on the ideal gas law or by consulting air property tables, determines the current air density and specific heat capacity at constant pressure.

[0047] Based on the aforementioned physical parameters and inventory ledger data, the identification calculation unit employs a weighted superposition algorithm to calculate the total equivalent heat capacity of the monitored area under the current theoretical state, and marks this value as the benchmark target value for this verification cycle. This calculation follows the superposition principle of thermodynamic extensive quantities; when calculating the contribution of air heat capacity, the volume of air displaced by the physical inventory goods must be deducted. The calculation formula is as follows: ; In the formula, This represents the theoretical total equivalent heat capacity of the regulated area. The fixed net volume of the regulated area in an empty warehouse state (i.e., the volume after deducting fixed facilities such as building columns and refrigeration equipment). This is an index variable for the product category; This represents the total number of different types of goods recorded in the inventory ledger. For the first The declared total mass of the goods; For the first The average density of the goods; The air density within the regulated area; The specific heat capacity of air at constant pressure; For the first A type of cargo in a specific physical state The specific heat capacity at constant pressure is given by the superscript. This parameter indicates that it is a standard physical property parameter called from a preset physical property database.

[0048] Please see the appendix Figure 4 , Figure 4 This is a schematic diagram of the operating condition state machine determination and passive energy conservation monitoring logic according to an embodiment of the present invention. The processing module receives sensor data streams uploaded by the edge transmission module in real time. These data streams include the gate opening / closing status signal collected by the operating condition sensing unit and the temperature time series inside the storage unit collected by the thermal sensing unit. The processing module performs sliding window statistical analysis on the temperature time series inside the storage unit to calculate the temperature fluctuation rate within the current time window. For the calculation of temperature fluctuation rate, the processing module is configured to represent it using the standard deviation of the temperature data within the sliding window, or using the mean of the absolute values ​​of the temperature differences between adjacent sampling points.

[0049] Based on finite state machine logic, when the processing module detects that the gate opening / closing signal is at a valid level indicating that the gate is open, or when the calculated temperature fluctuation rate is greater than a preset noise threshold, the processing module determines that it is currently in an interference condition. In this state, the processing module automatically suspends subsequent active perturbation and parameter identification tasks. In this embodiment, the noise threshold is preferably set to 0.5℃ / h to 1.0℃ / h to filter out normal temperature control fluctuations.

[0050] When the processing module detects that the door opening / closing status signal indicates that the door is closed, and the temperature fluctuation rate remains below a preset noise threshold for a period exceeding a preset stabilization time, the processing module determines that the system is currently in a steady-state window. In this embodiment, the stabilization time is preferably set to 15 to 30 minutes to ensure that the thermodynamic system reaches a quasi-static state.

[0051] While determining the operating conditions, the processing module performs a long-term passive energy conservation check in the background. The processing module extracts instantaneous active power data of the chiller unit and internal and external temperature data uploaded by the energy consumption metering unit within a set long period. Simultaneously, it retrieves the preset comprehensive heat transfer coefficient of the building envelope from the parameter storage unit. and effective heat transfer area (in, The values ​​were obtained in advance through thermal calibration tests under empty storage conditions.

[0052] The deviation between the total heat removed by the cooling system and the total heat infiltrated by the building envelope is calculated. The heat removed by the cooling system is obtained by integrating the product of electrical power and real-time energy efficiency ratio; the heat infiltrated by the building envelope is obtained by integrating the product of heat transfer coefficient and internal / external temperature difference; and the change in internal energy is calculated based on theoretical heat capacity and the initial and final temperature differences of the cycle. The processing module establishes the following energy balance inequality: ; In the formula, The long period duration set for passive energy conservation checks; It is a time variable; It is a time differential element; In order to conduct long-term monitoring, The instantaneous active power input to the refrigeration unit at all times; In order to conduct long-term monitoring, The performance coefficient of the refrigeration unit at any given time is determined based on the preset unit energy efficiency characteristic curves corresponding to the ambient temperature and the temperature inside the refrigeration unit at that time. The overall heat transfer coefficient of the building envelope; The effective heat transfer area of ​​the building envelope; For long-term monitoring, The ambient temperature outside the warehouse at any given time; For long-term monitoring, The average temperature inside the warehouse at any given time; The temperature inside the warehouse at the start of the passive monitoring cycle; The temperature inside the warehouse at the end of the passive monitoring cycle; This represents the allowable cumulative engineering error threshold. In this embodiment, The value can be either 12 hours or 24 hours; the allowable cumulative error threshold for engineering. Set to 10% to 15% of the total theoretical heat transfer.

[0053] If the calculated deviation value exceeds the allowable cumulative error threshold, it indicates that there is an abnormal energy dissipation during the long period, and the processing module will mark the passive monitoring results during that period as abnormal.

[0054] Please see the appendix Figure 5 , Figure 5 This is a schematic diagram of the thermodynamic parameter identification process based on active perturbation according to an embodiment of the present invention. After confirming that the system is in a steady-state window, the safety strategy unit first performs a pre-perturbation safety check to verify whether the constraints such as the minimum compressor running time threshold, the minimum compressor downtime threshold, the non-defrost cycle period, and the critical safety temperature margin of the goods are simultaneously met. In this embodiment, it verifies whether the minimum compressor running time has exceeded 5 minutes and whether the current storage temperature is at least 2°C away from the upper limit of the goods' safety temperature.

[0055] When the aforementioned safety constraints are met, the safety strategy unit generates a temperature setpoint drift command. This command includes a target setpoint drift amount and a perturbation duration. The direction of the target setpoint drift amount is configured to increase the output power of the refrigeration unit to elicit a clear thermal response. In this embodiment, the absolute value of the target setpoint drift amount is configured to be 1 to 3°C, and the perturbation duration is configured to be 20 to 40 minutes to ensure that the elicited thermal response is sufficiently significant and does not affect cargo safety.

[0056] The edge transmission module sends the instruction to the execution drive unit and adjusts the data sampling frequency to high-frequency mode during the duration of the perturbation, and simultaneously records the temperature response sequence inside the warehouse, the temperature sequence outside the warehouse, and the active power sequence of the refrigeration unit.

[0057] Subsequently, the identification and calculation unit constructs a first-order lumped-parameter RC network model describing the thermal dynamics of the monitored area. This model is based on the principle of energy conservation, assuming that the rate of change of the system's internal energy is equal to the difference between the heat flow entering the system and the heat flow leaving the chiller unit. The established differential equation is as follows: ; In the formula, The actual measured heat capacity value of the regulatory area to be identified; The rate of change of temperature in the storage chamber over time during active perturbation; It is a time variable; The overall heat transfer coefficient of the building envelope; The effective heat transfer area of ​​the building envelope; During active perturbation The ambient temperature outside the warehouse at any given time; During active perturbation The average temperature inside the warehouse at any given time; During active perturbation The constantly changing input electrical power of the refrigeration unit; During active perturbation Down, Performance coefficient of the refrigeration unit, subscript This coefficient represents a correction value under high-frequency sampling and variable power conditions; The overall thermal conductivity of the building envelope, i.e., the overall heat transfer coefficient. With effective heat transfer area The product of these values, which are pre-stored system constants, is obtained by performing active perturbation tests and using the least squares method in an empty warehouse state before the warehouse is put into use. By solving and calibrating the problem as the only unknown variable, the interference of heat leakage from the building envelope on the subsequent identification of the heat capacity of the cargo was eliminated.

[0058] To solve for the unknown parameters in the above differential equation, the identification computing unit discretizes the differential equation, converting it into a difference equation form: ; In the formula, The actual measured heat capacity value of the regulatory area to be identified; This represents the high-frequency sampling time interval during active perturbation. The current sampling time The temperature inside the storage room at the next sampling time; It is a time variable; The overall heat transfer coefficient of the building envelope; The effective heat transfer area of ​​the building envelope; During active perturbation The ambient temperature outside the warehouse at any given time; During active perturbation The average temperature inside the warehouse at any given time; During active perturbation The constantly changing input electrical power of the refrigeration unit; During active perturbation Down, Performance coefficient of the refrigeration unit, subscript This coefficient represents a correction value under high-frequency sampling and variable power conditions.

[0059] Based on this difference equation, the identification computational unit constructs a least-squares objective function for the actual measured heat capacity value. Through iterative calculation, the sum of squared residuals of the equations at all sampling points within the duration of the perturbation is minimized, thereby solving for the optimal actual measured heat capacity value. This numerical solution process belongs to existing techniques in the field of mathematical optimization; the specific iterative logic will not be elaborated here.

[0060] Finally, the identification calculation unit calculates the physical conformity deviation. This deviation characterizes the degree of consistency between the actual measured heat capacity value obtained from the inversion and the theoretical equivalent heat capacity value calculated in the digital physical mapping reference. To reflect the magnitude of the deviation, the absolute value of the relative error is used in the calculation, and the calculation formula is as follows: ; In the formula, For physical conformity deviation; For absolute value operations in a specific pattern, the subscript This operation represents the statistical processing pattern after removing outliers. By taking the absolute value, it ensures that whether the actual heat capacity is too large or too small, it is reflected as a positive deviation value, thus correctly driving the subsequent confidence score deduction logic.

[0061] The processing module receives the physical compliance deviation output by the identification and calculation unit and the long-term monitoring results output by the passive monitoring mechanism. The processing module quantizes the long-term monitoring results to generate a passive monitoring index. The processing module is configured to generate this passive monitoring index using either binary logic or linear decay logic. Under binary logic, the passive monitoring index is assigned a value of 1 when the energy integral deviation does not exceed the allowable engineering cumulative error threshold; otherwise, it is assigned a value of 0. Under linear decay logic, the passive monitoring index is assigned a value of 1 minus the ratio when the ratio of the energy integral deviation to the threshold is less than 1; otherwise, it is assigned a value of 0.

[0062] Next, the processing module uses a weighted fusion algorithm to calculate the current overall confidence score. This score quantifies the true credibility of the current inventory status at the physical level, and the calculation formula is as follows: ; In the formula, The overall confidence score ranges from [0,1]. For physical conformity deviation; For passive monitoring indicators, superscript This indicator is a value generated based on the results of energy conservation checks. This is a deviation sensitivity coefficient, configured to adjust the system's tolerance to thermal capacity deviations. In this embodiment, Setting it to 3 to 5 means that when the physical deviation exceeds 20% to 33%, the confidence level will decrease significantly. and The weighting coefficients must satisfy the condition that the sum of the two is 1. In this embodiment, Set to 0.6 to 0.8. The corresponding values ​​are set to 0.2 to 0.4.

[0063] The processing module compares the calculated overall confidence score with a preset confidence threshold. In this embodiment, the confidence threshold is preferably set to 0.8 to filter verification results with high confidence. If the overall confidence score is greater than or equal to the confidence threshold, the processing module generates a verification data packet. This verification data packet includes a verification session ID, a verification timestamp accurate to the second, a hash digest calculated based on inventory ledger data, the overall confidence score, and a snapshot of key physical parameters.

[0064] Finally, the processing module digitally signs the verification data packet using its private key, generating a transaction payload containing the signature, and sends the transaction payload to the interactive evidence storage module via the edge transmission module. The consensus verification unit in the interactive evidence storage module calls a pre-deployed smart contract interface to write the hash value and core fields of the verification data packet into the distributed ledger unit, and returns the transaction hash to complete the trusted evidence storage of the supply chain data. If the overall confidence score is less than the trust threshold, the processing module determines that the verification has failed, generates a risk control warning signal, and refuses to generate a trusted warehouse receipt.

[0065] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A supply chain interaction method to ensure data reliability, characterized in that, The method includes the following steps: Obtain inventory ledger data and real-time air parameters within the regulatory area, calculate the equivalent heat capacity of the regulatory area under the current theoretical inventory state, and mark it as the benchmark target value for physical verification. Collect on-site thermal environment data and operating condition switch signals to determine the current operating condition of the warehouse. Under interference conditions, only perform passive energy conservation checks based on long cycles. When the operating condition is determined to be in a steady-state window and the safety constraints are met, a temperature setpoint drift command is generated to drive the refrigeration equipment to produce controlled power changes and collect high-frequency response data. A thermodynamic model is constructed using the high-frequency response data to invert and calculate the actual measured heat capacity value of the monitored area, and to calculate the physical conformity deviation between the measured heat capacity value and the benchmark target value. A comprehensive confidence score is calculated by combining the physical compliance deviation and the results of the passive energy conservation check. When the score is higher than the preset confidence threshold, a verification data packet is generated and sent to the blockchain node for evidence storage.

2. The supply chain interaction method for ensuring data reliability according to claim 1, characterized in that, The calculation of the equivalent heat capacity of the monitored area under the current theoretical inventory state specifically includes: Based on the product categories in the inventory ledger, a pre-set physical parameter database is accessed to obtain the density and specific heat capacity at constant pressure for each product. Following the principle of thermodynamic superposition of extensive quantities, the effective air volume of the empty warehouse is obtained by subtracting the volume displaced by all the physical inventory from the fixed net volume of the supervised area in an empty warehouse state. The baseline target value is obtained by weighting and superimposing the air heat capacity contribution corresponding to the effective air volume of the empty warehouse with the heat capacity contribution of all the inventory goods.

3. The supply chain interaction method for ensuring data reliability according to claim 1, characterized in that, The determination of the current operating status of the warehouse specifically includes: Real-time monitoring of the warehouse door opening and closing status signal and calculation of the time series volatility of the temperature inside the warehouse; When the gate opening / closing status signal indicates that the gate is open, or when the volatility exceeds a preset noise threshold, it is determined to be an interference condition, and the active parameter identification process is suspended. When the gate switch status signal indicates that the gate is closed, and the volatility remains below the noise threshold for a period of time exceeding the preset stable duration, it is determined to be the steady-state window.

4. The supply chain interaction method for ensuring data reliability according to claim 1, characterized in that, The passive energy conservation check specifically includes: Acquire the instantaneous active power, coefficient of performance, and temperature difference between inside and outside the refrigeration unit within a set long period; The energy conservation deviation is obtained by calculating the difference between the total heat removed by the refrigeration unit during the long period and the total heat infiltrated by the enclosure structure during the long period, and subtracting the change in internal energy caused by the temperature change inside the warehouse. If the absolute value of the energy conservation deviation is less than the allowable engineering cumulative error threshold, the passive monitoring result is determined to be normal; otherwise, it is marked as abnormal.

5. A supply chain interaction method for ensuring data reliability according to claim 1, characterized in that, The safety constraints include verifying the compressor running time and the cargo temperature control safety margin; the temperature setpoint drift command includes the target setpoint drift amount and the duration of the perturbation, wherein the direction of the target setpoint drift amount is configured to increase the output power of the refrigeration unit.

6. A supply chain interaction method for ensuring data reliability according to claim 1, characterized in that, The construction of the thermodynamic model specifically refers to the construction of a first-order lumped parameter RC network model. The differential equation of this model describes the rate of change of the internal energy of the system as equal to the difference between the heat flow entering the building envelope and the heat flow leaving the chiller unit. The heat flow entering the enclosure structure is calculated based on the comprehensive thermal conductivity of the enclosure structure and the temperature difference between the inside and outside of the warehouse, while the heat flow leaving the refrigeration unit is calculated based on the controlled changes in the input power of the refrigeration unit during the active perturbation and the corrected coefficient of performance. The overall thermal conductivity value is a system constant calibrated in advance under empty conditions through active perturbation testing.

7. A supply chain interaction method for ensuring data reliability according to claim 1, characterized in that, The inversion calculation of the actual measured heat capacity value of the monitored area specifically includes: The differential equation is converted into a discrete difference equation. Based on the principle of least squares, an objective function is constructed for the actual measured heat capacity value, which minimizes the sum of squared residuals of the equations at all sampling points during the duration of the perturbation, thereby numerically solving for the actual measured heat capacity value.

8. A supply chain interaction method for ensuring data reliability according to claim 1, characterized in that, The calculation of the overall confidence score specifically includes: The results of the passive energy conservation check are quantified to generate passive monitoring indicators; Calculate the absolute value of the relative error of the physical compliance deviation and normalize it using the deviation sensitivity coefficient; A weighted fusion algorithm is used to linearly weight the normalized deviation data with the passive monitoring indicators to obtain the comprehensive confidence score.

9. A supply chain interaction method for ensuring data reliability according to claim 1, characterized in that, The specific process for the blockchain node to perform evidence storage is as follows: Generate a verification data packet containing the verification session ID, verification timestamp, inventory ledger hash digest, comprehensive confidence score, and snapshot of key physical parameters; The verification data packet is digitally signed using a private key, the data digest is written into the distributed ledger through a smart contract interface, and the transaction hash is returned.

10. A supply chain interaction system that ensures data reliability, characterized in that, Using the supply chain interaction method for ensuring data trustworthiness as described in any one of claims 1-9, the system comprises: The field sensing module is configured to collect thermal environment data, operating condition switch signals and energy consumption data inside and outside the warehouse, and respond to control commands to adjust the operating setpoint of the refrigeration unit; The edge transmission module is configured to perform protocol conversion of data, upload data, and distribute control commands; The processing module is configured to perform calculation of the benchmark target value, determine the operating conditions, generate a temperature setpoint drift command in the steady-state window to perform active perturbation, invert the actual measured heat capacity value based on the thermodynamic model and calculate the physical compliance deviation, and generate a verification data package. The interactive evidence storage module is configured to perform consensus verification on the verification data packet and write the verified data digest into the blockchain network.

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