Intelligent monitoring system integrating chips and sensors

Abstract

This invention provides an intelligent monitoring system integrating chips and sensors, relating to the field of intelligent monitoring technology. The system includes a chip module comprising an adaptive unit, a chip monitoring unit, a sensor monitoring unit, and a redundancy unit. The adaptive unit analyzes environmental information from the sensor to obtain repair adjustment coefficients; sets a set of repair adjustment parameters, including several repair adjustment parameters; and matches the repair adjustment coefficients with the set repair adjustment parameter set to obtain successfully matched repair adjustment parameters. Through the analysis and adjustment of the adaptive unit, this invention can dynamically adjust the sensor's operating parameters according to changes in environmental information, ensuring accurate data collection in different environments, improving monitoring accuracy and data quality. Combined with the presence of the redundancy unit, it can quickly switch to backup components when a sensor or chip fails, thereby reducing system downtime and ensuring the continuity of monitoring tasks.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring system integrating chips and sensors. Background Technology

[0002] With the continuous development and innovation of semiconductor technology and sensors, the performance of chips is becoming increasingly powerful, with continuous improvement in processing power, storage capacity and integration. New sensors are also able to detect various physical and chemical quantities more accurately, and have higher sensitivity, stability and reliability, enabling chips to better process and analyze large amounts of data from sensors, providing a powerful computing core for intelligent monitoring systems.

[0003] However, in practical use, chips and sensors may malfunction or experience performance degradation, affecting the accuracy and reliability of the monitoring system. Furthermore, different environmental conditions can also impact sensor operation, requiring timely adjustment of sensor operating parameters to ensure monitoring effectiveness. Therefore, we propose an intelligent monitoring system integrating chips and sensors to address these issues. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the present invention provides an intelligent monitoring system integrating chips and sensors to solve the problems mentioned in the background art.

[0005] The objective of this invention can be achieved through the following technical solution: including a sensor monitoring module and a chip module; the sensor monitoring module is used to collect environmental information and operating status information of the sensor;

[0006] The chip module includes an adaptive unit, a chip monitoring unit, a sensor monitoring unit, and a redundancy unit;

[0007] The adaptive unit is used to perform environmental anomaly analysis on the sensor's environmental information to obtain the normal influence value of any environmental parameter in the environmental information; then, it analyzes the fluctuation of any environmental parameter to obtain the parametric influence value of the environmental parameter; the normal influence value and parametric influence value of all environmental parameters in the environmental information are weighted to obtain the repair adjustment coefficient; a repair adjustment parameter group is set, including several repair adjustment parameters, such as sensitivity and sampling frequency; the repair adjustment coefficient is matched with the set repair adjustment parameter group to obtain the successfully matched repair adjustment parameters; and the sensor's operating parameters are adjusted according to the repair adjustment parameters.

[0008] The chip monitoring unit is used to monitor the chip's operating status information and perform status analysis by combining the chip's operating status information with historical records to obtain the chip fault assessment value; a fault assessment threshold is set for the chip. If the chip fault assessment value is greater than or equal to the fault assessment threshold, a chip fault signal is generated, indicating that the chip has a fault; conversely, if the chip fault assessment value is less than the fault assessment threshold, it indicates that the chip is in a normal state.

[0009] The sensing monitoring unit is used to monitor the working information of the sensor, perform anomaly analysis on the working information of the sensor, and obtain the sensing symmetry value; a sensing anomaly threshold is set. If the sensing symmetry value is greater than or equal to the sensing anomaly threshold, a sensing anomaly signal is generated; otherwise, if the sensing symmetry value is less than the sensing anomaly threshold, it indicates that the sensor is in a normal state.

[0010] The redundancy unit is used to receive corresponding signaling to perform corresponding operations. When a chip fault signaling or sensor abnormality signaling is received, the chip or sensor is switched to the corresponding redundant component. The redundant component includes the corresponding spare chip and spare sensor.

[0011] As a preferred method, environmental anomaly analysis is performed on the sensor's environmental information, specifically as follows:

[0012] According to the sensor environment standard specifications, set the standard value of any environmental parameter in the sensor environment information, calculate the difference between the environmental parameter value in the sensor environment information and the standard value of the environmental parameter, and obtain the standard difference value of the environmental parameter.

[0013] Set an environmental interference time zone, and calculate the mean and standard deviation of the standard deviation of any environmental parameter within the environmental interference time zone to obtain the mean and standard deviation fluctuation of the corresponding environmental parameter.

[0014] The disturbance range is obtained by calculating the difference between the maximum and minimum standard deviations within the environmental disturbance time zone;

[0015] The standard deviation, mean, and fluctuation of the environmental parameters are weighted and processed with the disturbance range to obtain the environmental normal influence value.

[0016] As a preferred approach, the fluctuation of any environmental parameter within the environmental interference time zone is analyzed, specifically as follows:

[0017] A line graph of environmental parameter changes is established. The value of any environmental parameter within the environmental interference time zone and its acquisition time are input into the line graph. The position of the environmental parameter value in the line graph is marked as a loop parameter point. Connecting adjacent loop parameter points yields a loop parameter line. The slope of the loop parameter line is calculated. A slope change threshold is set. The absolute value of any loop parameter slope is compared with the slope change threshold. If the absolute value of the loop parameter slope is greater than or equal to the slope change threshold, the loop parameter line corresponding to the slope is marked as an anomaly parameter line.

[0018] The number of anomaly lines within the time zone of environmental interference is recorded and marked as the anomaly line number.

[0019] Mark the midpoint of the anomaly parametric line as the anomaly parametric point, and connect adjacent anomaly parametric points to obtain the anomaly parametric line; calculate the length of the anomaly parametric line to obtain the anomaly parametric length value; calculate the standard deviation of the anomaly parametric length value within the environmental interference time zone to obtain the anomaly parametric fluctuation value;

[0020] The variation line number and the parametric fluctuation value are weighted to obtain the parametric influence value.

[0021] As a preferred approach, the chip's operating status information is combined with historical records for status analysis, specifically as follows:

[0022] Obtain chip operating status information, including chip temperature, power consumption, clock frequency, voltage fluctuation, and data transmission rate;

[0023] Set the abnormal trigger threshold for any state parameter in the chip's working state according to the chip's task requirements, and subtract the corresponding state parameter value from the abnormal trigger threshold to obtain the trigger distance value.

[0024] Set the chip monitoring time zone, and calculate the mean and standard deviation of the trigger distance value of any state parameter within the chip monitoring time zone to obtain the trigger mean and trigger fluctuation value;

[0025] The trigger influence value is obtained by weighting the trigger distance value, trigger mean value, and trigger fluctuation value of the state parameters; the core value is obtained by weighting the trigger influence values ​​of all state parameters in the chip's working state information.

[0026] An impact analysis was performed on the historical records of chip failures to obtain the fault mean square value.

[0027] The parametric influence value of any environmental parameter in the environmental information is extracted from the self-healing unit, and the parametric influence values ​​of all environmental parameters in the environmental information are weighted to obtain the core-ring influence value.

[0028] The chip fault assessment value is obtained by weighting the core value, core ring influence value, and fault mean value.

[0029] As a preferred approach, an impact analysis is performed on the historical records of chip malfunctions, specifically:

[0030] A chip monitoring line graph is established. The trigger distance value of the status parameter in the chip monitoring time zone and its acquisition time are input into the chip monitoring line graph. The position of the trigger distance value in the chip monitoring line graph is marked as a distance point. Adjacent distance points are connected to obtain distance lines. All distance lines in the chip monitoring line graph constitute the change line corresponding to the chip status parameter.

[0031] The chip monitoring line chart of any chip monitoring time zone identified from historical records is marked as a historical chip monitoring line chart; the change line of the chip's state parameters extracted from the historical chip monitoring line chart before the chip failure is marked as a historical change line chart.

[0032] The similarity between the change line corresponding to any chip state parameter and its corresponding historical change line is calculated to obtain the fault line similarity value of the state parameter; the fault line similarity values ​​of all state parameters in the working state information are weighted to obtain the state fault comprehensive similarity value.

[0033] Select the state fault similarity value with the largest number of settings in the historical record and calculate the average value of the fault.

[0034] As a preferred approach, anomaly analysis is performed on the sensor's operational information, specifically as follows:

[0035] Set a normal reference value for any parameter in the sensor's operating information. Subtract the corresponding parameter value from the normal reference value to obtain the sensor parameter difference. Set a sensor monitoring time zone. Calculate the mean and standard deviation of the sensor parameter difference for any parameter within the monitoring time zone to obtain the mean and fluctuation values ​​of the sensor variance. Weight the sensor parameter difference, mean, and fluctuation values ​​to obtain the sensor parametric value. Weight the sensor parametric values ​​of all parameters in the sensor's operating information to obtain the sensor composite value.

[0036] Preferably, the chip module also includes a dispatch module; the dispatch module is used to receive chip fault signals or sensor abnormality signals to trigger the personnel dispatch analysis corresponding to the signal to obtain the corresponding maintenance personnel; the chip fault signal or sensor abnormality signal and the corresponding chip or sensor number and location are sent to the maintenance personnel's smart terminal; after receiving the corresponding signal and the corresponding chip or sensor number and location through the smart terminal, the maintenance personnel perform maintenance on the chip or sensor.

[0037] As a preferred method, the personnel allocation analysis corresponding to the trigger signaling is used to obtain the corresponding maintenance personnel. Specifically, this involves: obtaining the maintenance personnel for the chip or sensor corresponding to the signaling; and sending information acquisition instructions to the maintenance personnel's smart terminals to obtain the maintenance personnel's current location, monthly maintenance count, start date, and total maintenance count.

[0038] The maintenance interval is calculated by measuring the distance difference between the current location of the maintenance personnel and the location of the chip or sensor; the monthly routine maintenance frequency is set, and the monthly repair frequency is subtracted from the monthly routine maintenance frequency to obtain the monthly maintenance difference; the length of time between the start date and the current date is calculated to obtain the start date; the daily average maintenance frequency is obtained by dividing the total number of maintenance times by the start date.

[0039] The maintenance interval, monthly maintenance frequency, and daily maintenance average are weighted to obtain the maintenance adjustment value; the maintenance personnel with the highest maintenance adjustment value are marked as repair personnel.

[0040] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0041] 1. This invention, through the analysis and adjustment of the adaptive unit, can dynamically adjust the sensor operating parameters according to changes in environmental information, ensuring accurate data collection in different environments, improving monitoring accuracy and data quality. Combined with the presence of redundant units, it can quickly switch to backup components when the sensor or chip fails, thereby reducing system downtime and ensuring the continuity of monitoring tasks.

[0042] 2. By analyzing the working status of the chip and sensor, this invention can promptly determine whether the chip or sensor is faulty. The timely fault detection capability greatly improves reliability and reduces the risk of data loss or error caused by faults. Attached Figure Description

[0043] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0044] Figure 1 This is a schematic diagram of the intelligent monitoring system integrating the chip and sensor of this invention. Detailed Implementation

[0045] The technical solutions of 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.

[0046] Please see Figure 1As shown, the intelligent monitoring system integrating chips and sensors includes a sensor monitoring module and a chip module; the sensor monitoring module is used to collect environmental information and operating status information of the sensors.

[0047] The chip module includes an adaptive unit, a chip monitoring unit, a sensor monitoring unit, and a redundancy unit;

[0048] The adaptive unit is used to perform environmental anomaly analysis on the sensor's environmental information to obtain the normal environmental influence value HC of any environmental parameter in the environmental information; then, it analyzes the fluctuation of any environmental parameter to obtain the parametric influence value YB of the environmental parameter; finally, it weights the normal environmental influence value and the parametric influence value of all environmental parameters in the environmental information to obtain the repair adjustment coefficient J, expressed by the formula: Where i represents the number of the environmental parameter in the environmental information, N represents the total number of all environmental parameters in the environmental information, iHC and iYB represent the conventional influence value and the non-conforming influence value of the environmental parameter i, respectively, and ia1 and ia2 represent the weights of the conventional influence value and the non-conforming influence value of the environmental parameter i, respectively.

[0049] Set a set of repair and adjustment parameters, including several repair and adjustment parameters such as sensitivity and sampling frequency; match the repair and adjustment coefficients with the set set of repair and adjustment parameters to obtain successfully matched repair and adjustment parameters; adjust the sensor operating parameters according to the repair and adjustment parameters.

[0050] The chip monitoring unit is used to monitor the chip's operating status information and perform status analysis by combining the chip's operating status information with historical records to obtain the chip fault assessment value; a fault assessment threshold is set for the chip. If the chip fault assessment value is greater than or equal to the fault assessment threshold, a chip fault signal is generated, indicating that the chip has a fault; conversely, if the chip fault assessment value is less than the fault assessment threshold, it indicates that the chip is in a normal state.

[0051] The sensing monitoring unit is used to monitor the working information of the sensor, perform anomaly analysis on the working information of the sensor, and obtain the sensing symmetry value; a sensing anomaly threshold is set. If the sensing symmetry value is greater than or equal to the sensing anomaly threshold, a sensing anomaly signal is generated; otherwise, if the sensing symmetry value is less than the sensing anomaly threshold, it indicates that the sensor is in a normal state.

[0052] The redundancy unit is used to receive corresponding signaling to perform corresponding operations. When a chip fault signaling or sensor abnormality signaling is received, the chip or sensor is switched to the corresponding redundant component. The redundant component includes the corresponding spare chip and spare sensor.

[0053] In this application, environmental anomaly analysis is performed on the sensor's environmental information, specifically as follows:

[0054] According to the sensor environmental standard specifications, the standard value of any environmental parameter in the sensor environmental information is set. The difference between the environmental parameter value in the sensor environmental information and the standard value of the environmental parameter is calculated to obtain the standard deviation value HC1 corresponding to the environmental parameter. The standard deviation value intuitively reflects the degree of deviation of the current environmental parameter from the standard state. For example, for a temperature sensor, if the standard operating temperature is 25℃ and the actual measured temperature is 30℃, then the standard deviation value HC1 is 5℃, indicating that the current temperature is 5℃ higher than the standard temperature.

[0055] Set an environmental interference time zone, and calculate the mean and standard deviation of the standard deviation of any environmental parameter within the environmental interference time zone to obtain the mean standard deviation HC2 and the standard deviation fluctuation value HC3 corresponding to the environmental parameter. The mean standard deviation HC2 reflects the average level of the environmental parameter's deviation from the standard value within the time zone, while the standard deviation fluctuation value HC3 reflects the degree of dispersion of this deviation. For example, if the standard deviation of temperature fluctuates greatly around a certain average value over a period of time, it indicates that the environmental temperature is unstable.

[0056] The disturbance range value HC4 is calculated by taking the difference between the maximum and minimum standard deviations within the environmental disturbance time zone. The disturbance range value reflects the extreme changes in environmental parameters within that time zone. For example, if the maximum standard deviation is 10℃ and the minimum standard deviation is -5℃, then the disturbance range value HC4 is 15℃, indicating that the environmental temperature changes significantly during this period.

[0057] The standard deviation, mean, fluctuation, and perturbation range of the environmental parameters are weighted and processed using the formula: HC = HC1×h1 + HC2×h2 + HC3×h3 + HC4×h4, to obtain the environmental normal influence value HC; where h1, h2, h3, and h4 represent the weights corresponding to the standard deviation, mean, fluctuation, and perturbation range of the environmental parameters, respectively.

[0058] In this application, the fluctuation of any environmental parameter within the environmental interference time zone is analyzed, specifically as follows:

[0059] An environmental parameter variation line graph is established. The value of any environmental parameter within the environmental interference time zone and its acquisition time are input into the environmental parameter variation line graph. The position of the environmental parameter value in the environmental parameter variation line graph is marked as a loop parameter point. Connecting adjacent loop parameter points yields a loop parameter line. The slope of the loop parameter line is calculated. A slope change threshold is set, and the absolute value of any loop parameter slope is compared with the slope change threshold. If the absolute value of the loop parameter slope is greater than or equal to the slope change threshold, the loop parameter line corresponding to the slope is marked as an anomaly line. The environmental parameter variation line graph visually displays the changes of environmental parameters over time. The loop parameter slope reflects the rate of change of environmental parameters between adjacent time points. For example, if the temperature rises rapidly over a period of time, the corresponding loop parameter slope will be relatively large. An anomaly line indicates that the environmental parameter has undergone a significant change during this period. For example, if the slope change threshold is set to a certain value, when the rate of temperature change exceeds this threshold, the corresponding loop parameter line is marked as an anomaly line, indicating that abnormal temperature fluctuations may have occurred.

[0060] The number of anomaly lines recorded within the environmental disturbance time zone is marked as the anomaly line number YB1; the anomaly line number reflects the frequency of significant changes in environmental parameters within the environmental disturbance time zone.

[0061] Mark the midpoint of the anomaly parameter line as the anomaly parameter point, and connect adjacent anomaly parameter points to obtain the anomaly line; calculate the length of the anomaly line to obtain the anomaly length value; calculate the standard deviation of the anomaly length value within the environmental disturbance time zone to obtain the anomaly fluctuation value YB2; the anomaly length value can reflect the duration or magnitude of the anomaly. For example, if the anomaly parameter line is long, it indicates that the abnormal change of the environmental parameter has lasted for a long time.

[0062] The variation number and the parametric fluctuation value are weighted and processed by the formula: YB=YB1×b1+YB1×b1, to obtain the parametric influence value YB; where b1 and b2 represent the weights corresponding to the variation number and the parametric fluctuation value, respectively.

[0063] In this application, the chip's operating state information is combined with historical records for state analysis, specifically as follows:

[0064] Obtain chip operating status information, including chip temperature, power consumption, clock frequency, voltage fluctuation, and data transmission rate;

[0065] Based on the chip's task requirements, set the abnormal trigger threshold for any state parameter in the chip's operating state. Subtract the corresponding state parameter value from the abnormal trigger threshold to obtain the trigger distance value CF1. The trigger distance value represents the degree of proximity between the current state parameter and the abnormal state.

[0066] Set a chip monitoring time zone, and calculate the mean and standard deviation of the trigger distance value of any state parameter within the chip monitoring time zone to obtain the trigger mean CF2 and trigger fluctuation value CF3. The trigger mean reflects the average closeness of the state parameter to the abnormal state within the chip monitoring time zone, while the trigger fluctuation value reflects the dispersion of the data. For example, if the trigger fluctuation value is large, it indicates that the state parameter is unstable within this time period and there may be potential problems.

[0067] The trigger influence value CF is obtained by weighting the trigger distance, trigger mean, and trigger fluctuation value of the state parameters, expressed by the formula: CF = CF1 × f1 + CF2 × f2 + CF3 × f3; where f1, f2, and f3 represent the weights corresponding to the trigger distance, trigger mean, and trigger fluctuation value of the state parameters, respectively. The core value XZ is obtained by weighting the trigger influence values ​​of all state parameters in the chip's operating state information, expressed by the formula: Where k represents the number of the state parameter in the chip's operating state information, M represents the total number of state parameters in the chip's operating state information, and kCF and kc represent the trigger influence value and corresponding weight of state parameter k in the chip's operating state information, respectively.

[0068] An impact analysis was performed on the historical records of chip failures to obtain the fault mean square value S.

[0069] The parametric influence value YB of any environmental parameter within the environmental information is extracted from the self-healing unit. The core-ring influence value Y is obtained by weighting all the parametric influence values ​​of environmental parameters within the environmental information. The formula is as follows: Where is represents the parametric influence value of environmental parameter i within the environmental information;

[0070] The chip fault assessment value E is obtained by weighting the core value XZ, the core-ring influence value Y, and the fault mean square value S. The formula is: E = XZ × e1 + Y × e2 + S × e3; where e1, e2, and e3 represent the weights of the core value, the core-ring influence value, and the fault mean square value, respectively.

[0071] In this application, an impact analysis is performed on the historical records of chip malfunctions, specifically as follows:

[0072] A chip monitoring line graph is established. The trigger distance value of the status parameter in the chip monitoring time zone and its acquisition time are input into the chip monitoring line graph. The position of the trigger distance value in the chip monitoring line graph is marked as a distance point. Adjacent distance points are connected to obtain distance lines. All distance lines in the chip monitoring line graph constitute the change line corresponding to the chip status parameter.

[0073] The chip monitoring line chart of any chip monitoring time zone identified from historical records is marked as a historical chip monitoring line chart; the change line of the chip's state parameters extracted from the historical chip monitoring line chart before the chip failure is marked as a historical change line chart.

[0074] The similarity value XS1 of the fault line similarity is obtained by calculating the similarity between the change line corresponding to any chip state parameter and its corresponding historical change line; the state fault similarity value XS is obtained by weighting the fault line similarity values ​​of all state parameters in the working state information, as expressed by the formula: Wherein, kXS1 and kt represent the fault piecewise similarity value and corresponding weight of the state parameter k in the working status information, respectively;

[0075] The average fault value S is obtained by selecting the state fault with the largest number of set values ​​in the historical records and calculating the mean value.

[0076] It should be noted that by analyzing the similarity between historical fault records and the current state, potential chip faults can be predicted. For example, if the curve of change in the current state parameters is very similar to the curve of change when the chip had a fault in the historical records, then a fault may be about to occur. The average similarity value of faults takes into account multiple historical fault scenarios and can more accurately reflect the average similarity of chip faults.

[0077] In this application, anomaly analysis is performed on the sensor's operating information, specifically as follows:

[0078] Set a normal reference value for any parameter in the sensor's operating information. Subtract the corresponding parameter value from the normal reference value to obtain the sensor parameter difference. Set a sensor monitoring time zone. Calculate the mean and standard deviation of the sensor parameter difference for any parameter within the monitoring time zone to obtain the mean and fluctuation values ​​of the sensor variance. Weight the sensor parameter difference, mean, and fluctuation values ​​to obtain the sensor parametric value. Weight the sensor parametric values ​​of all parameters in the sensor's operating information to obtain the sensor composite value.

[0079] In this application, the chip module also includes a dispatch module; the dispatch module is used to receive chip fault signaling or sensor abnormality signaling to trigger the corresponding personnel dispatch analysis to obtain the corresponding maintenance personnel; and send the chip fault signaling or sensor abnormality signaling and the corresponding chip or sensor number and location to the maintenance personnel's smart terminal; after receiving the corresponding signaling and the corresponding chip or sensor number and location through the smart terminal, the maintenance personnel perform maintenance on the chip or sensor.

[0080] It should be noted that when a chip or sensor malfunctions, the dispatch module can quickly receive chip fault signals or sensor abnormality signals and immediately trigger personnel dispatch analysis, shortening the time from the occurrence of the fault to the start of maintenance, improving the response speed to the fault, realizing the automated operation of the dispatch module, optimizing the intelligent management of the maintenance process, and reducing manual intervention and management costs.

[0081] In this application, the personnel allocation analysis corresponding to the triggering signaling is used to obtain the corresponding maintenance personnel, specifically: obtaining the maintenance personnel corresponding to the chip or sensor of the signaling; sending information acquisition instructions to the maintenance personnel's smart terminal to obtain the maintenance personnel's current location, monthly maintenance count, start date, and total maintenance count;

[0082] The maintenance distance is calculated by taking the distance difference between the current location of the maintenance personnel and the location of the chip or sensor; the smaller the maintenance distance, the shorter the time required for the maintenance personnel to reach the fault location.

[0083] Set the monthly routine maintenance frequency, and subtract the monthly repair frequency from the monthly routine maintenance frequency to obtain the monthly maintenance difference. The monthly maintenance difference reflects the difference between the maintenance personnel's performance in the current month and the routine maintenance frequency. The larger the difference, the fewer times the maintenance personnel are selected as repair personnel in that month.

[0084] The length of employment is calculated by dividing the time between the start date and the current date; the average daily maintenance is calculated by dividing the total number of maintenance visits by the length of employment; the average daily maintenance can measure the work efficiency and experience accumulation speed of maintenance personnel.

[0085] The maintenance interval, monthly maintenance frequency, and daily maintenance average are weighted to obtain the maintenance adjustment value; the maintenance personnel with the highest maintenance adjustment value are marked as repair personnel.

[0086] It should be noted that intelligent management of maintenance tasks is achieved through personnel allocation analysis. By analyzing maintenance personnel information, calculating relevant indicators, and determining maintenance personnel, manual intervention can be reduced and management efficiency can be improved.

[0087] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring system integrating chips and sensors, characterized in that, It includes a sensor monitoring module and a chip module; the sensor monitoring module is used to collect environmental information and operating status information of the sensor. The chip module includes an adaptive unit, a chip monitoring unit, a sensor monitoring unit, and a redundancy unit; The adaptive unit is used to perform environmental anomaly analysis on the sensor's environmental information to obtain the environmental normal influence value of any environmental parameter in the environmental information, specifically: According to the sensor environment standard specifications, set the standard value of any environmental parameter in the sensor environment information, calculate the difference between the environmental parameter value in the sensor environment information and the standard value of the environmental parameter, and obtain the standard difference value of the environmental parameter. Set an environmental interference time zone, and calculate the mean and standard deviation of the standard deviation of any environmental parameter within the environmental interference time zone to obtain the mean and standard deviation fluctuation of the corresponding environmental parameter. The disturbance range is obtained by calculating the difference between the maximum and minimum standard deviations within the environmental disturbance time zone; The standard deviation, mean, and fluctuation of the standard deviation, along with the disturbance range, are weighted to obtain the environmental parameter's normal influence value. Then, the fluctuation of any environmental parameter is analyzed to obtain the parameter's parametric influence value, specifically: Establish a line graph of environmental parameter changes. Input the value of any environmental parameter in the environmental interference time zone and its acquisition time into the line graph of environmental parameter changes. Mark the position of the environmental parameter value in the line graph of environmental parameter changes as a loop parameter point. Connect adjacent loop parameter points to obtain a loop parameter line. The slope of the cyclic parameter line is calculated to obtain the cyclic parameter slope; a slope change threshold is set, and the absolute value of any cyclic parameter slope is compared with the slope change threshold. If the absolute value of the cyclic parameter slope is greater than or equal to the slope change threshold, the cyclic parameter line corresponding to the cyclic parameter slope is marked as an anomaly parameter line. The number of anomaly lines within the time zone of environmental interference is recorded and marked as the anomaly line number. Mark the midpoint of the anomaly parametric line as the anomaly parametric point, and connect adjacent anomaly parametric points to obtain the anomaly parametric line; calculate the length of the anomaly parametric line to obtain the anomaly parametric length value; calculate the standard deviation of the anomaly parametric length value within the environmental interference time zone to obtain the anomaly parametric fluctuation value; The variation line number and the parametric fluctuation value are weighted to obtain the parametric influence value; The remediation adjustment coefficient is obtained by weighting the conventional and variable impact values ​​of all environmental parameters in the environmental information. Set a set of repair and adjustment parameters, including several repair and adjustment parameters such as sensitivity and sampling frequency; match the repair and adjustment coefficients with the set set of repair and adjustment parameters to obtain successfully matched repair and adjustment parameters; adjust the sensor operating parameters according to the repair and adjustment parameters. The chip monitoring unit is used to monitor the chip's operating status information and perform status analysis by combining the chip's operating status information with historical records to obtain the chip fault assessment value; a fault assessment threshold is set for the chip. If the chip fault assessment value is greater than or equal to the fault assessment threshold, a chip fault signal is generated, indicating that the chip has a fault; conversely, if the chip fault assessment value is less than the fault assessment threshold, it indicates that the chip is in a normal state. The sensing and monitoring unit is used to monitor the operating information of the sensors and perform anomaly analysis on the sensor operating information, specifically: Set a normal reference value for any parameter in the sensor's working information, and subtract the corresponding parameter value from the normal reference value to obtain the sensor parameter difference; set a sensor monitoring time zone, and calculate the mean and standard deviation of the sensor parameter difference for any parameter within the sensor monitoring time zone to obtain the sensor variance mean and sensor variance fluctuation value. The sensor parametric value is obtained by weighting the sensor parameter difference, the mean value of sensor parametric difference, and the value of sensor parametric fluctuation; the sensor composite value is obtained by weighting the sensor parametric values ​​of all parameters in the sensor working information. Set a sensor anomaly threshold. If the sensor symmetry value is greater than or equal to the sensor anomaly threshold, a sensor anomaly signal is generated. Conversely, if the sensor symmetry value is less than the sensor anomaly threshold, it indicates that the sensor is in normal condition. The redundancy unit is used to receive corresponding signaling to perform corresponding operations. When a chip fault signaling or sensor abnormality signaling is received, the chip or sensor is switched to the corresponding redundant component. The redundant component includes the corresponding spare chip and spare sensor.

2. The intelligent monitoring system integrating chip and sensor according to claim 1, characterized in that, The chip's operating status information is combined with historical records for status analysis, specifically as follows: Obtain chip operating status information, including chip temperature, power consumption, clock frequency, voltage fluctuation, and data transmission rate; Set the abnormal trigger threshold for any state parameter in the chip's working state according to the chip's task requirements, and subtract the corresponding state parameter value from the abnormal trigger threshold to obtain the trigger distance value. Set the chip monitoring time zone, and calculate the mean and standard deviation of the trigger distance value of any state parameter within the chip monitoring time zone to obtain the trigger mean and trigger fluctuation value; The trigger influence value is obtained by weighting the trigger distance value, trigger mean value, and trigger fluctuation value of the state parameters; the core value is obtained by weighting the trigger influence values ​​of all state parameters in the chip's working state information. An impact analysis was performed on the historical records of chip failures to obtain the fault mean square value. The parametric influence value of any environmental parameter in the environmental information is extracted from the self-healing unit, and the parametric influence values ​​of all environmental parameters in the environmental information are weighted to obtain the core-ring influence value. The chip fault assessment value is obtained by weighting the core value, core ring influence value, and fault mean value.

3. The intelligent monitoring system integrating chip and sensor according to claim 2, characterized in that, An impact analysis was performed on the historical records of chip failures, specifically: A chip monitoring line graph is established. The trigger distance value of the status parameter in the chip monitoring time zone and its acquisition time are input into the chip monitoring line graph. The position of the trigger distance value in the chip monitoring line graph is marked as a distance point. Adjacent distance points are connected to obtain distance lines. All distance lines in the chip monitoring line graph constitute the change line corresponding to the chip status parameter. The chip monitoring line chart of any chip monitoring time zone identified from historical records is marked as a historical chip monitoring line chart; the change line of the chip's state parameters extracted from the historical chip monitoring line chart before the chip failure is marked as a historical change line chart. The similarity value of the fault line of any chip state parameter is obtained by performing a similarity calculation between the change line corresponding to the historical change line. The state-fault similarity value is obtained by weighting the fault piecewise similarity values ​​of all state parameters in the working status information. Select the state fault similarity value with the largest number of settings in the historical record and calculate the average value of the fault.

4. The intelligent monitoring system integrating chip and sensor according to claim 1, characterized in that, The chip module also includes a dispatch module; the dispatch module is used to receive chip fault signals or sensor abnormality signals to trigger the corresponding personnel dispatch analysis to obtain the corresponding maintenance personnel; the chip fault signal or sensor abnormality signal and the corresponding chip or sensor number and location are sent to the maintenance personnel's smart terminal; after receiving the corresponding signal and the corresponding chip or sensor number and location through the smart terminal, the maintenance personnel perform maintenance on the chip or sensor.

5. The intelligent monitoring system integrating chip and sensor according to claim 4, characterized in that, The personnel allocation analysis corresponding to the triggered signaling is used to obtain the corresponding maintenance personnel. Specifically, this involves: obtaining the maintenance personnel for the chip or sensor corresponding to the signaling; sending information acquisition instructions to the maintenance personnel's smart terminal to obtain the maintenance personnel's current location, monthly maintenance count, start date, and total maintenance count; The maintenance distance is calculated by taking the distance difference between the current location of the maintenance personnel and the location of the chip or sensor. Set the monthly routine maintenance frequency, subtract the monthly repair frequency from the monthly routine maintenance frequency to get the monthly maintenance difference; calculate the length of time between the start date and the current date to get the start date duration; divide the total number of maintenance times by the start date duration to get the daily average maintenance frequency. The maintenance interval, monthly maintenance frequency, and daily maintenance average are weighted to obtain the maintenance adjustment value; the maintenance personnel with the highest maintenance adjustment value are marked as repair personnel.

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