CN110376430B - Communication components and parts test evaluation system based on big data - Google Patents
Communication components and parts test evaluation system based on big data Download PDFInfo
- Publication number
- CN110376430B CN110376430B CN201910646485.4A CN201910646485A CN110376430B CN 110376430 B CN110376430 B CN 110376430B CN 201910646485 A CN201910646485 A CN 201910646485A CN 110376430 B CN110376430 B CN 110376430B
- Authority
- CN
- China
- Prior art keywords
- chip
- temperature
- module
- power consumption
- environment temperature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R21/00—Arrangements for measuring electric power or power factor
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R23/00—Arrangements for measuring frequencies; Arrangements for analysing frequency spectra
- G01R23/02—Arrangements for measuring frequency, e.g. pulse repetition rate; Arrangements for measuring period of current or voltage
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/28—Testing of electronic circuits, e.g. by signal tracer
- G01R31/2851—Testing of integrated circuits [IC]
- G01R31/2855—Environmental, reliability or burn-in testing
- G01R31/2872—Environmental, reliability or burn-in testing related to electrical or environmental aspects, e.g. temperature, humidity, vibration, nuclear radiation
- G01R31/2874—Environmental, reliability or burn-in testing related to electrical or environmental aspects, e.g. temperature, humidity, vibration, nuclear radiation related to temperature
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Environmental & Geological Engineering (AREA)
- Health & Medical Sciences (AREA)
- Toxicology (AREA)
- Computer Hardware Design (AREA)
- Microelectronics & Electronic Packaging (AREA)
- General Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Testing Of Individual Semiconductor Devices (AREA)
Abstract
The invention discloses a communication component testing and evaluating system based on big data, which comprises an information acquisition module, a classification module, an environment temperature adjusting module, a main frequency adjusting module, a testing module, a detection module, a data processing module and an evaluating module, wherein the information acquisition module is used for acquiring the information of a communication component; through the cooperation of the information acquisition module, the classification module and the environment temperature adjusting module, the data acquisition condition is set more in detail and reasonably. The test module is matched with the detection module, so that the test range is wider, and the subsequent analysis of the relation of the chip in each device to the environmental temperature, the dominant frequency and the power consumption is facilitated. The data processing module is matched with the evaluation module to determine the environment temperature and the frequency with the highest equipment operation efficiency, so that the selection of proper equipment according to specific use environments is facilitated, and the efficiency and the integral score of the equipment in each use environment can be conveniently known.
Description
Technical Field
The invention relates to the field of test evaluation systems, in particular to a communication component test evaluation system based on big data.
Background
The chip plays an important role as a component for data processing in the communication equipment, and in an ideal model, the mathematical formula of the energy consumption of the chip can be expressed as P ═ C × V2F, wherein C is a constant, V represents voltage, f is frequency, in an ideal case, the frequency and energy consumption are in a linear relationship, and in actual use, different devices adopt different chips or different devices adopting the same chip, because of the difference between heat dissipation conditions and ambient temperature, the temperature and energy consumption of the chip are different, and further the relationship between the energy consumption and the frequency is different from that in an ideal model.
For example, patent document CN105629052B discloses a real-time detection method for chip power consumption, which includes: setting up a test circuit, the test circuit comprising: the device comprises a fuse, a power supply circuit and an electronic load which are sequentially connected in series, a voltage measuring device connected to two ends of the fuse and used for measuring voltage values of two ends of the fuse, an analog-to-digital conversion device connected with the voltage measuring device, and a computing device connected with the analog-to-digital conversion device; setting the electronic load to different power consumption values, and measuring the input current value flowing through the fuse by using a test circuit; fitting a curve formula by utilizing the actual power consumption value and the input current value of the electronic load, wherein the curve formula takes the input current value as an independent variable and the power consumption value as a dependent variable; and based on a curve formula, calculating the actual power consumption value of the tested chip according to the input current value of the tested chip. However, the method only detects the power consumption value, and simultaneously, the utilized curve formula is single, so that the complex situation of the real use environment cannot be met, but the method does not further detect and evaluate different main frequencies, different environment temperatures and the energy consumption difference of chips among different devices.
Therefore, a system capable of testing the overall power consumption, the main frequency and the ambient temperature of the chip is needed so as to know the difference of the actual power consumption of each chip in different devices.
Disclosure of Invention
In order to solve the above technical problems, an object of the present invention is to provide a communication component testing and evaluating system based on big data, which enables the setting of data acquisition conditions to be more detailed and reasonable through the cooperation of an information acquisition module, a classification module, and an environmental temperature adjustment module. The test module is matched with the detection module, so that the test range is wider, and the subsequent analysis of the relation of the chip in each device to the environmental temperature, the dominant frequency and the power consumption is facilitated. The data processing module is matched with the evaluation module to determine the environment temperature and the frequency with the highest equipment operation efficiency, so that the selection of proper equipment according to specific use environments is facilitated, and the efficiency and the integral score of the equipment in each use environment can be conveniently known.
The technical problem to be solved by the invention is as follows:
(1) how to test the whole energy consumption, the main frequency and the environmental temperature of the chip so as to know the difference of the actual energy consumption of each chip in different devices.
The purpose of the invention can be realized by the following technical scheme:
a big data-based communication component test and evaluation system comprises an information acquisition module, a classification module, an environment temperature regulation module, a main frequency regulation module, a test module, a detection module, a data processing module and an evaluation module;
the information acquisition module is used for acquiring the type, the equipment model, the chip model, the working temperature range and the chip dominant frequency range of the communication equipment;
the classification module is used for dividing equipment with the same category, equipment model and chip model number into one group and marking the group as a primary similarity group; dividing equipment with the same category and chip type number into one group and marking the group as a secondary similarity group; dividing the primary similar groups with the same category into one group and marking the group as a tertiary similar group;
the environment temperature adjusting module is used for generating an environment temperature gradient table and is also used for keeping the environment temperature within a fixed range according to the environment temperature gradient table when the chip power consumption test is carried out;
the specific steps of the environment temperature adjusting module for generating the environment temperature gradient table are as follows:
reading the working temperature ranges of all the devices in the three-level similarity group, and screening out the maximum value tmaxAnd a minimum value tminThe operating temperature range T of the product is [ T ]min,tmax]Screening all temperature values which are multiples of 5 ℃ in T, sequencing the temperature values, and recording as an environmental temperature gradient table of the category as tem ═ T1, T2, T3, …, tj, … and tm };
the main frequency adjusting module is used for generating a main frequency gradiometer and is also used for keeping the chip in full-load operation on a fixed main frequency according to the main frequency gradiometer when the power consumption of the chip is tested;
the specific steps of the dominant frequency adjusting module for generating the dominant frequency gradiometer are as follows:
reading the dominant frequency range of the chips of all the devices in the second-level similarity group, and screening out the maximum value fmaxAnd minimum value fminThe dominant frequency range of the product is F [ < F >min,fmax]Then, all main frequency values in F which are multiples of 100Mhz are screened and sequenced, and the main frequency gradient table is marked as S ═ S1, S2, …,si，…，sn}；
the test module is used for carrying out chip power consumption test on each primary similarity group according to the environment temperature gradient table and the primary frequency gradient table;
the detection module is used for detecting and recording the working state of the equipment, the chip power consumption and the chip temperature when the chip power consumption test is carried out, wherein the working state is whether the equipment works normally or not;
the data processing module calculates the chip power consumption and the chip temperature acquired by the detection module to generate a fitting function;
the evaluation module is used for determining the optimal environment temperature interval and the optimal operation dominant frequency interval of each primary similarity group according to the fitting function and determining the sequencing of the chip power consumption of all devices of the same class at each fixed environment temperature and fixed dominant frequency.
Further, the specific steps of the test module for performing the chip power consumption test are as follows:
s1, sequentially selecting equipment in one primary similarity group as a test object, calling an environment temperature adjusting module to adjust the environment temperature to t1, and keeping the fluctuation range of the environment temperature within +/-0.5 ℃ during testing;
s2, calling a main frequency adjusting module to adjust the main frequency to S1, and calling a detection module to record the real-time chip power consumption p after the chip runs for 30min under full load1 1And chip temperature k1 1；
S3 temperature k of chip1 1When the frequency is reduced to t1, the main frequency is adjusted to s2, and after the chip runs for 30min under full load, the real-time chip power consumption p is recorded1 2And chip temperature k1 2；
S4, and so on, according to the sequence of the main frequency values in the main frequency gradient table from small to large, obtaining the chip power consumption set P when the environment temperature is t1 and the equipment runs at full load under all main frequencies1 i＝{p1 1，p1 2，…，p1 nAnd chip temperature set Ki 1＝{k1 1，k1 2，…，k1 n}；
S5, calling an environment temperature adjusting module to adjust the environment temperature to t2, when the chip temperature k1 is reduced to t2, executing S2-S4 again to obtain a chip power consumption set P when the equipment runs at full load under all main frequencies when the environment temperature is t22 i＝{p2 1，p2 2，…，p2 nAnd chip temperature set Ki 2＝{k2 1，k2 2，…，k2 n}；
S6, repeating S1-S5 according to the temperature value from small to large in the environment temperature gradient table when the temperature of the chip is reduced to be the same as the environment temperature, and obtaining the chip power consumption P of the equipment with any fixed temperature and fixed main frequencyj iAnd chip temperature Kj i；
S7, calculating the average value of the chip power consumption and the average value of the chip temperature of all the devices in the primary similarity group under the same environment temperature and main frequency, and taking the average values as the real chip power consumption w of the devices under the temperature and main frequencyj iAnd corresponding true chip temperature hj iAnd generating a power consumption temperature table of the equipment corresponding to the primary similarity group, wherein j represents that the tested environment temperature is the temperature value of the jth element in the environment temperature gradient table, and i represents that the tested main frequency is the main frequency value of the ith element in the main frequency gradient table.
Further, the specific steps of generating the fitting function by the data processing module are as follows:
SS1, sequentially acquiring power consumption of chips corresponding to all main frequencies at each environmental temperature, inputting the power consumption into a data processing module to generate main frequency related power consumption fitting functions B(s), and then performing derivation on all B(s) to obtain corresponding derivative functions b(s);
SS2, sequentially acquiring chip power consumptions corresponding to all environment temperatures under all the main frequencies, inputting the chip power consumptions into a data processing module to generate power consumption fitting functions G (t) related to the environment temperatures, and then performing derivation on all G (t) to obtain corresponding derivative functions g (t);
SS3, calculated in interval F ═ Fmin,fmax]In the main frequency gradient table, each point corresponds to a set bs of the values of b(s), the average value α of the set bs is calculated, and then the interval of b(s) < α is calculated;
SS4, calculated in interval T ═ Tmin,tmax]In the environment temperature gradient table, each point corresponds to a set gt of values of g (t), an average value β of the set gt is calculated, and then an interval of g (t) < β is calculated.
Further, the evaluation module takes the section b(s) < α as the optimal operation dominant frequency section corresponding to the first-level similar group, takes the section g (t) < β as the optimal environment temperature section corresponding to the first-level similar group, sorts the chip power consumption of each first-level similar group in the three-level similar groups under each fixed environment temperature and dominant frequency from small to large, if the power consumption is the same, compares the corresponding chip temperature, the chip temperature is lower in rank, the first-level similar groups with the first rank is obtained for adding, wherein the first name adds 3 points, the second name adds 2 points and the third name adds 1 point, calculates the total points of all environment temperatures and each first-level similar group of dominant frequency, and sorts the groups from high to low according to the total points.
The invention has the beneficial effects that:
(1) and (3) data acquisition: the information acquisition module is used for acquiring related information of the equipment, so that subsequent data processing and comparison are facilitated, the equipment is divided into the same equipment, different equipment with the same chip and the same type of equipment through the classification module, the environment temperature gradient table covering the working temperature range of the product is generated through the environment temperature adjusting module, and the environment temperature during testing is controlled according to the environment temperature gradient table; the dominant frequency gradiometer covering the dominant frequency range of the product is generated through the dominant frequency adjusting module, and the dominant frequency during testing is controlled according to the dominant frequency gradiometer, so that the condition setting of data acquisition is more detailed and reasonable compared with the prior art.
(2) In the aspect of test flow: the test module carries out the energy consumption test to all equipment, and real-time chip energy consumption and real-time chip temperature when the cooperation detection module gathered each equipment test compare in prior art, and the scope of test is wider, has covered different ambient temperature and dominant frequency, is favorable to follow-up analysis chip to the relation of ambient temperature, dominant frequency and consumption in each equipment.
(3) Data processing and mining aspects: the data processing module calculates, calculates and tabulates the acquired chip power consumption and chip temperature, and then the data processing module is matched with the evaluation module to determine an optimal operation dominant frequency interval and an optimal environment temperature interval, and the environment temperature and frequency with the highest equipment operation efficiency can be determined through the optimal operation dominant frequency interval and the optimal environment temperature interval, so that the data processing module is favorable for selecting proper equipment according to specific use environments, and simultaneously sequencing and scoring similar equipment, and the efficiency and the integral scoring of the equipment in each use environment can be conveniently known.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a system block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the embodiment provides a communication component test and evaluation system based on big data, which includes an information acquisition module, a classification module, an environmental temperature adjustment module, a main frequency adjustment module, a test module, a detection module, a data processing module, and an evaluation module;
the information acquisition module is used for acquiring the type, the equipment model, the chip model, the working temperature range and the chip dominant frequency range of the communication equipment; if the communication equipment is a mobile communication terminal, the equipment model is abc-001, the chip model is a73, the working temperature range is-10 ℃ to 60 ℃, and the dominant frequency range of the chip is 760Mhz-2.9 Ghz.
The classification module is used for dividing equipment with the same category, equipment model and chip model number into one group and marking the group as a primary similarity group; dividing equipment with the same category and chip type number into one group and marking the group as a secondary similarity group; dividing the primary similar groups with the same category into one group and marking the group as a tertiary similar group; the devices in the first-level similarity group are the same devices, the devices in the second-level similarity group are the same devices adopting the same chip, for example, the communication terminals of different brands all adopt the same chip, and the devices in the third-level similarity group are the same devices, for example, the communication terminals of different brands adopt different chips.
The environment temperature adjusting module is used for generating an environment temperature gradient table and is also used for keeping the environment temperature within a fixed range according to the environment temperature gradient table when the chip power consumption test is carried out; the environmental temperature gradient meter is used for determining a temperature collecting point, the temperature range of the equipment in use needs to be covered, the working temperature ranges of different equipment are not necessarily the same, and therefore the environmental temperature gradient meter needs to be manufactured by considering the working range of the whole product.
The specific steps of the environment temperature adjusting module for generating the environment temperature gradient table are as follows:
reading the working temperature ranges of all the devices in the three-level similarity group, and screening out the maximum value tmaxAnd a minimum value tminThe operating temperature range T of the product is [ T ]min,tmax]And screening and sorting all temperature values which are multiples of 5 ℃ in T, and recording as an environmental temperature gradient table of the category as tem ═ T1, T2, T3, …, tj, … and tm }. E.g. T [ -15 ℃,70 DEG C]And if so, the environment temperature gradient meter tem is { -15, -10, -5, …, 30, …,70 }.
The main frequency adjusting module is used for generating a main frequency gradiometer and is also used for keeping the chip in full-load operation on a fixed main frequency according to the main frequency gradiometer when the power consumption of the chip is tested; the temperature gradient table is the same as the environment temperature gradient table, and the lowest dominant frequency and the highest dominant frequency of each chip are different, so that the dominant frequency gradient table needs to cover the working frequency of the whole product class.
The specific steps of the dominant frequency adjusting module for generating the dominant frequency gradiometer are as follows:
reading the dominant frequency range of the chips of all the devices in the second-level similarity group, and screening out the maximum value fmaxAnd minimum value fminThe dominant frequency range of the product is F [ < F >min,fmax]Then, screening all main frequency values which are multiples of 100Mhz in the F, sequencing the main frequency values to serve as a main frequency gradient table, and recording the main frequency gradient table as S ═ S1, S2, …, si, … and sn }; e.g. F ═ 430Mhz,2Ghz]And the dominant frequency gradient table S is {500Mhz, 600Mhz, …, 1Ghz, …,2Ghz }.
The test module is used for carrying out chip power consumption test on each primary similarity group according to the environment temperature gradient table and the primary frequency gradient table; the specific steps of the test module for testing the chip power consumption are as follows:
s1, sequentially selecting equipment in one primary similarity group as a test object, calling an environment temperature adjusting module to adjust the environment temperature to t1, and keeping the fluctuation range of the environment temperature within +/-0.5 ℃ during testing;
s2, calling a main frequency adjusting module to adjust the main frequency to S1, and calling a detection module to record the real-time chip power consumption p after the chip runs for 30min under full load1 1And chip temperature k1 1(ii) a After the chip operates for a period of time, the temperature of the chip reaches equilibrium, so that data acquisition is performed after the chip operates for a period of time.
S3 temperature k of chip1 1When the frequency is reduced to t1, the main frequency is adjusted to s2, and after the chip runs for 30min under full load, the real-time chip power consumption p is recorded1 2And chip temperature k1 2；
S4, and so on, according to the sequence of the main frequency values in the main frequency gradient table from small to large, obtaining the chip power consumption set P when the environment temperature is t1 and the equipment runs at full load under all main frequencies1 i＝{p1 1，p1 2，…，p1 nAnd chip temperature set Ki 1＝{k1 1，k1 2，…，k1 n}; such asp1 1Denotes the chip power consumption, k, at an ambient temperature of t1 and with a fixed dominant frequency of s11 1The chip temperature is represented when the ambient temperature is t1 and the dominant frequency is fixed at s 1.
S5, calling an environment temperature adjusting module to adjust the environment temperature to t2, when the chip temperature k1 is reduced to t2, executing S2-S4 again to obtain a chip power consumption set P when the equipment runs at full load under all main frequencies when the environment temperature is t22 i＝{p2 1，p2 2，…，p2 nAnd chip temperature set Ki 2＝{k2 1，k2 2，…，k2 n}; after the test is finished when the ambient temperature is t1, the test is carried out when the ambient temperature is t2, but in order to ensure the accuracy, the test is carried out after the temperature of the chip is reduced,
s6, repeating S1-S5 according to the temperature value from small to large in the environment temperature gradient table when the temperature of the chip is reduced to be the same as the environment temperature, and obtaining the chip power consumption P of the equipment with any fixed temperature and fixed main frequencyj iAnd chip temperature Kj i(ii) a Wherein the superscript j corresponds to the jth element in the environment temperature gradient table, and the subscript i corresponds to the ith element in the main frequency gradient table.
S7, calculating the average value of chip power consumption and the average value of chip temperature of all devices in the primary similarity group under the same environmental temperature and main frequency, ensuring the validity of data through the average values of a plurality of same devices for preventing the excessive data deviation caused by the individual specificity, and taking the average values as the real chip power consumption w of the devices under the temperature and the main frequencyj iAnd corresponding true chip temperature hj iAnd generating a power consumption temperature table of the equipment corresponding to the primary similarity group, wherein in a form of table 1, a first row is a temperature acquisition point corresponding to the environment temperature gradient table, a first column corresponds to an acquisition point corresponding to the main frequency gradient table, wherein j represents the tested environment temperature as the temperature value of the jth element in the environment temperature gradient table, and i represents the tested main frequency as the main frequency value of the ith element in the main frequency gradient table. To this end is completedThe power consumption test of the equipment is carried out continuously until all the equipment completes the test, and power consumption differences among different chips or different equipment on the same chip are compared conveniently.
| t1 | t2 | … | tj | … | tm | |
| s1 | w1 1,h1 1 | w2 1,h2 1 | … | wj 1,hj 1 | … | wm 1,hm 1 |
| s2 | w1 2,h1 2 | w2 2,h2 2 | … | wj 2,hj 2 | … | wm 2,hm 2 |
| … | … | … | … | … | … | … |
| si | w1 i,h1 1 | w2 i,h2 i | … | wj i,hj i | … | wm i,hm i |
| … | … | … | … | … | … | … |
| sn | w1 n,h1 n | w2 n,h2 n | … | wj n,hj n | … | wm n,hm n |
TABLE 1 Power consumption thermometer
The detection module is used for detecting and recording the working state of the equipment, the chip power consumption and the chip temperature when the chip power consumption test is carried out, wherein the working state is whether the equipment works normally or not; and for the data with abnormal working state, the data processing module does not perform operation.
The data processing module calculates the chip power consumption and the chip temperature acquired by the detection module to generate a fitting function; during fitting, data fitting is carried out through matlab, a quadratic function, a cubic function, an exponential function, a logarithmic function and the like are selected in a fitting mode, various fitting curves are fitted firstly, and a function with the minimum average fitting deviation value is selected as a fitting function through calculating the average fitting deviation value.
The specific steps of the data processing module for generating the fitting function are as follows:
SS1, sequentially acquiring power consumption of chips corresponding to all main frequencies at each environmental temperature, inputting the power consumption into a data processing module to generate main frequency related power consumption fitting functions B(s), and then performing derivation on all B(s) to obtain corresponding derivative functions b(s);
SS2, sequentially acquiring chip power consumptions corresponding to all environment temperatures under all the main frequencies, inputting the chip power consumptions into a data processing module to generate power consumption fitting functions G (t) related to the environment temperatures, and then performing derivation on all G (t) to obtain corresponding derivative functions g (t); the derivative function can be used to describe the variation trend of the chip power consumption with the variation of the independent variable.
SS3, calculated in interval F ═ Fmin,fmax]In which each point in the main frequency gradient table corresponds to the value of b(s)And collecting the bs, calculating an average value α of the bs, and calculating a section of b(s) < α, wherein the larger the value of b(s), the faster the power consumption rises, the lower the energy efficiency ratio, so the energy efficiency ratio begins to decline after the value is higher than a certain value, and α is the average power consumption change rate, so α can be used as a reference value.
SS4, calculated in interval T ═ Tmin,tmax]In the environment temperature gradient table, the set gt of the values of g (t) corresponds to each point, the average value β of the set gt is calculated, and then the interval g (t) < β is calculated, and α is the same, the larger the value of g (t), the larger the influence of the rise of the environment temperature on the energy consumption is, after a certain value is exceeded, the environment temperature rises a little, the more the energy consumption rises, and the energy efficiency ratio is worse, so β can be used as the average influence coefficient of the environment temperature and can be used as a reference value.
The evaluation module is used for determining the optimal environment temperature interval and the optimal operation dominant frequency interval of each primary similarity group according to the fitting function and determining the sequencing of the chip power consumption of all devices of the same class at each fixed environment temperature and fixed dominant frequency.
The evaluation module takes the interval b(s) < α as an optimal operation dominant frequency interval corresponding to the first-level similar groups, takes the interval g (t) < β as an optimal environment temperature interval corresponding to the first-level similar groups, sorts the chip power consumption of each first-level similar group in the three-level similar groups under each fixed environment temperature and dominant frequency from small to large, compares the corresponding chip temperature if the power consumption is the same, has lower ranking, obtains the first-level similar groups with the first ranking three for adding, wherein the first name adds 3 points, the second name adds 2 points, and the third name adds 1 point, calculates the total points of all environment temperatures and the dominant frequency of each first-level similar group, and sorts the total points from high to low if the power consumption is the same, if two different devices a and b are sorted under the test environment of t1 and s1, a is the third name, b is the fifth name, a is marked with 1 point, but b is not marked with no point, if a is the first name, b is the third name, b is marked with the fifth name, if the second name is the fifth name, b is not marked with the first name, then the second name, a is marked with the fifth name, b, if the second name is not marked with the first name, the second name, the third name, the fourth name.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (4)
1. A big data-based communication component test and evaluation system is characterized by comprising an information acquisition module, a classification module, an environment temperature regulation module, a main frequency regulation module, a test module, a detection module, a data processing module and an evaluation module;
the information acquisition module is used for acquiring the type, the equipment model, the chip model, the working temperature range and the chip dominant frequency range of the communication equipment;
the classification module is used for dividing equipment with the same category, equipment model and chip model number into one group and marking the group as a primary similarity group; dividing equipment with the same category and chip type number into one group and marking the group as a secondary similarity group; dividing the primary similar groups with the same category into one group and marking the group as a tertiary similar group;
the environment temperature adjusting module is used for generating an environment temperature gradient table and is also used for keeping the environment temperature within a fixed range according to the environment temperature gradient table when the chip power consumption test is carried out;
the specific steps of the environment temperature adjusting module for generating the environment temperature gradient table are as follows:
reading the working temperature ranges of all the devices in the three-level similarity group, and screening out the maximum value tmaxAnd a minimum value tminThe operating temperature range T of the product is [ T ]min,tmax]Screening all temperature values which are multiples of 5 ℃ in T, sequencing the temperature values, and recording as an environmental temperature gradient table of the category as tem ═ T1, T2, T3, …, tj, … and tm };
the main frequency adjusting module is used for generating a main frequency gradiometer and is also used for keeping the chip in full-load operation on a fixed main frequency according to the main frequency gradiometer when the power consumption of the chip is tested;
the specific steps of the dominant frequency adjusting module for generating the dominant frequency gradiometer are as follows:
reading the dominant frequency range of the chips of all the devices in the second-level similarity group, and screening out the maximum value fmaxAnd minimum value fminThe dominant frequency range of the product is F [ < F >min,fmax]Then, screening all main frequency values which are multiples of 100Mhz in the F, sequencing the main frequency values to serve as a main frequency gradient table, and recording the main frequency gradient table as S ═ S1, S2, …, si, … and sn };
the test module is used for carrying out chip power consumption test on each primary similarity group according to the environment temperature gradient table and the primary frequency gradient table;
the detection module is used for detecting and recording the working state of the equipment, the chip power consumption and the chip temperature when the chip power consumption test is carried out, wherein the working state is whether the equipment works normally or not;
the data processing module calculates the chip power consumption and the chip temperature acquired by the detection module to generate a fitting function;
the evaluation module is used for determining the optimal environment temperature interval and the optimal operation dominant frequency interval of each primary similarity group according to the fitting function and determining the sequencing of the chip power consumption of all devices of the same class at each fixed environment temperature and fixed dominant frequency.
2. The big data-based communication component test and evaluation system as claimed in claim 1, wherein the specific steps of the test module for performing chip power consumption test are as follows:
s1, sequentially selecting equipment in one primary similarity group as a test object, calling an environment temperature adjusting module to adjust the environment temperature to t1, and keeping the fluctuation range of the environment temperature within +/-0.5 ℃ during testing;
s2, calling a main frequency adjusting module to adjust the main frequency to S1, and calling a detection module to record the real-time chip power consumption p after the chip runs for 30min under full load1 1And chip temperature k1 1；
S3 temperature k of chip1 1When the frequency is reduced to t1, the main frequency is adjusted to s2, and after the chip runs for 30min under full load, the real-time chip power consumption p is recorded1 2And chip temperature k1 2；
S4, and so on, according to the sequence of the main frequency values in the main frequency gradient table from small to large, obtaining the chip power consumption set P when the environment temperature is t1 and the equipment runs at full load under all main frequencies1 i＝{p1 1，p1 2，…，p1 nAnd chip temperature set Ki 1＝{k1 1，k1 2，…，k1 n}；
S5, calling an environment temperature adjusting module to adjust the environment temperature to t2, when the chip temperature k1 is reduced to t2, executing S2-S4 again to obtain a chip power consumption set P when the equipment runs at full load under all main frequencies when the environment temperature is t22 i＝{p2 1，p2 2，…，p2 nAnd chip temperature set Ki 2＝{k2 1，k2 2，…，k2 n}；
S6, repeating S1-S5 according to the temperature value from small to large in the environment temperature gradient table when the temperature of the chip is reduced to be the same as the environment temperature, and obtaining the chip power consumption P of the equipment with any fixed temperature and fixed main frequencyj iAnd chip temperature Kj i；
S7, calculating the average value of chip power consumption and the average value of chip temperature of all devices in the primary similarity group under the same environment temperature and the same main frequency, taking the average values as the real chip power consumption wji and the corresponding real chip temperature hji of the corresponding devices under the temperature and the main frequency, and generating a power consumption temperature table of the devices corresponding to the primary similarity group, wherein j represents that the tested environment temperature is the temperature value of the jth element in the environment temperature gradient table, and i represents that the tested main frequency is the main frequency value of the ith element in the main frequency gradient table.
3. The big data-based communication component testing and evaluating system according to claim 2, wherein the data processing module generates the fitting function by the specific steps of:
SS1, sequentially acquiring power consumption of chips corresponding to all main frequencies at each environmental temperature, inputting the power consumption into a data processing module to generate main frequency related power consumption fitting functions B(s), and then performing derivation on all B(s) to obtain corresponding derivative functions b(s);
SS2, sequentially acquiring chip power consumptions corresponding to all environment temperatures under all the main frequencies, inputting the chip power consumptions into a data processing module to generate power consumption fitting functions G (t) related to the environment temperatures, and then performing derivation on all G (t) to obtain corresponding derivative functions g (t);
SS3, calculated in interval F ═ Fmin,fmax]In the main frequency gradient table, each point corresponds to a set bs of the values of b(s), the average value α of the set bs is calculated, and then the interval of b(s) < α is calculated;
SS4, calculated in interval T ═ Tmin,tmax]In the environment temperature gradient table, each point corresponds to a set gt of values of g (t), an average value β of the set gt is calculated, and then an interval of g (t) < β is calculated.
4. The communication component testing and evaluating system based on big data as claimed in claim 3, wherein the evaluating module takes the section b(s) < α as the optimal operation dominant frequency section corresponding to the first-level similarity group, takes the section g (t) < β as the optimal environment temperature section corresponding to the first-level similarity group, sorts the chip power consumption of all the devices of each first-level similarity group in the third-level similarity group at each fixed environment temperature and dominant frequency from small to large, compares the corresponding chip temperature if the power consumption is the same, has higher ranking with low chip temperature, obtains the first-level similarity group of the first three for adding, wherein the first name adds 3 points, the second name adds 2 points, and the third name adds 1 point, calculates the total points of all the environment temperatures and each first-level similarity group of the dominant frequency, and sorts the total points from high to low.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910646485.4A CN110376430B (en) | 2019-07-17 | 2019-07-17 | Communication components and parts test evaluation system based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910646485.4A CN110376430B (en) | 2019-07-17 | 2019-07-17 | Communication components and parts test evaluation system based on big data |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110376430A CN110376430A (en) | 2019-10-25 |
| CN110376430B true CN110376430B (en) | 2020-05-22 |
Family
ID=68253686
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910646485.4A Active CN110376430B (en) | 2019-07-17 | 2019-07-17 | Communication components and parts test evaluation system based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110376430B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110986190B (en) * | 2019-11-12 | 2021-01-22 | 珠海格力电器股份有限公司 | Method, device, system and medium for monitoring and analyzing performance of main board of outdoor unit of air conditioner |
| CN112433091A (en) * | 2020-12-04 | 2021-03-02 | 武汉轻工大学 | Real-time detection system for power consumption of chip |
| CN112833955A (en) * | 2021-01-08 | 2021-05-25 | 三一汽车起重机械有限公司 | Method for establishing hydraulic oil quality monitoring model, monitoring method, device and system |
| CN112798892B (en) * | 2021-01-14 | 2022-07-15 | 淮阴工学院 | Vehicle-mounted multimedia automatic test system based on Internet of things |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102788951A (en) * | 2012-09-05 | 2012-11-21 | 无锡江南计算技术研究所 | Automatic test equipment (ATE) test result judging method and ATE test method |
| WO2013097213A1 (en) * | 2011-12-31 | 2013-07-04 | 北京大学深圳研究生院 | High-temperature burn-in test method and device for contactless wl/wlp chip |
| CN103645402A (en) * | 2013-12-14 | 2014-03-19 | 佛山市中格威电子有限公司 | Test method and test device capable of identifying different models of remote controllers |
| CN103809102A (en) * | 2012-11-06 | 2014-05-21 | 比亚迪股份有限公司 | Method for testing chip being programmed and testing system |
| CN104198918A (en) * | 2014-09-12 | 2014-12-10 | 张保宁 | Testing system for small-lot production of high-speed and -precision ADC (analog to digital converter) chips |
| CN104660466A (en) * | 2015-02-06 | 2015-05-27 | 深圳先进技术研究院 | Security testing method and system |
| CN104749514A (en) * | 2015-04-10 | 2015-07-01 | 中国电子科技集团公司第三十八研究所 | Direct connection type testing device for low-power-consumption differential transmission chip |
| CN105182265A (en) * | 2015-09-29 | 2015-12-23 | 中国电力科学研究院 | Metering chip model selection testing system |
| CN105676105A (en) * | 2014-11-19 | 2016-06-15 | 比亚迪股份有限公司 | Chip test method and chip test machine |
| CN106199179A (en) * | 2016-06-22 | 2016-12-07 | 何颖 | Frequency testing device and method |
| CN108620344A (en) * | 2017-03-21 | 2018-10-09 | 深圳市华宇半导体有限公司 | The automatic sensitive chip automatization test system of smart mobile phone and its control method |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10002810B2 (en) * | 2015-06-25 | 2018-06-19 | International Business Machines Corporation | On-chip combined hot carrier injection and bias temperature instability monitor |
| CN105629052B (en) * | 2015-12-23 | 2018-04-10 | 无锡江南计算技术研究所 | Chip power-consumption real-time detection method |
| CN106680697A (en) * | 2016-12-08 | 2017-05-17 | 西安电子科技大学 | Test detector of digital signal processor |
| CN109446586B (en) * | 2018-10-03 | 2022-12-20 | 复旦大学 | High-efficient general chip test system |
-
2019
- 2019-07-17 CN CN201910646485.4A patent/CN110376430B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013097213A1 (en) * | 2011-12-31 | 2013-07-04 | 北京大学深圳研究生院 | High-temperature burn-in test method and device for contactless wl/wlp chip |
| CN102788951A (en) * | 2012-09-05 | 2012-11-21 | 无锡江南计算技术研究所 | Automatic test equipment (ATE) test result judging method and ATE test method |
| CN103809102A (en) * | 2012-11-06 | 2014-05-21 | 比亚迪股份有限公司 | Method for testing chip being programmed and testing system |
| CN103645402A (en) * | 2013-12-14 | 2014-03-19 | 佛山市中格威电子有限公司 | Test method and test device capable of identifying different models of remote controllers |
| CN104198918A (en) * | 2014-09-12 | 2014-12-10 | 张保宁 | Testing system for small-lot production of high-speed and -precision ADC (analog to digital converter) chips |
| CN105676105A (en) * | 2014-11-19 | 2016-06-15 | 比亚迪股份有限公司 | Chip test method and chip test machine |
| CN104660466A (en) * | 2015-02-06 | 2015-05-27 | 深圳先进技术研究院 | Security testing method and system |
| CN104749514A (en) * | 2015-04-10 | 2015-07-01 | 中国电子科技集团公司第三十八研究所 | Direct connection type testing device for low-power-consumption differential transmission chip |
| CN105182265A (en) * | 2015-09-29 | 2015-12-23 | 中国电力科学研究院 | Metering chip model selection testing system |
| CN106199179A (en) * | 2016-06-22 | 2016-12-07 | 何颖 | Frequency testing device and method |
| CN108620344A (en) * | 2017-03-21 | 2018-10-09 | 深圳市华宇半导体有限公司 | The automatic sensitive chip automatization test system of smart mobile phone and its control method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110376430A (en) | 2019-10-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110376430B (en) | Communication components and parts test evaluation system based on big data | |
| CN112926144B (en) | Multi-stress accelerated life test coupling effect analysis and life prediction method | |
| Xu et al. | Artificial-intelligence-based methodology for load disaggregation at bulk supply point | |
| CN110070282B (en) | Low-voltage transformer area line loss influence factor analysis method based on comprehensive relevance | |
| CN111327377B (en) | Method, device, equipment and storage medium for field intensity prediction | |
| CN102955902A (en) | Method and system for evaluating reliability of radar simulation equipment | |
| CN111507611A (en) | Method and system for determining electricity stealing suspected user | |
| Charytoniuk et al. | Neural-network-based demand forecasting in a deregulated environment | |
| CN107423754B (en) | Automatic radiation source identification system based on parameter multi-attribute autonomous intelligent decision | |
| CN107730097B (en) | Bus load prediction method and device and computing equipment | |
| CN107067100A (en) | Wind power anomalous data identification method and device for identifying | |
| CN116258355B (en) | Distribution area load curve decomposition method and device suitable for multipoint power estimation | |
| Kong et al. | A remote estimation method of smart meter errors based on neural network filter and generalized damping recursive least square | |
| CN116049705A (en) | Clustering analysis-based power system user load characteristic clustering method and system | |
| CN111126499A (en) | Secondary clustering-based power consumption behavior pattern classification method | |
| CN112580844A (en) | Meteorological data processing method, device, equipment and computer readable storage medium | |
| CN114266457A (en) | Method for detecting different loss inducement of distribution line | |
| CN113505943B (en) | Method, system, equipment and medium for predicting short-term load of power grid | |
| GAZELOGLU et al. | Comparison of weighted least squares and robust estimation in structural equation modeling of ordinal categorical data with larger sample sizes | |
| CN104461878A (en) | Software quality evaluation method based on user-defined models | |
| KR102266494B1 (en) | System and Method of Deep Learning-based Customer Classification for Demand Respond | |
| CN115130764A (en) | Power distribution network situation prediction method and system based on state evaluation | |
| da Silva Pessoa et al. | Sensibility analysis of a fault location method based on ANN, WPT and decision tree in distribution systems | |
| CN114676749A (en) | Power distribution network operation data abnormity judgment method based on data mining | |
| Kloubert | Fast point estimate method for correlated multimodally distributed input variables |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |