A remote monitoring system of intelligent anti-cheating pricing scale

Abstract

The application relates to the technical field of anti-cheating pricing scales, in particular to a remote monitoring system of an intelligent anti-cheating pricing scale. The system comprises: a pricing scale benchmark input module, which is used for collecting a weight voltage signal, constructing a coordinate set after normalization processing, calculating a benchmark characteristic value by using a minimum spanning tree algorithm and recording an ambient temperature; and a pricing scale verification module, which is used for collecting a to-be-tested object voltage signal and calculating a to-be-tested characteristic value, correcting the benchmark characteristic value by using the ambient temperature to obtain a dynamic reference threshold value, and determining whether the weighing is compliant by comparing a deviation degree. The application solves the problem that a light object fake weight is difficult to identify in remote calibration, and realizes remote automatic verification and safe control of the anti-cheating pricing scale.

Description

Technical Field

[0001] This application relates to the field of anti-cheating pricing scale technology, specifically to a remote monitoring system for an intelligent anti-cheating pricing scale. Background Technology

[0002] The accuracy of electronic price-computing scales relies on linear calibration coefficients stored in the microcontroller, which are established through calibration using standard-mass weights. In remote monitoring scenarios, since supervisors cannot be present in real time, there is a risk that unscrupulous merchants may use non-standard-mass lightweight objects (e.g., using a 2kg object to impersonate a 5kg standard weight) for parameter calibration. Such operations are often combined with illegal modifications to the circuit gain (such as parallel resistors or modifying software coefficients), artificially amplifying the voltage reading generated on the sensor by the lightweight object, thus making the numerical response appear consistent with that of a standard weight.

[0003] Current technologies primarily implement regulation by verifying digital certificates, checking operation logs, or comparing steady-state voltage amplitudes. However, since quality and gain are equivalent in steady-state voltage output... Simply relying on voltage amplitude cannot distinguish between standard mass + standard gain and lightweight object + high gain. Furthermore, although the transient rebound frequency of objects with different masses varies (frequency is inversely proportional to the square root of mass), the microcontroller sampling rate of conventional price-computing scales is low (usually below 100Hz), making it difficult to accurately extract frequency features from short-sequence signals using traditional frequency domain analysis (such as FFT). This results in an inability to effectively identify physical mass fraud during remote calibration, hindering remote automatic verification and security control of anti-cheating price-computing scales. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a remote monitoring system for an intelligent anti-cheating price-computing scale, and the specific technical solution adopted is as follows: This application proposes a remote monitoring system for an intelligent anti-cheating price-computing scale, the system comprising: The benchmark input module for the price-computing scale is used to collect the voltage signal of the weight after it contacts the weighing pan of the price-computing scale and normalize it; based on the normalized voltage amplitude at each time point and the change range of the normalized voltage amplitude between adjacent time points in the normalized voltage signal, a voltage-change range coordinate set is constructed; based on the distance between all coordinate points in the voltage-change range coordinate set, a fully connected undirected graph is constructed using the minimum spanning tree algorithm, the weights of all edges in the fully connected undirected graph are accumulated, the resulting value is recorded as the benchmark feature value, and the ambient temperature at this time is obtained; The price-computing scale verification module is used to collect the voltage signal containing the oscillating waveform of the current object to be measured and construct a voltage-variance amplitude coordinate set, and calculate the target feature value of the current object to be measured according to the calculation method of the reference feature value; acquire the ambient temperature in real time, and use the preset material temperature response coefficient to perform temperature drift correction on the reference feature value to obtain a dynamic reference threshold; calculate the deviation between the target feature value and the dynamic reference threshold to determine whether it is compliant to use the price-computing scale to weigh the current object to be measured.

[0005] Preferably, the process of acquiring the voltage signal of the weight includes: After the weights come into contact with the weighing pan of the price-computing scale, the voltage signal is continuously detected and collected. The backward differential value of the voltage amplitude at each moment is calculated. When the backward differential value is detected to change from a positive value to a negative value for the first time, the corresponding moment is taken as the starting moment, and the signal within the preset time period is continuously collected as the voltage signal of the weights.

[0006] Preferably, the process of normalizing the voltage signal is as follows: Calculate the mean of a preset proportion of voltage amplitudes at the end of the voltage signal as the steady-state baseline. Calculate the absolute difference between all voltage amplitudes in the voltage signal and the steady-state baseline. Select the largest absolute difference and use the ratio of all absolute differences to the largest absolute difference as the normalized value of the corresponding voltage amplitude.

[0007] Preferably, the process of constructing the voltage-variation amplitude coordinate set is as follows: In the normalized voltage signal, the normalized voltage amplitude at each time point and the corresponding change range of the normalized voltage amplitude are used to form two-dimensional coordinate points. The two-dimensional coordinate points at all times in the voltage signal form a voltage-change range coordinate set.

[0008] Preferably, the variation range of the voltage normalized value is the difference between the voltage normalized value at each time point and the voltage normalized value at the adjacent previous time point.

[0009] Preferably, the application of the minimum spanning tree algorithm to construct a fully connected undirected graph includes: Each coordinate point in the voltage-variance coordinate set is treated as a vertex in the graph, and the Euclidean distance between any two vertices is calculated as the weight of the edge connecting the corresponding two vertices. Perform a minimum spanning tree traversal of all vertices, select the set of edges that can connect all vertices without forming a cycle, calculate the sum of the weights of all edges in each edge set, and form the undirected graph by connecting all edges in the edge set corresponding to the minimum weight sum as the fully connected undirected graph.

[0010] Preferred, dynamic reference threshold The expression is: In the formula, Represents the baseline eigenvalue; This indicates the preset material temperature response coefficient; This represents the ambient temperature at which the baseline characteristic value was obtained; This indicates the temperature of the environment in which the object being measured is located.

[0011] Preferably, the calibration steps for the material temperature response coefficient are as follows: The object to be tested and the price-computing scale are placed in a temperature-controlled environment. The measured characteristic values ​​of the object to be tested at different temperatures are calculated. The measured characteristic values ​​of the object to be tested at all temperatures are linearly fitted, and the slope of the fitted line is used as the material temperature response coefficient.

[0012] Preferably, the deviation between the measured feature value and the dynamic reference threshold is the result of the difference between the measured feature value and the dynamic reference threshold divided by the dynamic reference threshold.

[0013] Preferably, the determination of whether the weighing of the current object to be measured using a price-computing scale is compliant includes: If the deviation is greater than the preset allowable deviation for compliance judgment, the current object to be measured is judged to be weighed in violation of regulations; otherwise, the current object to be measured is judged to be weighed in compliance with regulations.

[0014] This application has the following beneficial effects: This application effectively eliminates the interference of circuit gain tampering on feature extraction by acquiring the voltage signal of standard weights and performing gain-reduction normalization processing, retaining only the waveform time structure that reflects the essence of quality; it constructs a voltage-variation amplitude coordinate set using normalized voltage amplitude and variation amplitude, and uses the minimum spanning tree algorithm to quantize weak frequency differences into benchmark feature values, overcoming the problem of frequency domain analysis failure under low sampling rate and significantly improving the sensitivity of physical quality identification; at the same time, it constructs and locks a unique physical fingerprint by combining ambient temperature, providing a precise and tamper-proof decision anchor point for subsequent verification, thus realizing the establishment of a high-precision, interference-resistant anti-cheating benchmark on low-cost hardware; Furthermore, this application extracts the target feature values ​​of the object under test through isomorphic processing and uses the material temperature response coefficient to dynamically correct the reference feature values ​​at temperature, thus constructing a dynamic reference threshold that adapts to environmental changes and effectively eliminating physical property interference caused by temperature drift. By calculating the deviation between the target feature values ​​and the dynamic reference threshold, the physical difference between the current load under test and the standard weight can be accurately quantified. When the deviation exceeds the preset tolerance, it automatically determines a violation and blocks parameter modification; otherwise, it determines compliance and authorizes updates. This enables remote automatic verification and security control of the anti-cheating pricing scale under complex working conditions. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A block diagram of a remote monitoring system for an intelligent anti-cheating price-computing scale provided in one embodiment of this application; Figure 2 This is a flowchart illustrating the compliance determination of weighing of an object under test, provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a remote monitoring system for an intelligent anti-cheating price-computing scale proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following description, in conjunction with the accompanying drawings, details the specific scheme of the remote monitoring system for an intelligent anti-cheating price-computing scale provided in this application.

[0020] Please see Figure 1 The diagram shows a block diagram of a remote monitoring system for an intelligent anti-cheating pricing scale according to an embodiment of this application. The system includes: a pricing scale benchmark input module 101 and a pricing scale verification module 102.

[0021] The benchmark input module 101 is used to collect the voltage signal of the weight after it contacts the weighing pan of the weighing scale and normalize it; based on the normalized voltage amplitude at each time point and the change range of the normalized voltage amplitude between adjacent time points in the normalized voltage signal, a voltage-change range coordinate set is constructed; based on the distance between all coordinate points in the voltage-change range coordinate set, a fully connected undirected graph is constructed using the minimum spanning tree algorithm, the weights of all edges in the fully connected undirected graph are accumulated, the resulting value is recorded as the benchmark feature value, and the ambient temperature at this time is obtained.

[0022] M101: Acquires the voltage signal of the weights and normalizes it.

[0023] A standard measuring instrument—a weight—is placed on the weighing pan of the price-computing scale. The voltage signal under a standard load is collected and then degaussed to extract waveform features that are only related to mass.

[0024] (1) Trigger sampling and voltage signal acquisition.

[0025] In order to accurately capture the moment when the loading action ends and the free rebound oscillation begins in a noisy voltage signal, this embodiment adopts a triggering logic that combines voltage amplitude stage and first peak detection. The specific process is as follows: Using a micro-controlled analog-to-digital converter (ADC) at a preset sampling frequency (This embodiment is) It continuously collects the voltage signal output by the sensor.

[0026] Furthermore, it is determined whether the voltage amplitude has undergone a significant step relative to the zero point of the empty scale. After the weight contacts the weighing pan of the price-computing scale, the voltage signal is continuously detected and collected, and the backward differential value of the voltage amplitude at each moment is calculated. When the backward differential value is detected to change from a positive value to a negative value for the first time, the corresponding moment is taken as the starting moment, and the signal within the preset time period is continuously collected as the voltage signal of the weight.

[0027] It should be noted that the preset duration is set manually. In this embodiment, the preset duration is 3 seconds, which is based on the precise matching of the physical characteristics of the rebound oscillation of the price-computing scale sensor and the balance of low-power hardware performance. Specifically, the free decay oscillation generated by the elastic system of a conventional price-computing scale after being loaded usually enters a steady state in a very short time (hundreds of milliseconds). The 3-second time window is sufficient to capture the entire process of the oscillation from intense to decaying, ensuring that it contains enough waveform feature information for subsequent analysis. In practical applications, as other implementation methods, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0028] The calculation process of the backward difference value is a well-known technique and will not be described in detail here.

[0029] (2) Normalization processing of the voltage signal of the weight.

[0030] The magnitude of the voltage signal of the collected weights depends not only on the mass of the load on the scale, but also on the direct influence of the circuit gain coefficient. In order to prevent criminals from forging voltage amplitude by modifying the gain, this embodiment normalizes the voltage signal of the weights, forcing the waveform amplitude to be mapped to the same dimensionless interval, and only retaining the time structure characteristics of the waveform. The specific process is as follows: In this embodiment, the mean of a preset proportion of voltage amplitudes at the end of the voltage signal is calculated as the steady-state baseline. The absolute difference between all voltage amplitudes in the voltage signal and the steady-state baseline is calculated, and the maximum absolute difference is selected. The ratio of all absolute differences to the maximum absolute difference is used as the normalized value of the corresponding voltage amplitude. The values ​​of all voltage amplitude normalized values ​​are strictly limited to [0,1].

[0031] It should be noted that the preset ratio is set manually. In this embodiment, the preset ratio is 10%, which is based on the optimal balance between the accuracy and computational efficiency of steady-state estimation in signal processing. Specifically, at the end of the acquired voltage signal, the oscillation waveform has tended to be stable and contains small random noise. Selecting the last 10% of points for arithmetic mean calculation of the steady-state baseline conforms to the principle that small sample mean estimation can effectively smooth random errors in statistics, and can obtain an accurate voltage reference representing the oscillation static state. If the number of points is too small, it is easy to be affected by transient noise, resulting in an inaccurate baseline. If the number of points is too large, it may contain weak oscillation components that have not been fully decayed, resulting in an artificially high baseline. The selection of 10% of points can filter out high-frequency noise to the greatest extent and accurately reflect the final equilibrium position of the sensor after loading, providing a reliable zero-point reference for subsequent normalization calculations. In actual applications, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0032] At this point, regardless of how many times the circuit gain has been amplified, or whether it is real or not... Load or fake? After this step, the amplitude difference of the load is completely eliminated, and subsequent analysis will be based solely on the fluctuation pattern (i.e., frequency characteristics) of the normalized voltage signal.

[0033] M102: Based on the normalized voltage amplitude values ​​at each time point in the normalized voltage signal and the variation amplitude of the normalized voltage values ​​between adjacent time points, construct a voltage-variation amplitude coordinate set; based on the distance between all coordinate points in the voltage-variation amplitude coordinate set, apply the minimum spanning tree algorithm to construct a fully connected undirected graph, accumulate the weights of all edges in the fully connected undirected graph, record the resulting value as the baseline feature value, and obtain the ambient temperature at this time.

[0034] After obtaining the normalized voltage signal with amplitude interference eliminated, this step aims to extract the essential characteristic reflecting the physical quality of the load—the inherent oscillation frequency. Considering the severe resolution limitations of traditional frequency domain analysis methods (such as FFT) under low sampling rates and short signal lengths, this embodiment constructs a voltage-variance amplitude coordinate set based on the normalized voltage amplitude values ​​at each time point in the normalized voltage signal and the variation amplitude of the normalized voltage values ​​between adjacent time points. Based on the distances between all coordinate points in the voltage-variance amplitude coordinate set, a fully connected undirected graph is constructed using the minimum spanning tree algorithm. This transforms subtle frequency differences into significant geometric distribution differences. The weights of all edges in the fully connected undirected graph are accumulated, and the resulting value is recorded as the baseline feature value. The specific process is as follows: (1) Based on the normalized voltage amplitude at each time point in the normalized voltage signal and the change range of the normalized voltage amplitude between adjacent time points, construct a voltage-change range coordinate set.

[0035] Physics principles show that the smaller the load mass, the higher the natural frequency of the elastic system, and the faster the rebound oscillation. Given a fixed voltage amplitude of [-1, 1], the oscillation speed is directly reflected in the magnitude of the change in the normalized voltage amplitude per unit time. Therefore, this embodiment first constructs a voltage-variance amplitude coordinate set based on the normalized voltage amplitude at each moment in the normalized voltage signal and the variation amplitude of the normalized voltage amplitude between adjacent moments. Specifically: In the normalized voltage signal, the difference between the normalized voltage value at each time point and the previous time point is recorded as the change amplitude of the normalized voltage value at each time point. Furthermore, the normalized voltage amplitude at each time point and the corresponding change range of the normalized voltage amplitude are used to form two-dimensional coordinate points, and the two-dimensional coordinate points at all times in the voltage signal are used to form a voltage-change range coordinate set.

[0036] (2) Based on the distance between all coordinate points in the voltage-variation amplitude coordinate set, the minimum spanning tree algorithm is used to construct a fully connected undirected graph. The weights of all edges in the fully connected undirected graph are accumulated, and the resulting value is recorded as the baseline feature value. The ambient temperature at this time is then obtained.

[0037] To accurately quantify the sparsity of the voltage-variance amplitude coordinate set, this embodiment introduces the Minimum Spanning Tree (MST) algorithm from graph theory. Compared to simple statistical variance, the MST algorithm can capture the global topological structure of the voltage-variance amplitude coordinate set, has a better suppression effect on outlier noise, and has extremely low computational cost under small sample conditions, making it very suitable for running on low-cost microcontrollers. Therefore, this embodiment constructs a fully connected undirected graph based on the distance between all coordinate points in the voltage-variance amplitude coordinate set using the MST algorithm. The weights of all edges in the fully connected undirected graph are accumulated, and the resulting value is denoted as the baseline feature value, specifically: Each coordinate point in the voltage-variance coordinate set is treated as a vertex in the graph, and the Euclidean distance between any two vertices is calculated as the weight of the edge connecting the corresponding two vertices. Perform a minimum spanning tree traversal of all vertices, select the set of edges that can connect all vertices without forming a cycle, calculate the sum of the weights of all edges in each edge set, and form the undirected graph by connecting all edges in the edge set corresponding to the minimum weight sum as the fully connected undirected graph.

[0038] It should be noted that there are many commonly used minimum spanning tree algorithms. This embodiment uses Kruskal's algorithm. In practical applications, as other implementation methods, implementers may also use other minimum spanning tree algorithms according to specific circumstances. This embodiment does not impose any special restrictions.

[0039] The calculation process of Euclidean distance and the principle of Kruskal's algorithm are well-known techniques and will not be elaborated further.

[0040] Furthermore, the sum of the weights of all edges in the fully connected undirected graph is denoted as the baseline eigenvalue.

[0041] Based on the benchmark characteristic value, it can be understood that the benchmark characteristic value is used to characterize the inherent frequency characteristics and phase space distribution compactness of the rebound oscillation generated by the weight on the sensor. The calculation of the benchmark characteristic value is directly constrained by the spatial distance between the voltage-variation amplitude coordinate points. If the spatial distance between the voltage-variation amplitude coordinate points is shorter, that is, the coordinate points are more compactly distributed, the benchmark characteristic value is smaller, reflecting the physical state of slow rebound and gentle displacement change of the object, which means that the judgment standard of quality tends to be strict. Conversely, if the spatial distance between the voltage-variation amplitude coordinate points is larger, that is, the coordinate points are more sparsely distributed, the benchmark characteristic value is larger, reflecting the physical state of violent rebound and rapid displacement change of the object, which means that the recorded standard characteristics exhibit more active oscillation characteristics. This value serves as the anchor point for subsequent comparison, and its accuracy directly determines the sensitivity of the anti-cheating price-computing scale in identifying quality differences.

[0042] While calculating the baseline characteristic value, the onboard temperature sensor reads the ambient temperature. Furthermore, to prevent the baseline data from being maliciously tampered with in subsequent use, the processor writes the data pair (baseline characteristic value, ambient temperature) to the microcontroller's internal non-volatile secure memory area. Once written, this data pair is locked as the device's unique physical fingerprint under standard load, available only for subsequent verification and cannot be overwritten or modified.

[0043] Thus, this embodiment effectively eliminates the interference of circuit gain tampering on feature extraction by acquiring the voltage signal of the standard weight and performing gain-reduction normalization processing, retaining only the waveform time structure that reflects the essence of quality. It constructs a voltage-variation amplitude coordinate set using normalized voltage amplitude and variation amplitude, and employs the minimum spanning tree algorithm to quantize weak frequency differences into benchmark feature values, overcoming the problem of frequency domain analysis failure at low sampling rates and significantly improving the sensitivity of physical quality identification. Simultaneously, it constructs and locks a unique physical fingerprint based on ambient temperature, providing a precise and tamper-proof decision anchor point for subsequent verification, thereby achieving high-precision, interference-resistant anti-cheating benchmark establishment on low-cost hardware.

[0044] The price-computing scale verification module 102 is used to acquire the voltage signal containing the oscillating waveform of the current object to be measured and construct a voltage-variance amplitude coordinate set, and calculate the target feature value of the current object to be measured according to the calculation method of the reference feature value; acquire the ambient temperature in real time, and use the preset material temperature response coefficient to perform temperature drift correction on the reference feature value to obtain a dynamic reference threshold; calculate the deviation between the target feature value and the dynamic reference threshold to determine whether it is compliant to weigh the current object to be measured using the price-computing scale.

[0045] When the weighing scale initiates a calibration request in remote monitoring mode, it enters the real-time verification stage. To ensure the mathematical comparability of the verification results, the sampling and processing of the current unknown load in this embodiment must maintain strict isomorphism with the benchmark entry stage, that is, using the exact same sampling parameters and algorithm logic. Specifically, this embodiment collects the voltage signal containing the oscillating waveform of the current object under test and constructs a voltage-variance amplitude coordinate set. According to the calculation method of the benchmark feature value, it calculates the target feature value of the current object under test; it acquires the ambient temperature in real time, and uses a preset material temperature response coefficient to perform temperature drift correction on the benchmark feature value to obtain a dynamic reference threshold; it calculates the deviation between the target feature value and the dynamic reference threshold to determine whether it is compliant to weigh the current object under test using the weighing scale. The specific process is as follows: First, following the steps M101 and M102, the voltage signal containing the oscillating waveform of the current object under test is collected using the same method, and a voltage-variation amplitude coordinate set is constructed. Then, the target feature value of the current object under test is calculated according to the calculation method of the reference feature value.

[0046] Although the measured characteristic value can reflect the load mass, the physical properties of the sensor's elastic element (such as the elastic modulus) also drift with changes in ambient temperature. Typically, decreasing temperature causes metallic materials to harden (strength). (Increase). According to the frequency formula In load quality Stiffness Increasing the frequency will lead to The value increases, which in turn leads to the measured feature value. Naturally increasing. Without correction, normal operation at low temperatures may be misjudged as excessively frequent cheating. Therefore, this embodiment uses a preset material temperature response coefficient to correct the temperature drift of the reference characteristic value, obtaining a dynamic reference threshold, specifically: As one implementation method, in this embodiment, a dynamic reference threshold is used. The expression is: In the formula, Represents the baseline eigenvalue; This indicates the preset material temperature response coefficient; This represents the ambient temperature at which the baseline characteristic value was obtained; This indicates the temperature of the environment in which the object being measured is located, obtained using an onboard temperature sensor.

[0047] The calibration process for the material temperature response coefficient is as follows: the object to be tested and the weighing scale are placed in a temperature-controlled environment, the test characteristic values ​​of the object to be tested are calculated at different temperatures (such as -10°C, 20°C, 50°C), the test characteristic values ​​of the object to be tested at all temperatures are linearly fitted, and the slope of the fitted line is taken as the material temperature response coefficient.

[0048] This can be understood based on the dynamic reference threshold: Scenario 1 (Low Temperature Environment): If the temperature of the environment in which the object under test is located is lower than the ambient temperature when the baseline feature value was entered (i.e., the environment becomes colder), then the temperature difference term... A positive value will result in a calculated dynamic reference threshold. In the original benchmark eigenvalues The corresponding increase is based on the physical law: the characteristic value of a normal object naturally increases at low temperatures, so the judgment criteria should also be relaxed accordingly to avoid false alarms.

[0049] Scenario 2 (High Temperature Environment): If the temperature of the environment in which the object under test is located is higher than the ambient temperature when the baseline feature value was entered (i.e., the environment becomes hotter), the temperature difference term will be negative, which will lead to a negative dynamic reference threshold in the calculation. In the original benchmark eigenvalues Based on this, the value is adjusted accordingly to match the physical phenomena of material softening, frequency reduction, and characteristic value decrease at high temperatures.

[0050] Furthermore, this embodiment calculates the deviation between the measured feature value and the dynamic reference threshold to determine whether it is compliant to weigh the current object using a price-computing scale. Specifically: In this embodiment, the difference between the feature value to be measured and the dynamic reference threshold is divided by the dynamic reference threshold, and the result is taken as the deviation between the feature value to be measured and the dynamic reference threshold.

[0051] Based on the deviation, it can be understood that the deviation between the measured feature value and the dynamic reference threshold is used to quantify the degree to which the current measured object deviates from the standard physical characteristics and serves as the basis for the final judgment of whether it is a violation. The larger the deviation (i.e., cheating with a lighter object), the larger the difference between the measured feature value and the dynamic reference threshold, resulting in a larger deviation. This reflects that the physical state of the current object deviates significantly from the qualified standard, meaning that the pricing scale will be judged as a violation and calibration will be blocked. Conversely, if the deviation is smaller, it reflects that the physical state of the current object is highly in line with the standard expectation, meaning that the pricing scale will be judged as compliant and parameter modification will be allowed. This indicator intuitively measures the severity of cheating.

[0052] Furthermore, if the deviation exceeds the preset compliance judgment tolerance, the current weighing of the object under test is determined to be in violation of regulations, and the following actions are performed: The processor determines that the calibration request is illegal, maintains the write-protected state of the metering parameter storage area, physically blocks any attempt to modify the parameters, and at the same time, the processor displays a quality anomaly (Error-Mass) prompt locally and sends an alarm message to the cloud monitoring platform.

[0053] Conversely, if the deviation is less than or equal to the preset compliance judgment tolerance, the weighing of the current object under test is determined to be compliant, and the following steps are performed: The processor sends a status code indicating that physical verification has passed to the cloud monitoring platform. After receiving the digital signature authorization instruction from the platform, the processor temporarily removes the hardware or software write protection of the measurement parameter storage area, i.e., the EEPROM calibration sector, allowing the new linear coefficients generated in this calibration to be written. After the writing is completed, the processor immediately restores the write protection state and locks the parameters.

[0054] Preferably, the flowchart for determining compliance of the weighing of the object to be tested provided in this embodiment is as follows: Figure 2 As shown.

[0055] It should be noted that the preset tolerance for compliance judgment is set manually. In this embodiment, the preset tolerance for compliance judgment is 0.05. A 5% deviation can effectively accommodate the feature value dispersion caused by objective factors such as sensor manufacturing tolerance, ADC quantization error and environmental disturbances, ensuring that qualified products are not misjudged. At the same time, this threshold is strict enough to clearly distinguish the significant physical feature differences (usually far exceeding 5%) caused by cheating with lightweight objects from normal errors. Thus, while ensuring the system's compliance pass rate, it maximizes the sensitivity to detect cheating behavior. In actual applications, implementers can also set it according to specific circumstances. This embodiment does not impose any special restrictions.

[0056] Thus, this embodiment extracts the target feature values ​​of the object under test through isomorphic processing, and uses the material temperature response coefficient to dynamically correct the reference feature values ​​at temperature, constructing a dynamic reference threshold that adapts to environmental changes, effectively eliminating physical property interference caused by temperature drift; by calculating the deviation between the target feature value and the dynamic reference threshold, the physical difference between the current load under test and the standard weight can be accurately quantified. When the deviation exceeds the preset tolerance, it is automatically judged as a violation and parameter modification is blocked; otherwise, it is judged as compliant and authorized for update, thereby realizing remote automatic verification and security control of the anti-cheating pricing scale under complex working conditions.

[0057] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0058] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0059] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A remote monitoring system for an intelligent anti-cheating price-computing scale, characterized in that, The system includes: The benchmark input module for the price-computing scale is used to collect the voltage signal of the weight after it contacts the weighing pan of the price-computing scale and normalize it; based on the normalized voltage amplitude at each time point and the change range of the normalized voltage amplitude between adjacent time points in the normalized voltage signal, a voltage-change range coordinate set is constructed; based on the distance between all coordinate points in the voltage-change range coordinate set, a fully connected undirected graph is constructed using the minimum spanning tree algorithm, the weights of all edges in the fully connected undirected graph are accumulated, the resulting value is recorded as the benchmark feature value, and the ambient temperature at this time is obtained; The price-computing scale verification module is used to acquire the voltage signal containing the oscillating waveform of the current object to be measured and construct a voltage-variance amplitude coordinate set, and calculate the target feature value of the current object to be measured according to the calculation method of the reference feature value; acquire the ambient temperature in real time, and use the preset material temperature response coefficient to perform temperature drift correction on the reference feature value to obtain a dynamic reference threshold; calculate the deviation between the target feature value and the dynamic reference threshold to determine whether it is compliant to use the price-computing scale to weigh the current object to be measured. The application of the minimum spanning tree algorithm to construct a fully connected undirected graph includes: Each coordinate point in the voltage-variance coordinate set is treated as a vertex in the graph, and the Euclidean distance between any two vertices is calculated as the weight of the edge connecting the corresponding two vertices. The minimum spanning tree algorithm is executed, traversing all vertices and selecting the set of edges that can connect all vertices without forming a cycle. The sum of the weights of all edges in each edge set is calculated, and the undirected graph formed by connecting all edges in the edge set corresponding to the minimum weight sum is taken as the fully connected undirected graph.

2. The remote monitoring system for an intelligent anti-cheating price-computing scale according to claim 1, characterized in that, The process of acquiring the voltage signal of the weights includes: After the weights come into contact with the weighing pan of the price-computing scale, the voltage signal is continuously detected and collected. The backward differential value of the voltage amplitude at each moment is calculated. When the backward differential value is detected to change from a positive value to a negative value for the first time, the corresponding moment is taken as the starting moment, and the signal within the preset time period is continuously collected as the voltage signal of the weights.

3. The remote monitoring system for an intelligent anti-cheating price-computing scale according to claim 1, characterized in that, The process of normalizing a voltage signal is as follows: Calculate the mean of a preset proportion of voltage amplitudes at the end of the voltage signal as the steady-state baseline. Calculate the absolute difference between all voltage amplitudes in the voltage signal and the steady-state baseline. Select the largest absolute difference and use the ratio of all absolute differences to the largest absolute difference as the normalized value of the corresponding voltage amplitude.

4. The remote monitoring system for an intelligent anti-cheating price-computing scale according to claim 1, characterized in that, The process of constructing the voltage-variation amplitude coordinate set is as follows: In the normalized voltage signal, the normalized voltage amplitude at each time point and the corresponding change range of the normalized voltage amplitude are used to form two-dimensional coordinate points. The two-dimensional coordinate points at all times in the voltage signal form a voltage-change range coordinate set.

5. The remote monitoring system for an intelligent anti-cheating price-computing scale according to claim 4, characterized in that, The variation range of the voltage normalized value is the difference between the voltage normalized value at each time point and the voltage normalized value at the adjacent previous time point.

6. The remote monitoring system for an intelligent anti-cheating price-computing scale according to claim 1, characterized in that, Dynamic reference threshold The expression is: In the formula, Represents the baseline eigenvalue; This indicates the preset material temperature response coefficient; This represents the ambient temperature at which the baseline characteristic value was obtained; This indicates the temperature of the environment in which the object being measured is located.

7. The remote monitoring system for an intelligent anti-cheating price-computing scale according to claim 6, characterized in that, The calibration steps for the material temperature response coefficient are as follows: The object to be tested and the price-computing scale are placed in a temperature-controlled environment. The measured characteristic values ​​of the object to be tested at different temperatures are calculated. The measured characteristic values ​​of the object to be tested at all temperatures are linearly fitted, and the slope of the fitted line is used as the material temperature response coefficient.

8. The remote monitoring system for an intelligent anti-cheating price-computing scale according to claim 1, characterized in that, The deviation between the measured feature value and the dynamic reference threshold is the result of the difference between the measured feature value and the dynamic reference threshold divided by the dynamic reference threshold.

9. The remote monitoring system for an intelligent anti-cheating price-computing scale according to claim 1, characterized in that, The determination of whether the use of a price-computing scale to weigh the object to be measured is compliant includes: If the deviation is greater than the preset allowable deviation for compliance judgment, the current object to be measured is judged to be weighed in violation of regulations; otherwise, the current object to be measured is judged to be weighed in compliance with regulations.

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